2025-07-17T08:33:16.2480629Z Current runner version: '2.326.0' 2025-07-17T08:33:16.2485478Z Runner name: 'linux.rocm.gpu.mi300.2-8zrv9-runner-r2mdd' 2025-07-17T08:33:16.2486224Z Runner group name: 'default' 2025-07-17T08:33:16.2486909Z Machine name: 'linux' 2025-07-17T08:33:16.2489045Z ##[group]GITHUB_TOKEN Permissions 2025-07-17T08:33:16.2490709Z Contents: read 2025-07-17T08:33:16.2491141Z Metadata: read 2025-07-17T08:33:16.2491570Z ##[endgroup] 2025-07-17T08:33:16.2493295Z Secret source: Actions 2025-07-17T08:33:16.2493982Z Prepare workflow directory 2025-07-17T08:33:16.2876464Z Prepare all required actions 2025-07-17T08:33:16.2905842Z Getting action download info 2025-07-17T08:33:16.6244167Z Download action repository 'pytorch/pytorch@main' (SHA:39ac189808c61588f3594dbc2fc1d69bb6194c47) 2025-07-17T08:33:25.5136647Z Download action repository 'aws-actions/configure-aws-credentials@ececac1a45f3b08a01d2dd070d28d111c5fe6722' (SHA:ececac1a45f3b08a01d2dd070d28d111c5fe6722) 2025-07-17T08:33:26.3866639Z Download action repository 'aws-actions/amazon-ecr-login@062b18b96a7aff071d4dc91bc00c4c1a7945b076' (SHA:062b18b96a7aff071d4dc91bc00c4c1a7945b076) 2025-07-17T08:33:26.7711277Z Download action repository 'pytorch/test-infra@main' (SHA:a9ec424ad5e5851e47d68139cfd953b4031778d5) 2025-07-17T08:33:27.7962982Z Download action repository 'actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02' (SHA:ea165f8d65b6e75b540449e92b4886f43607fa02) 2025-07-17T08:33:28.3463186Z Getting action download info 2025-07-17T08:33:28.5131607Z Download action repository 'actions/checkout@v4' (SHA:11bd71901bbe5b1630ceea73d27597364c9af683) 2025-07-17T08:33:28.9874647Z Getting action download info 2025-07-17T08:33:29.1881713Z Download action repository 'nick-fields/retry@v3.0.0' (SHA:7152eba30c6575329ac0576536151aca5a72780e) 2025-07-17T08:33:29.5756163Z Getting action download info 2025-07-17T08:33:29.7801081Z Uses: pytorch/pytorch/.github/workflows/_rocm-test.yml@refs/heads/main (a38f433be2e94a64b095a44ba39879d02d0c2316) 2025-07-17T08:33:29.7804012Z ##[group] Inputs 2025-07-17T08:33:29.7804288Z build-environment: linux-noble-rocm-py3.12-mi300 2025-07-17T08:33:29.7805620Z test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 6, "runner": "linux.rocm.gpu.mi300.2", "unstable": "unstable"}, {"config": "default", "shard": 2, "num_shards": 6, "runner": "linux.rocm.gpu.mi300.2", "unstable": "unstable"}, {"config": "default", "shard": 3, "num_shards": 6, "runner": "linux.rocm.gpu.mi300.2", "unstable": "unstable"}, {"config": "default", "shard": 4, "num_shards": 6, "runner": "linux.rocm.gpu.mi300.2", "unstable": "unstable"}, {"config": "default", "shard": 5, "num_shards": 6, "runner": "linux.rocm.gpu.mi300.2", "unstable": "unstable"}, {"config": "default", "shard": 6, "num_shards": 6, "runner": "linux.rocm.gpu.mi300.2", "unstable": "unstable"}]} 2025-07-17T08:33:29.7807162Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T08:33:29.7807635Z sync-tag: 2025-07-17T08:33:29.7808240Z timeout-minutes: 300 2025-07-17T08:33:29.7808414Z tests-to-include: 2025-07-17T08:33:29.7808563Z dashboard-tag: 2025-07-17T08:33:29.7808937Z disable-monitor: true 2025-07-17T08:33:29.7809111Z monitor-log-interval: 5 2025-07-17T08:33:29.7809310Z monitor-data-collect-interval: 1 2025-07-17T08:33:29.7809508Z ##[endgroup] 2025-07-17T08:33:29.7809808Z Complete job name: linux-noble-rocm-py3.12-mi300 / test (default, 1, 6, linux.rocm.gpu.mi300.2, unstable) 2025-07-17T08:33:29.8255648Z ##[group]Run pytorch/pytorch/.github/actions/checkout-pytorch@main 2025-07-17T08:33:29.8256148Z with: 2025-07-17T08:33:29.8256297Z no-sudo: true 2025-07-17T08:33:29.8256459Z submodules: recursive 2025-07-17T08:33:29.8256623Z fetch-depth: 0 2025-07-17T08:33:29.8257005Z env: 2025-07-17T08:33:29.8257164Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:33:29.8257378Z ##[endgroup] 2025-07-17T08:33:29.8324560Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-07-17T08:33:29.8325481Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-07-17T08:33:29.8341352Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T08:33:29.8341613Z env: 2025-07-17T08:33:29.8341761Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:33:29.8341934Z ##[endgroup] 2025-07-17T08:33:29.8523614Z ##[group]Run # Use all available CPUs for fetching 2025-07-17T08:33:29.8523969Z # Use all available CPUs for fetching 2025-07-17T08:33:29.8524193Z cd "${GITHUB_WORKSPACE}" 2025-07-17T08:33:29.8524421Z git config --global fetch.parallel 0 2025-07-17T08:33:29.8524663Z git config --global submodule.fetchJobs 0 2025-07-17T08:33:29.8524883Z  2025-07-17T08:33:29.8525110Z # Clean workspace. The default checkout action should also do this, but 2025-07-17T08:33:29.8525399Z # do it here as well just in case 2025-07-17T08:33:29.8525626Z if [[ -d .git ]]; then 2025-07-17T08:33:29.8525820Z  if [ -z "${NO_SUDO}" ]; then 2025-07-17T08:33:29.8526031Z  sudo git clean -ffdx 2025-07-17T08:33:29.8526218Z  else 2025-07-17T08:33:29.8526379Z  git clean -ffdx 2025-07-17T08:33:29.8526546Z  fi 2025-07-17T08:33:29.8526690Z fi 2025-07-17T08:33:29.8539452Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T08:33:29.8539921Z env: 2025-07-17T08:33:29.8540232Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:33:29.8540570Z NO_SUDO: true 2025-07-17T08:33:29.8540797Z ##[endgroup] 2025-07-17T08:33:29.8695318Z ##[group]Run actions/checkout@v4 2025-07-17T08:33:29.8695546Z with: 2025-07-17T08:33:29.8695714Z ref: a38f433be2e94a64b095a44ba39879d02d0c2316 2025-07-17T08:33:29.8695929Z fetch-depth: 0 2025-07-17T08:33:29.8696091Z submodules: recursive 2025-07-17T08:33:29.8696253Z show-progress: false 2025-07-17T08:33:29.8696448Z repository: pytorch/pytorch 2025-07-17T08:33:29.8696724Z token: *** 2025-07-17T08:33:29.8696874Z ssh-strict: true 2025-07-17T08:33:29.8697031Z ssh-user: git 2025-07-17T08:33:29.8697186Z persist-credentials: true 2025-07-17T08:33:29.8697364Z clean: true 2025-07-17T08:33:29.8697534Z sparse-checkout-cone-mode: true 2025-07-17T08:33:29.8697803Z fetch-tags: false 2025-07-17T08:33:29.8697955Z lfs: false 2025-07-17T08:33:29.8698098Z set-safe-directory: true 2025-07-17T08:33:29.8698290Z env: 2025-07-17T08:33:29.8698424Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:33:29.8698585Z ##[endgroup] 2025-07-17T08:33:29.9816753Z Syncing repository: pytorch/pytorch 2025-07-17T08:33:29.9817730Z ##[group]Getting Git version info 2025-07-17T08:33:29.9818022Z Working directory is '/home/runner/_work/pytorch/pytorch' 2025-07-17T08:33:29.9818439Z [command]/usr/bin/git version 2025-07-17T08:33:29.9827626Z git version 2.50.1 2025-07-17T08:33:29.9848505Z ##[endgroup] 2025-07-17T08:33:29.9856929Z Copying '/home/runner/.gitconfig' to '/home/runner/_work/_temp/ea7e4787-c2fc-4f5b-9062-9235dbba75f4/.gitconfig' 2025-07-17T08:33:29.9866563Z Temporarily overriding HOME='/home/runner/_work/_temp/ea7e4787-c2fc-4f5b-9062-9235dbba75f4' before making global git config changes 2025-07-17T08:33:29.9867087Z Adding repository directory to the temporary git global config as a safe directory 2025-07-17T08:33:29.9878805Z [command]/usr/bin/git config --global --add safe.directory /home/runner/_work/pytorch/pytorch 2025-07-17T08:33:29.9910684Z Deleting the contents of '/home/runner/_work/pytorch/pytorch' 2025-07-17T08:33:29.9913848Z ##[group]Initializing the repository 2025-07-17T08:33:29.9916897Z [command]/usr/bin/git init /home/runner/_work/pytorch/pytorch 2025-07-17T08:33:29.9977661Z hint: Using 'master' as the name for the initial branch. This default branch name 2025-07-17T08:33:29.9978079Z hint: is subject to change. To configure the initial branch name to use in all 2025-07-17T08:33:29.9978418Z hint: of your new repositories, which will suppress this warning, call: 2025-07-17T08:33:29.9979175Z hint: 2025-07-17T08:33:29.9979381Z hint: git config --global init.defaultBranch 2025-07-17T08:33:29.9979601Z hint: 2025-07-17T08:33:29.9979811Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and 2025-07-17T08:33:29.9980144Z hint: 'development'. The just-created branch can be renamed via this command: 2025-07-17T08:33:29.9980411Z hint: 2025-07-17T08:33:29.9980555Z hint: git branch -m 2025-07-17T08:33:29.9980737Z hint: 2025-07-17T08:33:29.9980955Z hint: Disable this message with "git config set advice.defaultBranchName false" 2025-07-17T08:33:29.9981329Z Initialized empty Git repository in /home/runner/_work/pytorch/pytorch/.git/ 2025-07-17T08:33:29.9988244Z [command]/usr/bin/git remote add origin https://github.com/pytorch/pytorch 2025-07-17T08:33:30.0026416Z ##[endgroup] 2025-07-17T08:33:30.0026715Z ##[group]Disabling automatic garbage collection 2025-07-17T08:33:30.0029588Z [command]/usr/bin/git config --local gc.auto 0 2025-07-17T08:33:30.0056220Z ##[endgroup] 2025-07-17T08:33:30.0056458Z ##[group]Setting up auth 2025-07-17T08:33:30.0061895Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2025-07-17T08:33:30.0094942Z [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' || :" 2025-07-17T08:33:30.0378664Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2025-07-17T08:33:30.0407493Z [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' || :" 2025-07-17T08:33:30.0683606Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-07-17T08:33:30.0723512Z ##[endgroup] 2025-07-17T08:33:30.0723841Z ##[group]Fetching the repository 2025-07-17T08:33:30.0737072Z [command]/usr/bin/git -c protocol.version=2 fetch --prune --no-recurse-submodules origin +refs/heads/*:refs/remotes/origin/* +refs/tags/*:refs/tags/* 2025-07-17T08:34:13.8689494Z From https://github.com/pytorch/pytorch 2025-07-17T08:34:13.8690229Z * [new branch] 2.6.0.dev20241004+ -> origin/2.6.0.dev20241004+ 2025-07-17T08:34:13.8693944Z * [new branch] 20250616_dtype_docs -> origin/20250616_dtype_docs 2025-07-17T08:34:13.8696470Z * [new branch] HDCharles-2.6.0-release-notes -> origin/HDCharles-2.6.0-release-notes 2025-07-17T08:34:13.8696971Z * [new branch] JackCaoG/dynamo_make_fx_non_core_aten_ops -> origin/JackCaoG/dynamo_make_fx_non_core_aten_ops 2025-07-17T08:34:13.8698519Z * [new branch] PR-AOTInductorNoneBug -> origin/PR-AOTInductorNoneBug 2025-07-17T08:34:13.8701867Z * [new branch] PR-AOTInductorNoneBugFix -> origin/PR-AOTInductorNoneBugFix 2025-07-17T08:34:13.8705074Z * [new branch] PR-FixConfigsIssue -> origin/PR-FixConfigsIssue 2025-07-17T08:34:13.8708325Z * [new branch] PR-NoneBugFix-viable -> origin/PR-NoneBugFix-viable 2025-07-17T08:34:13.8711574Z * [new branch] PR-ResetToZero -> origin/PR-ResetToZero 2025-07-17T08:34:13.8714946Z * [new branch] Update-Flash-Packaging -> origin/Update-Flash-Packaging 2025-07-17T08:34:13.8719011Z * [new branch] ZainRizvi-patch-1 -> origin/ZainRizvi-patch-1 2025-07-17T08:34:13.8721503Z * [new branch] add-missing-args-normalization -> origin/add-missing-args-normalization 2025-07-17T08:34:13.8724739Z * [new branch] addUtilForLinuxBuild -> origin/addUtilForLinuxBuild 2025-07-17T08:34:13.8728049Z * [new branch] add_windows_testing_back -> origin/add_windows_testing_back 2025-07-17T08:34:13.8731600Z * [new branch] addmm-heuristic -> origin/addmm-heuristic 2025-07-17T08:34:13.8735103Z * [new branch] addsimde -> origin/addsimde 2025-07-17T08:34:13.8739456Z * [new branch] adi/test -> origin/adi/test 2025-07-17T08:34:13.8742738Z * [new branch] adi/test_fusions -> origin/adi/test_fusions 2025-07-17T08:34:13.8746300Z * [new branch] adi/test_presve_change -> origin/adi/test_presve_change 2025-07-17T08:34:13.8749427Z * [new branch] adi/testpresve_change -> origin/adi/testpresve_change 2025-07-17T08:34:13.8752819Z * [new branch] adi/update_openblas -> origin/adi/update_openblas 2025-07-17T08:34:13.8758506Z * [new branch] aditew01/test/vec_bf16 -> origin/aditew01/test/vec_bf16 2025-07-17T08:34:13.8761717Z * [new branch] ah-globalfeedback-hook -> origin/ah-globalfeedback-hook 2025-07-17T08:34:13.8765026Z * [new branch] albanD-patch-1 -> origin/albanD-patch-1 2025-07-17T08:34:13.8768517Z * [new branch] alt-disable -> origin/alt-disable 2025-07-17T08:34:13.8772677Z * [new branch] angelayi/155426 -> origin/angelayi/155426 2025-07-17T08:34:13.8776004Z * [new branch] angelayi/157183 -> origin/angelayi/157183 2025-07-17T08:34:13.8779714Z * [new branch] angelayi/aoti_additional_files -> origin/angelayi/aoti_additional_files 2025-07-17T08:34:13.8783071Z * [new branch] angelayi/aoti_custom_op -> origin/angelayi/aoti_custom_op 2025-07-17T08:34:13.8810382Z * [new branch] angelayi/benchmark -> origin/angelayi/benchmark 2025-07-17T08:34:13.8810819Z * [new branch] angelayi/benchmark2 -> origin/angelayi/benchmark2 2025-07-17T08:34:13.8811235Z * [new branch] angelayi/change_pytree_serialization -> origin/angelayi/change_pytree_serialization 2025-07-17T08:34:13.8811656Z * [new branch] angelayi/cpp_loader -> origin/angelayi/cpp_loader 2025-07-17T08:34:13.8811986Z * [new branch] angelayi/customop -> origin/angelayi/customop 2025-07-17T08:34:13.8812325Z * [new branch] angelayi/del_lib -> origin/angelayi/del_lib 2025-07-17T08:34:13.8812642Z * [new branch] angelayi/docs -> origin/angelayi/docs 2025-07-17T08:34:13.8812951Z * [new branch] angelayi/docs2 -> origin/angelayi/docs2 2025-07-17T08:34:13.8813309Z * [new branch] angelayi/dynamo_fake_input -> origin/angelayi/dynamo_fake_input 2025-07-17T08:34:13.8815527Z * [new branch] angelayi/errmsg -> origin/angelayi/errmsg 2025-07-17T08:34:13.8818279Z * [new branch] angelayi/fake_device -> origin/angelayi/fake_device 2025-07-17T08:34:13.8821493Z * [new branch] angelayi/int_lift_constants -> origin/angelayi/int_lift_constants 2025-07-17T08:34:13.8824829Z * [new branch] angelayi/logging.bak -> origin/angelayi/logging.bak 2025-07-17T08:34:13.8827878Z * [new branch] angelayi/logging2 -> origin/angelayi/logging2 2025-07-17T08:34:13.8831284Z * [new branch] angelayi/no_so_weight -> origin/angelayi/no_so_weight 2025-07-17T08:34:13.8834447Z * [new branch] angelayi/pytree -> origin/angelayi/pytree 2025-07-17T08:34:13.8837963Z * [new branch] angelayi/scan_layers -> origin/angelayi/scan_layers 2025-07-17T08:34:13.8840845Z * [new branch] angelayi/symint_input -> origin/angelayi/symint_input 2025-07-17T08:34:13.8844097Z * [new branch] angelayi/torch_size -> origin/angelayi/torch_size 2025-07-17T08:34:13.8847313Z * [new branch] angelayi/torchgenooops -> origin/angelayi/torchgenooops 2025-07-17T08:34:13.8850813Z * [new branch] angelayi/update_schema_msg -> origin/angelayi/update_schema_msg 2025-07-17T08:34:13.8854056Z * [new branch] aoti_static_linkage -> origin/aoti_static_linkage 2025-07-17T08:34:13.8857399Z * [new branch] atalman-inductor-perf-cu124 -> origin/atalman-inductor-perf-cu124 2025-07-17T08:34:13.8860552Z * [new branch] atalman-inductor-perf-cu124.1 -> origin/atalman-inductor-perf-cu124.1 2025-07-17T08:34:13.8863901Z * [new branch] atalman-patch-1 -> origin/atalman-patch-1 2025-07-17T08:34:13.8867128Z * [new branch] atalman-patch-2 -> origin/atalman-patch-2 2025-07-17T08:34:13.8870509Z * [new branch] atalman-patch-3 -> origin/atalman-patch-3 2025-07-17T08:34:13.8873588Z * [new branch] atalman-patch-4 -> origin/atalman-patch-4 2025-07-17T08:34:13.8876911Z * [new branch] atalman-patch-5 -> origin/atalman-patch-5 2025-07-17T08:34:13.8880320Z * [new branch] atalman-patch-6 -> origin/atalman-patch-6 2025-07-17T08:34:13.8883658Z * [new branch] atalman-patch-7 -> origin/atalman-patch-7 2025-07-17T08:34:13.8886914Z * [new branch] atalman-patch-8 -> origin/atalman-patch-8 2025-07-17T08:34:13.8890209Z * [new branch] atalman_inductor_2.3.0 -> origin/atalman_inductor_2.3.0 2025-07-17T08:34:13.8893528Z * [new branch] 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[new branch] zxiiro/windows -> origin/zxiiro/windows 2025-07-17T08:34:15.7526554Z * [new tag] bc2caa7fdf006894eff7af936babde69ab5a40f8-huydhn-debug -> bc2caa7fdf006894eff7af936babde69ab5a40f8-huydhn-debug 2025-07-17T08:34:15.7529450Z * [new tag] ci/binaries/77164 -> ci/binaries/77164 2025-07-17T08:34:15.7532433Z * [new tag] ciflow/android/149601 -> ciflow/android/149601 2025-07-17T08:34:15.7534919Z * [new tag] ciflow/binaries/143959 -> ciflow/binaries/143959 2025-07-17T08:34:15.7536879Z * [new tag] ciflow/binaries/156049 -> ciflow/binaries/156049 2025-07-17T08:34:15.7538854Z * [new tag] ciflow/binaries/157432 -> ciflow/binaries/157432 2025-07-17T08:34:15.7540792Z * [new tag] ciflow/binaries/157685 -> ciflow/binaries/157685 2025-07-17T08:34:15.7542761Z * [new tag] ciflow/binaries/157689 -> ciflow/binaries/157689 2025-07-17T08:34:15.7544737Z * [new tag] ciflow/binaries/158104 -> ciflow/binaries/158104 2025-07-17T08:34:15.7548761Z * [new tag] ciflow/binaries/158151 -> ciflow/binaries/158151 2025-07-17T08:34:15.7551210Z * [new tag] ciflow/binaries_libtorch/143959 -> ciflow/binaries_libtorch/143959 2025-07-17T08:34:15.7553160Z * [new tag] ciflow/binaries_libtorch/156049 -> ciflow/binaries_libtorch/156049 2025-07-17T08:34:15.7555104Z * [new tag] ciflow/binaries_libtorch/157432 -> ciflow/binaries_libtorch/157432 2025-07-17T08:34:15.7557203Z * [new tag] ciflow/binaries_libtorch/157791 -> ciflow/binaries_libtorch/157791 2025-07-17T08:34:15.7559276Z * [new tag] ciflow/binaries_libtorch/157928 -> ciflow/binaries_libtorch/157928 2025-07-17T08:34:15.7561749Z * [new tag] ciflow/binaries_wheel/143959 -> ciflow/binaries_wheel/143959 2025-07-17T08:34:15.7563720Z * [new tag] ciflow/binaries_wheel/156049 -> ciflow/binaries_wheel/156049 2025-07-17T08:34:15.7565656Z * [new tag] ciflow/binaries_wheel/157432 -> ciflow/binaries_wheel/157432 2025-07-17T08:34:15.7567577Z * [new tag] ciflow/binaries_wheel/157791 -> ciflow/binaries_wheel/157791 2025-07-17T08:34:15.7569540Z * [new tag] ciflow/binaries_wheel/157928 -> ciflow/binaries_wheel/157928 2025-07-17T08:34:15.7571625Z * [new tag] ciflow/binaries_wheel/158408 -> ciflow/binaries_wheel/158408 2025-07-17T08:34:15.7574129Z * [new tag] ciflow/h100-cutlass-backend/156626 -> ciflow/h100-cutlass-backend/156626 2025-07-17T08:34:15.7576465Z * [new tag] ciflow/h100-distributed/144552 -> ciflow/h100-distributed/144552 2025-07-17T08:34:15.7578535Z * [new tag] ciflow/h100-distributed/150312 -> ciflow/h100-distributed/150312 2025-07-17T08:34:15.7580510Z * [new tag] ciflow/h100-distributed/156605 -> ciflow/h100-distributed/156605 2025-07-17T08:34:15.7582644Z * [new tag] ciflow/h100-distributed/156703 -> ciflow/h100-distributed/156703 2025-07-17T08:34:15.7585245Z * [new tag] ciflow/h100-symm-mem/151845 -> ciflow/h100-symm-mem/151845 2025-07-17T08:34:15.7587344Z * [new tag] ciflow/h100-symm-mem/157180 -> ciflow/h100-symm-mem/157180 2025-07-17T08:34:15.7589369Z * [new tag] ciflow/h100-symm-mem/157970 -> ciflow/h100-symm-mem/157970 2025-07-17T08:34:15.7591710Z * [new tag] ciflow/h100-symm-mem/158511 -> ciflow/h100-symm-mem/158511 2025-07-17T08:34:15.7593484Z * [new tag] ciflow/h100-symm-mem/158512 -> ciflow/h100-symm-mem/158512 2025-07-17T08:34:15.7595475Z * [new tag] ciflow/h100-symm-mem/158513 -> ciflow/h100-symm-mem/158513 2025-07-17T08:34:15.7597528Z * [new tag] ciflow/h100-symm-mem/158514 -> ciflow/h100-symm-mem/158514 2025-07-17T08:34:15.7599446Z * [new tag] ciflow/h100-symm-mem/158515 -> ciflow/h100-symm-mem/158515 2025-07-17T08:34:15.7601557Z * [new tag] ciflow/h100-symm-mem/158523 -> ciflow/h100-symm-mem/158523 2025-07-17T08:34:15.7604013Z * [new tag] ciflow/h100/156980 -> ciflow/h100/156980 2025-07-17T08:34:15.7605999Z * [new tag] ciflow/h100/158459 -> ciflow/h100/158459 2025-07-17T08:34:15.7608610Z * [new tag] ciflow/inductor-perf-test-nightly-rocm/151845 -> ciflow/inductor-perf-test-nightly-rocm/151845 2025-07-17T08:34:15.7610716Z * [new tag] ciflow/inductor-perf-test-nightly-rocm/156592 -> ciflow/inductor-perf-test-nightly-rocm/156592 2025-07-17T08:34:15.7613139Z * [new tag] ciflow/inductor-perf-test-nightly/156592 -> ciflow/inductor-perf-test-nightly/156592 2025-07-17T08:34:15.7615492Z * [new tag] ciflow/inductor-rocm/151845 -> ciflow/inductor-rocm/151845 2025-07-17T08:34:15.7617752Z * [new tag] ciflow/inductor-rocm/156192 -> ciflow/inductor-rocm/156192 2025-07-17T08:34:15.7619848Z * [new tag] ciflow/inductor-rocm/157191 -> ciflow/inductor-rocm/157191 2025-07-17T08:34:15.7621810Z * [new tag] ciflow/inductor-rocm/157520 -> ciflow/inductor-rocm/157520 2025-07-17T08:34:15.7623788Z * [new tag] ciflow/inductor-rocm/158074 -> ciflow/inductor-rocm/158074 2025-07-17T08:34:15.7626017Z * [new tag] ciflow/inductor-rocm/158102 -> ciflow/inductor-rocm/158102 2025-07-17T08:34:15.7628232Z * [new tag] ciflow/inductor-rocm/158103 -> ciflow/inductor-rocm/158103 2025-07-17T08:34:15.7630201Z * [new tag] ciflow/inductor-rocm/158459 -> ciflow/inductor-rocm/158459 2025-07-17T08:34:15.7632658Z * [new tag] ciflow/inductor/137400 -> ciflow/inductor/137400 2025-07-17T08:34:15.7634706Z * [new tag] ciflow/inductor/138214 -> ciflow/inductor/138214 2025-07-17T08:34:15.7636724Z * [new tag] ciflow/inductor/139561 -> ciflow/inductor/139561 2025-07-17T08:34:15.7638650Z * [new tag] ciflow/inductor/143712 -> ciflow/inductor/143712 2025-07-17T08:34:15.7640618Z * [new tag] ciflow/inductor/144516 -> ciflow/inductor/144516 2025-07-17T08:34:15.7642586Z * [new tag] ciflow/inductor/144556 -> ciflow/inductor/144556 2025-07-17T08:34:15.7644600Z * [new tag] ciflow/inductor/147470 -> ciflow/inductor/147470 2025-07-17T08:34:15.7646566Z * [new tag] ciflow/inductor/148180 -> ciflow/inductor/148180 2025-07-17T08:34:15.7648594Z * [new tag] ciflow/inductor/148328 -> ciflow/inductor/148328 2025-07-17T08:34:15.7650579Z * [new tag] ciflow/inductor/148484 -> ciflow/inductor/148484 2025-07-17T08:34:15.7652583Z * [new tag] ciflow/inductor/148492 -> ciflow/inductor/148492 2025-07-17T08:34:15.7654576Z * [new tag] ciflow/inductor/148569 -> ciflow/inductor/148569 2025-07-17T08:34:15.7656581Z * [new tag] ciflow/inductor/149003 -> ciflow/inductor/149003 2025-07-17T08:34:15.7658612Z * [new tag] ciflow/inductor/149961 -> ciflow/inductor/149961 2025-07-17T08:34:15.7660646Z * [new tag] ciflow/inductor/150762 -> ciflow/inductor/150762 2025-07-17T08:34:15.7662861Z * [new tag] ciflow/inductor/151777 -> ciflow/inductor/151777 2025-07-17T08:34:15.7665109Z * [new tag] ciflow/inductor/151845 -> ciflow/inductor/151845 2025-07-17T08:34:15.7667017Z * [new tag] ciflow/inductor/152624 -> ciflow/inductor/152624 2025-07-17T08:34:15.7668989Z * [new tag] ciflow/inductor/154149 -> ciflow/inductor/154149 2025-07-17T08:34:15.7670960Z * [new tag] ciflow/inductor/154199 -> ciflow/inductor/154199 2025-07-17T08:34:15.7673001Z * [new tag] ciflow/inductor/154551 -> ciflow/inductor/154551 2025-07-17T08:34:15.7675022Z * [new tag] ciflow/inductor/154694 -> ciflow/inductor/154694 2025-07-17T08:34:15.7677036Z * [new tag] ciflow/inductor/155452 -> ciflow/inductor/155452 2025-07-17T08:34:15.7679015Z * [new tag] ciflow/inductor/155608 -> ciflow/inductor/155608 2025-07-17T08:34:15.7680990Z * [new tag] ciflow/inductor/155877 -> ciflow/inductor/155877 2025-07-17T08:34:15.7683042Z * [new tag] ciflow/inductor/155958 -> ciflow/inductor/155958 2025-07-17T08:34:15.7684988Z * [new tag] ciflow/inductor/156049 -> ciflow/inductor/156049 2025-07-17T08:34:15.7686973Z * [new tag] ciflow/inductor/156118 -> ciflow/inductor/156118 2025-07-17T08:34:15.7688931Z * [new tag] ciflow/inductor/156141 -> ciflow/inductor/156141 2025-07-17T08:34:15.7690888Z * [new tag] ciflow/inductor/156175 -> ciflow/inductor/156175 2025-07-17T08:34:15.7692905Z * [new tag] ciflow/inductor/156192 -> ciflow/inductor/156192 2025-07-17T08:34:15.7694994Z * [new tag] ciflow/inductor/156296 -> ciflow/inductor/156296 2025-07-17T08:34:15.7697030Z * [new tag] ciflow/inductor/156369 -> ciflow/inductor/156369 2025-07-17T08:34:15.7699197Z * [new tag] ciflow/inductor/156370 -> ciflow/inductor/156370 2025-07-17T08:34:15.7701252Z * [new tag] ciflow/inductor/156371 -> ciflow/inductor/156371 2025-07-17T08:34:15.7703227Z * [new tag] ciflow/inductor/156416 -> ciflow/inductor/156416 2025-07-17T08:34:15.7705224Z * [new tag] ciflow/inductor/156460 -> ciflow/inductor/156460 2025-07-17T08:34:15.7707473Z * [new tag] ciflow/inductor/156592 -> ciflow/inductor/156592 2025-07-17T08:34:15.7709486Z * [new tag] ciflow/inductor/156605 -> ciflow/inductor/156605 2025-07-17T08:34:15.7711471Z * [new tag] ciflow/inductor/156626 -> ciflow/inductor/156626 2025-07-17T08:34:15.7713420Z * [new tag] ciflow/inductor/156652 -> ciflow/inductor/156652 2025-07-17T08:34:15.7715408Z * [new tag] ciflow/inductor/156781 -> ciflow/inductor/156781 2025-07-17T08:34:15.7717441Z * [new tag] ciflow/inductor/156851 -> ciflow/inductor/156851 2025-07-17T08:34:15.7719722Z * [new tag] ciflow/inductor/156874 -> ciflow/inductor/156874 2025-07-17T08:34:15.7721779Z * [new tag] ciflow/inductor/156977 -> ciflow/inductor/156977 2025-07-17T08:34:15.7723685Z * [new tag] ciflow/inductor/156980 -> ciflow/inductor/156980 2025-07-17T08:34:15.7725731Z * [new tag] ciflow/inductor/157152 -> ciflow/inductor/157152 2025-07-17T08:34:15.7727686Z * [new tag] ciflow/inductor/157298 -> ciflow/inductor/157298 2025-07-17T08:34:15.7729678Z * [new tag] ciflow/inductor/157520 -> ciflow/inductor/157520 2025-07-17T08:34:15.7731657Z * [new tag] ciflow/inductor/157572 -> ciflow/inductor/157572 2025-07-17T08:34:15.7733656Z * [new tag] ciflow/inductor/157580 -> ciflow/inductor/157580 2025-07-17T08:34:15.7735613Z * [new tag] ciflow/inductor/157594 -> ciflow/inductor/157594 2025-07-17T08:34:15.7737971Z * [new tag] ciflow/inductor/157633 -> ciflow/inductor/157633 2025-07-17T08:34:15.7739792Z * [new tag] ciflow/inductor/157635 -> ciflow/inductor/157635 2025-07-17T08:34:15.7741874Z * [new tag] ciflow/inductor/157685 -> ciflow/inductor/157685 2025-07-17T08:34:15.7743985Z * [new tag] ciflow/inductor/157686 -> ciflow/inductor/157686 2025-07-17T08:34:15.7746036Z * [new tag] ciflow/inductor/157689 -> ciflow/inductor/157689 2025-07-17T08:34:15.7748108Z * [new tag] ciflow/inductor/157699 -> ciflow/inductor/157699 2025-07-17T08:34:15.7750168Z * [new tag] ciflow/inductor/157743 -> ciflow/inductor/157743 2025-07-17T08:34:15.7752332Z * [new tag] ciflow/inductor/157748 -> ciflow/inductor/157748 2025-07-17T08:34:15.7754296Z * [new tag] ciflow/inductor/157804 -> ciflow/inductor/157804 2025-07-17T08:34:15.7756281Z * [new tag] ciflow/inductor/157822 -> ciflow/inductor/157822 2025-07-17T08:34:15.7758428Z * [new tag] ciflow/inductor/157854 -> ciflow/inductor/157854 2025-07-17T08:34:15.7760623Z * [new tag] ciflow/inductor/157887 -> ciflow/inductor/157887 2025-07-17T08:34:15.7762590Z * [new tag] ciflow/inductor/157902 -> ciflow/inductor/157902 2025-07-17T08:34:15.7764701Z * [new tag] ciflow/inductor/157927 -> ciflow/inductor/157927 2025-07-17T08:34:15.7766692Z * [new tag] ciflow/inductor/157944 -> ciflow/inductor/157944 2025-07-17T08:34:15.7768795Z * [new tag] ciflow/inductor/157951 -> ciflow/inductor/157951 2025-07-17T08:34:15.7771052Z * [new tag] ciflow/inductor/157954 -> ciflow/inductor/157954 2025-07-17T08:34:15.7773177Z * [new tag] ciflow/inductor/157967 -> ciflow/inductor/157967 2025-07-17T08:34:15.7775218Z * [new tag] ciflow/inductor/157969 -> ciflow/inductor/157969 2025-07-17T08:34:15.7777318Z * [new tag] ciflow/inductor/157971 -> ciflow/inductor/157971 2025-07-17T08:34:15.7779324Z * [new tag] ciflow/inductor/157979 -> ciflow/inductor/157979 2025-07-17T08:34:15.7781495Z * [new tag] ciflow/inductor/157982 -> ciflow/inductor/157982 2025-07-17T08:34:15.7783513Z * [new tag] ciflow/inductor/157993 -> ciflow/inductor/157993 2025-07-17T08:34:15.7785686Z * [new tag] ciflow/inductor/158004 -> ciflow/inductor/158004 2025-07-17T08:34:15.7791197Z * [new tag] ciflow/inductor/158011 -> ciflow/inductor/158011 2025-07-17T08:34:15.7793204Z * [new tag] ciflow/inductor/158015 -> ciflow/inductor/158015 2025-07-17T08:34:15.7795270Z * [new tag] ciflow/inductor/158046 -> ciflow/inductor/158046 2025-07-17T08:34:15.7797433Z * [new tag] ciflow/inductor/158047 -> ciflow/inductor/158047 2025-07-17T08:34:15.7799447Z * [new tag] ciflow/inductor/158048 -> ciflow/inductor/158048 2025-07-17T08:34:15.7801410Z * [new tag] ciflow/inductor/158049 -> ciflow/inductor/158049 2025-07-17T08:34:15.7803412Z * [new tag] ciflow/inductor/158061 -> ciflow/inductor/158061 2025-07-17T08:34:15.7805387Z * [new tag] ciflow/inductor/158062 -> ciflow/inductor/158062 2025-07-17T08:34:15.7807324Z * [new tag] ciflow/inductor/158072 -> ciflow/inductor/158072 2025-07-17T08:34:15.7809335Z * [new tag] ciflow/inductor/158074 -> ciflow/inductor/158074 2025-07-17T08:34:15.7811354Z * [new tag] ciflow/inductor/158075 -> ciflow/inductor/158075 2025-07-17T08:34:15.7813348Z * [new tag] ciflow/inductor/158084 -> ciflow/inductor/158084 2025-07-17T08:34:15.7815413Z * [new tag] ciflow/inductor/158091 -> ciflow/inductor/158091 2025-07-17T08:34:15.7817554Z * [new tag] ciflow/inductor/158097 -> ciflow/inductor/158097 2025-07-17T08:34:15.7819350Z * [new tag] ciflow/inductor/158098 -> ciflow/inductor/158098 2025-07-17T08:34:15.7821317Z * [new tag] ciflow/inductor/158104 -> ciflow/inductor/158104 2025-07-17T08:34:15.7823303Z * [new tag] ciflow/inductor/158119 -> ciflow/inductor/158119 2025-07-17T08:34:15.7825372Z * [new tag] ciflow/inductor/158156 -> ciflow/inductor/158156 2025-07-17T08:34:15.7827479Z * [new tag] ciflow/inductor/158171 -> ciflow/inductor/158171 2025-07-17T08:34:15.7829880Z * [new tag] ciflow/inductor/158174 -> ciflow/inductor/158174 2025-07-17T08:34:15.7831867Z * [new tag] ciflow/inductor/158188 -> ciflow/inductor/158188 2025-07-17T08:34:15.7833893Z * [new tag] ciflow/inductor/158193 -> ciflow/inductor/158193 2025-07-17T08:34:15.7835892Z * [new tag] ciflow/inductor/158211 -> ciflow/inductor/158211 2025-07-17T08:34:15.7837993Z * [new tag] ciflow/inductor/158223 -> ciflow/inductor/158223 2025-07-17T08:34:15.7839959Z * [new tag] ciflow/inductor/158237 -> ciflow/inductor/158237 2025-07-17T08:34:15.7841927Z * [new tag] ciflow/inductor/158259 -> ciflow/inductor/158259 2025-07-17T08:34:15.7844083Z * [new tag] ciflow/inductor/158284 -> ciflow/inductor/158284 2025-07-17T08:34:15.7846207Z * [new tag] ciflow/inductor/158289 -> ciflow/inductor/158289 2025-07-17T08:34:15.7848196Z * [new tag] ciflow/inductor/158311 -> ciflow/inductor/158311 2025-07-17T08:34:15.7850297Z * [new tag] ciflow/inductor/158312 -> ciflow/inductor/158312 2025-07-17T08:34:15.7852399Z * [new tag] ciflow/inductor/158318 -> ciflow/inductor/158318 2025-07-17T08:34:15.7854364Z * [new tag] ciflow/inductor/158321 -> ciflow/inductor/158321 2025-07-17T08:34:15.7856459Z * [new tag] ciflow/inductor/158338 -> ciflow/inductor/158338 2025-07-17T08:34:15.7858456Z * [new tag] ciflow/inductor/158349 -> ciflow/inductor/158349 2025-07-17T08:34:15.7860478Z * [new tag] ciflow/inductor/158350 -> ciflow/inductor/158350 2025-07-17T08:34:15.7862474Z * [new tag] ciflow/inductor/158351 -> ciflow/inductor/158351 2025-07-17T08:34:15.7864448Z * [new tag] ciflow/inductor/158360 -> ciflow/inductor/158360 2025-07-17T08:34:15.7866522Z * [new tag] ciflow/inductor/158361 -> ciflow/inductor/158361 2025-07-17T08:34:15.7868559Z * [new tag] ciflow/inductor/158363 -> ciflow/inductor/158363 2025-07-17T08:34:15.7870507Z * [new tag] ciflow/inductor/158368 -> ciflow/inductor/158368 2025-07-17T08:34:15.7872504Z * [new tag] ciflow/inductor/158377 -> ciflow/inductor/158377 2025-07-17T08:34:15.7874533Z * [new tag] ciflow/inductor/158379 -> ciflow/inductor/158379 2025-07-17T08:34:15.7876568Z * [new tag] ciflow/inductor/158381 -> ciflow/inductor/158381 2025-07-17T08:34:15.7878533Z * [new tag] ciflow/inductor/158391 -> ciflow/inductor/158391 2025-07-17T08:34:15.7880689Z * [new tag] ciflow/inductor/158394 -> ciflow/inductor/158394 2025-07-17T08:34:15.7882631Z * [new tag] ciflow/inductor/158397 -> ciflow/inductor/158397 2025-07-17T08:34:15.7884816Z * [new tag] ciflow/inductor/158400 -> ciflow/inductor/158400 2025-07-17T08:34:15.7886851Z * [new tag] ciflow/inductor/158404 -> ciflow/inductor/158404 2025-07-17T08:34:15.7888868Z * [new tag] ciflow/inductor/158406 -> ciflow/inductor/158406 2025-07-17T08:34:15.7891070Z * [new tag] ciflow/inductor/158410 -> ciflow/inductor/158410 2025-07-17T08:34:15.7892978Z * [new tag] ciflow/inductor/158418 -> ciflow/inductor/158418 2025-07-17T08:34:15.7895055Z * [new tag] ciflow/inductor/158424 -> ciflow/inductor/158424 2025-07-17T08:34:15.7897059Z * [new tag] ciflow/inductor/158426 -> ciflow/inductor/158426 2025-07-17T08:34:15.7899039Z * [new tag] ciflow/inductor/158427 -> ciflow/inductor/158427 2025-07-17T08:34:15.7901303Z * [new tag] ciflow/inductor/158430 -> ciflow/inductor/158430 2025-07-17T08:34:15.7903569Z * [new tag] ciflow/inductor/158435 -> ciflow/inductor/158435 2025-07-17T08:34:15.7905693Z * [new tag] ciflow/inductor/158442 -> ciflow/inductor/158442 2025-07-17T08:34:15.7907787Z * [new tag] ciflow/inductor/158449 -> ciflow/inductor/158449 2025-07-17T08:34:15.7909830Z * [new tag] ciflow/inductor/158456 -> ciflow/inductor/158456 2025-07-17T08:34:15.7911858Z * [new tag] ciflow/inductor/158458 -> ciflow/inductor/158458 2025-07-17T08:34:15.7913806Z * [new tag] ciflow/inductor/158459 -> ciflow/inductor/158459 2025-07-17T08:34:15.7915805Z * [new tag] ciflow/inductor/158460 -> ciflow/inductor/158460 2025-07-17T08:34:15.7917779Z * [new tag] ciflow/inductor/158462 -> ciflow/inductor/158462 2025-07-17T08:34:15.7919796Z * [new tag] ciflow/inductor/158467 -> ciflow/inductor/158467 2025-07-17T08:34:15.7921771Z * [new tag] ciflow/inductor/158468 -> ciflow/inductor/158468 2025-07-17T08:34:15.7923758Z * [new tag] ciflow/inductor/158476 -> ciflow/inductor/158476 2025-07-17T08:34:15.7926071Z * [new tag] ciflow/inductor/158479 -> ciflow/inductor/158479 2025-07-17T08:34:15.7928002Z * [new tag] ciflow/inductor/158480 -> ciflow/inductor/158480 2025-07-17T08:34:15.7930024Z * [new tag] ciflow/inductor/158485 -> ciflow/inductor/158485 2025-07-17T08:34:15.7931988Z * [new tag] ciflow/inductor/158490 -> ciflow/inductor/158490 2025-07-17T08:34:15.7933966Z * [new tag] ciflow/inductor/158492 -> ciflow/inductor/158492 2025-07-17T08:34:15.7936195Z * [new tag] ciflow/inductor/158494 -> ciflow/inductor/158494 2025-07-17T08:34:15.7938138Z * [new tag] ciflow/inductor/158495 -> ciflow/inductor/158495 2025-07-17T08:34:15.7940142Z * [new tag] ciflow/inductor/158498 -> ciflow/inductor/158498 2025-07-17T08:34:15.7942182Z * [new tag] ciflow/inductor/158499 -> ciflow/inductor/158499 2025-07-17T08:34:15.7944158Z * [new tag] ciflow/inductor/158500 -> ciflow/inductor/158500 2025-07-17T08:34:15.7946260Z * [new tag] ciflow/inductor/158501 -> ciflow/inductor/158501 2025-07-17T08:34:15.7948266Z * [new tag] ciflow/inductor/158504 -> ciflow/inductor/158504 2025-07-17T08:34:15.7950250Z * [new tag] ciflow/inductor/158509 -> ciflow/inductor/158509 2025-07-17T08:34:15.7952207Z * [new tag] ciflow/inductor/158520 -> ciflow/inductor/158520 2025-07-17T08:34:15.7954150Z * [new tag] ciflow/inductor/158524 -> ciflow/inductor/158524 2025-07-17T08:34:15.7956339Z * [new tag] ciflow/inductor/158525 -> ciflow/inductor/158525 2025-07-17T08:34:15.7958424Z * [new tag] ciflow/inductor/158526 -> ciflow/inductor/158526 2025-07-17T08:34:15.7960414Z * [new tag] ciflow/inductor/158528 -> ciflow/inductor/158528 2025-07-17T08:34:15.7962423Z * [new tag] ciflow/inductor/158534 -> ciflow/inductor/158534 2025-07-17T08:34:15.7964560Z * [new tag] ciflow/inductor/158535 -> ciflow/inductor/158535 2025-07-17T08:34:15.7966731Z * [new tag] ciflow/inductor/158537 -> ciflow/inductor/158537 2025-07-17T08:34:15.7968654Z * [new tag] ciflow/inductor/158538 -> ciflow/inductor/158538 2025-07-17T08:34:15.7970733Z * [new tag] ciflow/inductor/158543 -> ciflow/inductor/158543 2025-07-17T08:34:15.7973006Z * [new tag] ciflow/inductor/3b9a386 -> ciflow/inductor/3b9a386 2025-07-17T08:34:15.7975202Z * [new tag] ciflow/inductor/3d4b92b -> ciflow/inductor/3d4b92b 2025-07-17T08:34:15.7977361Z * [new tag] ciflow/inductor/d224ac7 -> ciflow/inductor/d224ac7 2025-07-17T08:34:15.7979906Z * [new tag] ciflow/linux-aarch64/157520 -> ciflow/linux-aarch64/157520 2025-07-17T08:34:15.7981854Z * [new tag] ciflow/linux-aarch64/157782 -> ciflow/linux-aarch64/157782 2025-07-17T08:34:15.7983915Z * [new tag] ciflow/linux-aarch64/157994 -> ciflow/linux-aarch64/157994 2025-07-17T08:34:15.7986156Z * [new tag] ciflow/linux-aarch64/158445 -> ciflow/linux-aarch64/158445 2025-07-17T08:34:15.7988670Z * [new tag] ciflow/mps/155200 -> ciflow/mps/155200 2025-07-17T08:34:15.7990638Z * [new tag] ciflow/mps/157553 -> ciflow/mps/157553 2025-07-17T08:34:15.7992585Z * [new tag] ciflow/mps/157875 -> ciflow/mps/157875 2025-07-17T08:34:15.7994553Z * [new tag] ciflow/mps/157876 -> ciflow/mps/157876 2025-07-17T08:34:15.7996520Z * [new tag] ciflow/mps/158237 -> ciflow/mps/158237 2025-07-17T08:34:15.7998505Z * [new tag] ciflow/mps/158349 -> ciflow/mps/158349 2025-07-17T08:34:15.8000509Z * [new tag] ciflow/mps/158350 -> ciflow/mps/158350 2025-07-17T08:34:15.8002493Z * [new tag] ciflow/mps/158351 -> ciflow/mps/158351 2025-07-17T08:34:15.8005017Z * [new tag] ciflow/periodic-rocm-mi300/156192 -> ciflow/periodic-rocm-mi300/156192 2025-07-17T08:34:15.8006999Z * [new tag] ciflow/periodic-rocm-mi300/157191 -> ciflow/periodic-rocm-mi300/157191 2025-07-17T08:34:15.8010482Z * [new tag] ciflow/periodic-rocm-mi300/158102 -> ciflow/periodic-rocm-mi300/158102 2025-07-17T08:34:15.8011648Z * [new tag] ciflow/periodic-rocm-mi300/158103 -> ciflow/periodic-rocm-mi300/158103 2025-07-17T08:34:15.8014575Z * [new tag] ciflow/periodic/054a2fd -> ciflow/periodic/054a2fd 2025-07-17T08:34:15.8015676Z * [new tag] ciflow/periodic/143959 -> ciflow/periodic/143959 2025-07-17T08:34:15.8017784Z * [new tag] ciflow/periodic/156559 -> ciflow/periodic/156559 2025-07-17T08:34:15.8019918Z * [new tag] ciflow/periodic/156900 -> ciflow/periodic/156900 2025-07-17T08:34:15.8021854Z * [new tag] ciflow/periodic/157748 -> ciflow/periodic/157748 2025-07-17T08:34:15.8023971Z * [new tag] ciflow/periodic/157939 -> ciflow/periodic/157939 2025-07-17T08:34:15.8026140Z * [new tag] ciflow/periodic/158145 -> ciflow/periodic/158145 2025-07-17T08:34:15.8028490Z * [new tag] ciflow/periodic/2a6d37d -> ciflow/periodic/2a6d37d 2025-07-17T08:34:15.8030613Z * [new tag] ciflow/periodic/317eeb8 -> ciflow/periodic/317eeb8 2025-07-17T08:34:15.8032709Z * [new tag] ciflow/periodic/3c32 -> ciflow/periodic/3c32 2025-07-17T08:34:15.8034912Z * [new tag] ciflow/periodic/3e98831 -> ciflow/periodic/3e98831 2025-07-17T08:34:15.8037254Z * [new tag] ciflow/periodic/94512-point -> ciflow/periodic/94512-point 2025-07-17T08:34:15.8040031Z * [new tag] ciflow/periodic/csl/test87519 -> ciflow/periodic/csl/test87519 2025-07-17T08:34:15.8042193Z * [new tag] ciflow/periodic/csltest88275 -> ciflow/periodic/csltest88275 2025-07-17T08:34:15.8044492Z * [new tag] ciflow/periodic/csltest88761 -> ciflow/periodic/csltest88761 2025-07-17T08:34:15.8046589Z * [new tag] ciflow/periodic/release_1.12 -> ciflow/periodic/release_1.12 2025-07-17T08:34:15.8048831Z * [new tag] ciflow/periodic/release_1.12.0 -> ciflow/periodic/release_1.12.0 2025-07-17T08:34:15.8051310Z * [new tag] ciflow/periodic/sha-ec5b83 -> ciflow/periodic/sha-ec5b83 2025-07-17T08:34:15.8053668Z * [new tag] ciflow/rocm-mi300/156192 -> ciflow/rocm-mi300/156192 2025-07-17T08:34:15.8055631Z * [new tag] ciflow/rocm-mi300/157191 -> ciflow/rocm-mi300/157191 2025-07-17T08:34:15.8057710Z * [new tag] ciflow/rocm-mi300/157520 -> ciflow/rocm-mi300/157520 2025-07-17T08:34:15.8059770Z * [new tag] ciflow/rocm-mi300/158102 -> ciflow/rocm-mi300/158102 2025-07-17T08:34:15.8061751Z * [new tag] ciflow/rocm-mi300/158103 -> ciflow/rocm-mi300/158103 2025-07-17T08:34:15.8063826Z * [new tag] ciflow/rocm-mi300/158221 -> ciflow/rocm-mi300/158221 2025-07-17T08:34:15.8065995Z * [new tag] ciflow/rocm-mi300/158459 -> ciflow/rocm-mi300/158459 2025-07-17T08:34:15.8068578Z * [new tag] ciflow/rocm/148492 -> ciflow/rocm/148492 2025-07-17T08:34:15.8070507Z * [new tag] ciflow/rocm/149601 -> ciflow/rocm/149601 2025-07-17T08:34:15.8072516Z * [new tag] ciflow/rocm/150312 -> ciflow/rocm/150312 2025-07-17T08:34:15.8074531Z * [new tag] ciflow/rocm/151845 -> ciflow/rocm/151845 2025-07-17T08:34:15.8076529Z * [new tag] ciflow/rocm/155200 -> ciflow/rocm/155200 2025-07-17T08:34:15.8078579Z * [new tag] ciflow/rocm/155877 -> ciflow/rocm/155877 2025-07-17T08:34:15.8080583Z * [new tag] ciflow/rocm/156165 -> ciflow/rocm/156165 2025-07-17T08:34:15.8082591Z * [new tag] ciflow/rocm/156192 -> ciflow/rocm/156192 2025-07-17T08:34:15.8084619Z * [new tag] ciflow/rocm/156592 -> ciflow/rocm/156592 2025-07-17T08:34:15.8086595Z * [new tag] ciflow/rocm/157520 -> ciflow/rocm/157520 2025-07-17T08:34:15.8088700Z * [new tag] ciflow/rocm/157964 -> ciflow/rocm/157964 2025-07-17T08:34:15.8090743Z * [new tag] ciflow/rocm/158074 -> ciflow/rocm/158074 2025-07-17T08:34:15.8092769Z * [new tag] ciflow/rocm/158219 -> ciflow/rocm/158219 2025-07-17T08:34:15.8094726Z * [new tag] ciflow/rocm/158220 -> ciflow/rocm/158220 2025-07-17T08:34:15.8096751Z * [new tag] ciflow/rocm/158224 -> ciflow/rocm/158224 2025-07-17T08:34:15.8098819Z * [new tag] ciflow/rocm/158271 -> ciflow/rocm/158271 2025-07-17T08:34:15.8100739Z * [new tag] ciflow/rocm/158408 -> ciflow/rocm/158408 2025-07-17T08:34:15.8102716Z * [new tag] ciflow/rocm/158459 -> ciflow/rocm/158459 2025-07-17T08:34:15.8105128Z * [new tag] ciflow/s390/143959 -> ciflow/s390/143959 2025-07-17T08:34:15.8107292Z * [new tag] ciflow/s390/151447 -> ciflow/s390/151447 2025-07-17T08:34:15.8109902Z * [new tag] ciflow/slow/01c7106 -> ciflow/slow/01c7106 2025-07-17T08:34:15.8112008Z * [new tag] ciflow/slow/0577043 -> ciflow/slow/0577043 2025-07-17T08:34:15.8115053Z * [new tag] ciflow/slow/0d5b74da0cab798fbfdb9caa53fad816999c8386-sdym -> ciflow/slow/0d5b74da0cab798fbfdb9caa53fad816999c8386-sdym 2025-07-17T08:34:15.8117147Z * [new tag] ciflow/slow/0e81104 -> ciflow/slow/0e81104 2025-07-17T08:34:15.8119318Z * [new tag] ciflow/slow/157385 -> ciflow/slow/157385 2025-07-17T08:34:15.8121643Z * [new tag] ciflow/slow/157748 -> ciflow/slow/157748 2025-07-17T08:34:15.8123515Z * [new tag] ciflow/slow/158222 -> ciflow/slow/158222 2025-07-17T08:34:15.8125619Z * [new tag] ciflow/slow/158312 -> ciflow/slow/158312 2025-07-17T08:34:15.8127741Z * [new tag] ciflow/slow/158424 -> ciflow/slow/158424 2025-07-17T08:34:15.8130050Z * [new tag] ciflow/slow/1732077 -> ciflow/slow/1732077 2025-07-17T08:34:15.8132422Z * [new tag] ciflow/slow/187eb7c -> ciflow/slow/187eb7c 2025-07-17T08:34:15.8134722Z * [new tag] ciflow/slow/1faef89 -> ciflow/slow/1faef89 2025-07-17T08:34:15.8136928Z * [new tag] ciflow/slow/3920ec1 -> ciflow/slow/3920ec1 2025-07-17T08:34:15.8139166Z * [new tag] ciflow/slow/3b7c6b2 -> ciflow/slow/3b7c6b2 2025-07-17T08:34:15.8141456Z * [new tag] ciflow/slow/59a3759 -> ciflow/slow/59a3759 2025-07-17T08:34:15.8143647Z * [new tag] ciflow/slow/70ef0bb -> ciflow/slow/70ef0bb 2025-07-17T08:34:15.8145961Z * [new tag] ciflow/slow/788ff06 -> ciflow/slow/788ff06 2025-07-17T08:34:15.8148612Z * [new tag] ciflow/slow/8751002215790a3a88750faa8f4366933e296693-sdym -> ciflow/slow/8751002215790a3a88750faa8f4366933e296693-sdym 2025-07-17T08:34:15.8150646Z * [new tag] ciflow/slow/9d85864 -> ciflow/slow/9d85864 2025-07-17T08:34:15.8152890Z * [new tag] ciflow/slow/9ffad5b -> ciflow/slow/9ffad5b 2025-07-17T08:34:15.8155143Z * [new tag] ciflow/slow/a206e8b -> ciflow/slow/a206e8b 2025-07-17T08:34:15.8157501Z * [new tag] ciflow/slow/a837609 -> ciflow/slow/a837609 2025-07-17T08:34:15.8159714Z * [new tag] ciflow/slow/af841f3 -> ciflow/slow/af841f3 2025-07-17T08:34:15.8162311Z * [new tag] ciflow/slow/da3aba1e46157c4df504b067477cdf2b3c96b194-sdym -> ciflow/slow/da3aba1e46157c4df504b067477cdf2b3c96b194-sdym 2025-07-17T08:34:15.8164532Z * [new tag] ciflow/triton_binaries/158408 -> ciflow/triton_binaries/158408 2025-07-17T08:34:15.8166461Z * [new tag] ciflow/triton_binaries/158459 -> ciflow/triton_binaries/158459 2025-07-17T08:34:15.8168799Z * [new tag] ciflow/trunk/113258 -> ciflow/trunk/113258 2025-07-17T08:34:15.8170768Z * [new tag] ciflow/trunk/137400 -> ciflow/trunk/137400 2025-07-17T08:34:15.8172676Z * [new tag] ciflow/trunk/139971 -> ciflow/trunk/139971 2025-07-17T08:34:15.8174622Z * [new tag] ciflow/trunk/143712 -> ciflow/trunk/143712 2025-07-17T08:34:15.8176634Z * [new tag] ciflow/trunk/144557 -> ciflow/trunk/144557 2025-07-17T08:34:15.8178799Z * [new tag] ciflow/trunk/147470 -> ciflow/trunk/147470 2025-07-17T08:34:15.8180619Z * [new tag] ciflow/trunk/148180 -> ciflow/trunk/148180 2025-07-17T08:34:15.8182592Z * [new tag] ciflow/trunk/148328 -> ciflow/trunk/148328 2025-07-17T08:34:15.8184541Z * [new tag] ciflow/trunk/148492 -> ciflow/trunk/148492 2025-07-17T08:34:15.8186748Z * [new tag] ciflow/trunk/149003 -> ciflow/trunk/149003 2025-07-17T08:34:15.8188703Z * [new tag] ciflow/trunk/149601 -> ciflow/trunk/149601 2025-07-17T08:34:15.8190639Z * [new tag] ciflow/trunk/149961 -> ciflow/trunk/149961 2025-07-17T08:34:15.8192706Z * [new tag] ciflow/trunk/150312 -> ciflow/trunk/150312 2025-07-17T08:34:15.8194685Z * [new tag] ciflow/trunk/150691 -> ciflow/trunk/150691 2025-07-17T08:34:15.8196675Z * [new tag] ciflow/trunk/150762 -> ciflow/trunk/150762 2025-07-17T08:34:15.8198880Z * [new tag] ciflow/trunk/151777 -> ciflow/trunk/151777 2025-07-17T08:34:15.8200678Z * [new tag] ciflow/trunk/151845 -> ciflow/trunk/151845 2025-07-17T08:34:15.8202661Z * [new tag] ciflow/trunk/152624 -> ciflow/trunk/152624 2025-07-17T08:34:15.8204832Z * [new tag] ciflow/trunk/153666 -> ciflow/trunk/153666 2025-07-17T08:34:15.8206870Z * [new tag] ciflow/trunk/154149 -> ciflow/trunk/154149 2025-07-17T08:34:15.8208855Z * [new tag] ciflow/trunk/154199 -> ciflow/trunk/154199 2025-07-17T08:34:15.8210886Z * [new tag] ciflow/trunk/154694 -> ciflow/trunk/154694 2025-07-17T08:34:15.8212956Z * [new tag] ciflow/trunk/154983 -> ciflow/trunk/154983 2025-07-17T08:34:15.8214972Z * [new tag] ciflow/trunk/155489 -> ciflow/trunk/155489 2025-07-17T08:34:15.8217020Z * [new tag] ciflow/trunk/155958 -> ciflow/trunk/155958 2025-07-17T08:34:15.8219292Z * [new tag] ciflow/trunk/156049 -> ciflow/trunk/156049 2025-07-17T08:34:15.8221399Z * [new tag] ciflow/trunk/156097 -> ciflow/trunk/156097 2025-07-17T08:34:15.8223409Z * [new tag] ciflow/trunk/156141 -> ciflow/trunk/156141 2025-07-17T08:34:15.8225447Z * [new tag] ciflow/trunk/156165 -> ciflow/trunk/156165 2025-07-17T08:34:15.8227641Z * [new tag] ciflow/trunk/156175 -> ciflow/trunk/156175 2025-07-17T08:34:15.8229623Z * [new tag] ciflow/trunk/156192 -> ciflow/trunk/156192 2025-07-17T08:34:15.8231676Z * [new tag] ciflow/trunk/156296 -> ciflow/trunk/156296 2025-07-17T08:34:15.8233634Z * [new tag] ciflow/trunk/156370 -> ciflow/trunk/156370 2025-07-17T08:34:15.8235655Z * [new tag] ciflow/trunk/156559 -> ciflow/trunk/156559 2025-07-17T08:34:15.8237612Z * [new tag] ciflow/trunk/156605 -> ciflow/trunk/156605 2025-07-17T08:34:15.8239642Z * [new tag] ciflow/trunk/156626 -> ciflow/trunk/156626 2025-07-17T08:34:15.8242493Z * [new tag] ciflow/trunk/156666 -> ciflow/trunk/156666 2025-07-17T08:34:15.8244655Z * [new tag] ciflow/trunk/156753 -> ciflow/trunk/156753 2025-07-17T08:34:15.8246693Z * [new tag] ciflow/trunk/156781 -> ciflow/trunk/156781 2025-07-17T08:34:15.8248643Z * [new tag] ciflow/trunk/156874 -> ciflow/trunk/156874 2025-07-17T08:34:15.8250647Z * [new tag] ciflow/trunk/157199 -> ciflow/trunk/157199 2025-07-17T08:34:15.8252644Z * [new tag] ciflow/trunk/157432 -> ciflow/trunk/157432 2025-07-17T08:34:15.8254619Z * [new tag] ciflow/trunk/157520 -> ciflow/trunk/157520 2025-07-17T08:34:15.8256695Z * [new tag] ciflow/trunk/157550 -> ciflow/trunk/157550 2025-07-17T08:34:15.8258577Z * [new tag] ciflow/trunk/157552 -> ciflow/trunk/157552 2025-07-17T08:34:15.8260640Z * [new tag] ciflow/trunk/157580 -> ciflow/trunk/157580 2025-07-17T08:34:15.8262780Z * [new tag] ciflow/trunk/157685 -> ciflow/trunk/157685 2025-07-17T08:34:15.8264794Z * [new tag] ciflow/trunk/157689 -> ciflow/trunk/157689 2025-07-17T08:34:15.8266919Z * [new tag] ciflow/trunk/157699 -> ciflow/trunk/157699 2025-07-17T08:34:15.8268915Z * [new tag] ciflow/trunk/157748 -> ciflow/trunk/157748 2025-07-17T08:34:15.8270898Z * [new tag] ciflow/trunk/157791 -> ciflow/trunk/157791 2025-07-17T08:34:15.8272863Z * [new tag] ciflow/trunk/157804 -> ciflow/trunk/157804 2025-07-17T08:34:15.8274839Z * [new tag] ciflow/trunk/157887 -> ciflow/trunk/157887 2025-07-17T08:34:15.8277036Z * [new tag] ciflow/trunk/157908 -> ciflow/trunk/157908 2025-07-17T08:34:15.8278949Z * [new tag] ciflow/trunk/157910 -> ciflow/trunk/157910 2025-07-17T08:34:15.8281042Z * [new tag] ciflow/trunk/157935 -> ciflow/trunk/157935 2025-07-17T08:34:15.8283319Z * [new tag] ciflow/trunk/157963 -> ciflow/trunk/157963 2025-07-17T08:34:15.8285470Z * [new tag] ciflow/trunk/157994 -> ciflow/trunk/157994 2025-07-17T08:34:15.8287450Z * [new tag] ciflow/trunk/158015 -> ciflow/trunk/158015 2025-07-17T08:34:15.8289393Z * [new tag] ciflow/trunk/158062 -> ciflow/trunk/158062 2025-07-17T08:34:15.8291382Z * [new tag] ciflow/trunk/158072 -> ciflow/trunk/158072 2025-07-17T08:34:15.8293345Z * [new tag] ciflow/trunk/158091 -> ciflow/trunk/158091 2025-07-17T08:34:15.8295440Z * [new tag] ciflow/trunk/158104 -> ciflow/trunk/158104 2025-07-17T08:34:15.8297370Z * [new tag] ciflow/trunk/158119 -> ciflow/trunk/158119 2025-07-17T08:34:15.8299382Z * [new tag] ciflow/trunk/158148 -> ciflow/trunk/158148 2025-07-17T08:34:15.8301495Z * [new tag] ciflow/trunk/158185 -> ciflow/trunk/158185 2025-07-17T08:34:15.8303819Z * [new tag] ciflow/trunk/158203 -> ciflow/trunk/158203 2025-07-17T08:34:15.8305896Z * [new tag] ciflow/trunk/158219 -> ciflow/trunk/158219 2025-07-17T08:34:15.8307982Z * [new tag] ciflow/trunk/158220 -> ciflow/trunk/158220 2025-07-17T08:34:15.8309956Z * [new tag] ciflow/trunk/158222 -> ciflow/trunk/158222 2025-07-17T08:34:15.8311957Z * [new tag] ciflow/trunk/158223 -> ciflow/trunk/158223 2025-07-17T08:34:15.8313960Z * [new tag] ciflow/trunk/158224 -> ciflow/trunk/158224 2025-07-17T08:34:15.8316244Z * [new tag] ciflow/trunk/158229 -> ciflow/trunk/158229 2025-07-17T08:34:15.8318152Z * [new tag] ciflow/trunk/158249 -> ciflow/trunk/158249 2025-07-17T08:34:15.8320220Z * [new tag] ciflow/trunk/158259 -> ciflow/trunk/158259 2025-07-17T08:34:15.8322206Z * [new tag] ciflow/trunk/158289 -> ciflow/trunk/158289 2025-07-17T08:34:15.8324235Z * [new tag] ciflow/trunk/158312 -> ciflow/trunk/158312 2025-07-17T08:34:15.8326361Z * [new tag] ciflow/trunk/158323 -> ciflow/trunk/158323 2025-07-17T08:34:15.8328545Z * [new tag] ciflow/trunk/158356 -> ciflow/trunk/158356 2025-07-17T08:34:15.8330572Z * [new tag] ciflow/trunk/158363 -> ciflow/trunk/158363 2025-07-17T08:34:15.8332577Z * [new tag] ciflow/trunk/158368 -> ciflow/trunk/158368 2025-07-17T08:34:15.8334706Z * [new tag] ciflow/trunk/158373 -> ciflow/trunk/158373 2025-07-17T08:34:15.8336723Z * [new tag] ciflow/trunk/158377 -> ciflow/trunk/158377 2025-07-17T08:34:15.8338874Z * [new tag] ciflow/trunk/158380 -> ciflow/trunk/158380 2025-07-17T08:34:15.8340894Z * [new tag] ciflow/trunk/158400 -> ciflow/trunk/158400 2025-07-17T08:34:15.8342835Z * [new tag] ciflow/trunk/158418 -> ciflow/trunk/158418 2025-07-17T08:34:15.8344887Z * [new tag] ciflow/trunk/158424 -> ciflow/trunk/158424 2025-07-17T08:34:15.8347000Z * [new tag] ciflow/trunk/158430 -> ciflow/trunk/158430 2025-07-17T08:34:15.8349015Z * [new tag] ciflow/trunk/158442 -> ciflow/trunk/158442 2025-07-17T08:34:15.8351072Z * [new tag] ciflow/trunk/158458 -> ciflow/trunk/158458 2025-07-17T08:34:15.8353113Z * [new tag] ciflow/trunk/158459 -> ciflow/trunk/158459 2025-07-17T08:34:15.8355522Z * [new tag] ciflow/trunk/158473 -> ciflow/trunk/158473 2025-07-17T08:34:15.8357254Z * [new tag] ciflow/trunk/158479 -> ciflow/trunk/158479 2025-07-17T08:34:15.8359255Z * [new tag] ciflow/trunk/158485 -> ciflow/trunk/158485 2025-07-17T08:34:15.8361313Z * [new tag] ciflow/trunk/158492 -> ciflow/trunk/158492 2025-07-17T08:34:15.8363300Z * [new tag] ciflow/trunk/158524 -> ciflow/trunk/158524 2025-07-17T08:34:15.8365476Z * [new tag] ciflow/trunk/158532 -> ciflow/trunk/158532 2025-07-17T08:34:15.8367461Z * [new tag] ciflow/trunk/158541 -> ciflow/trunk/158541 2025-07-17T08:34:15.8369480Z * [new tag] ciflow/trunk/158543 -> ciflow/trunk/158543 2025-07-17T08:34:15.8372261Z * [new tag] ciflow/unstable/123 -> ciflow/unstable/123 2025-07-17T08:34:15.8374884Z * [new tag] ciflow/win-arm64/157935 -> ciflow/win-arm64/157935 2025-07-17T08:34:15.8377394Z * [new tag] ciflow/xpu/139971 -> ciflow/xpu/139971 2025-07-17T08:34:15.8379355Z * [new tag] ciflow/xpu/150218 -> ciflow/xpu/150218 2025-07-17T08:34:15.8381539Z * [new tag] ciflow/xpu/155200 -> ciflow/xpu/155200 2025-07-17T08:34:15.8383496Z * [new tag] ciflow/xpu/156812 -> ciflow/xpu/156812 2025-07-17T08:34:15.8385514Z * [new tag] ciflow/xpu/157699 -> ciflow/xpu/157699 2025-07-17T08:34:15.8387798Z * [new tag] ciflow/xpu/157954 -> ciflow/xpu/157954 2025-07-17T08:34:15.8389850Z * [new tag] ciflow/xpu/158336 -> ciflow/xpu/158336 2025-07-17T08:34:15.8391935Z * [new tag] ciflow/xpu/158337 -> ciflow/xpu/158337 2025-07-17T08:34:15.8394051Z * [new tag] ciflow/xpu/158340 -> ciflow/xpu/158340 2025-07-17T08:34:15.8396177Z * [new tag] ciflow/xpu/158533 -> ciflow/xpu/158533 2025-07-17T08:34:15.8398142Z * [new tag] ciflow/xpu/158542 -> ciflow/xpu/158542 2025-07-17T08:34:15.8400346Z * [new tag] cslpull75 -> cslpull75 2025-07-17T08:34:15.8402487Z * [new tag] cslpull76 -> cslpull76 2025-07-17T08:34:15.8404665Z * [new tag] cslpull77 -> cslpull77 2025-07-17T08:34:15.8406836Z * [new tag] cslpull78 -> cslpull78 2025-07-17T08:34:15.8408964Z * [new tag] cslpull79 -> cslpull79 2025-07-17T08:34:15.8411193Z * [new tag] cslpull80 -> cslpull80 2025-07-17T08:34:15.8413395Z * [new tag] cslpull81 -> cslpull81 2025-07-17T08:34:15.8415638Z * [new tag] cslpull82 -> cslpull82 2025-07-17T08:34:15.8417803Z * [new tag] cslpull83 -> cslpull83 2025-07-17T08:34:15.8419930Z * [new tag] cslpull84 -> cslpull84 2025-07-17T08:34:15.8422047Z * [new tag] cslpull85 -> cslpull85 2025-07-17T08:34:15.8424220Z * [new tag] cslpull86 -> cslpull86 2025-07-17T08:34:15.8426416Z * [new tag] cslpull87 -> cslpull87 2025-07-17T08:34:15.8428726Z * [new tag] cslpull88 -> cslpull88 2025-07-17T08:34:15.8430835Z * [new tag] cslpull89 -> cslpull89 2025-07-17T08:34:15.8432864Z * [new tag] cslpull90 -> cslpull90 2025-07-17T08:34:15.8435306Z * [new tag] cslpull91 -> cslpull91 2025-07-17T08:34:15.8437434Z * [new tag] cslpull92 -> cslpull92 2025-07-17T08:34:15.8439816Z * [new tag] flight_5 -> flight_5 2025-07-17T08:34:15.8441888Z * [new tag] flight_5.1 -> flight_5.1 2025-07-17T08:34:15.8444108Z * [new tag] flight_5.2 -> flight_5.2 2025-07-17T08:34:15.8446206Z * [new tag] flight_5.3 -> flight_5.3 2025-07-17T08:34:15.8448393Z * [new tag] forpull1 -> forpull1 2025-07-17T08:34:15.8451053Z * [new tag] malfet/tag-2ef5611 -> malfet/tag-2ef5611 2025-07-17T08:34:15.8453141Z * [new tag] malfet/tag-317b1a0 -> malfet/tag-317b1a0 2025-07-17T08:34:15.8455318Z * [new tag] malfet/tag-ec6f767 -> malfet/tag-ec6f767 2025-07-17T08:34:15.8457566Z * [new tag] nightly-binary -> nightly-binary 2025-07-17T08:34:15.8459589Z * [new tag] sqzhang_flight4_plus -> sqzhang_flight4_plus 2025-07-17T08:34:15.8461824Z * [new tag] sqzhang_flight_3 -> sqzhang_flight_3 2025-07-17T08:34:15.8464628Z * [new tag] trunk/0083032e7559dc8f02483ba60373adfcdaf9dae6 -> trunk/0083032e7559dc8f02483ba60373adfcdaf9dae6 2025-07-17T08:34:15.8469530Z * [new tag] trunk/008345be9d0c32f67459bcf3e6705be43d496f74 -> trunk/008345be9d0c32f67459bcf3e6705be43d496f74 2025-07-17T08:34:15.8471635Z * [new tag] trunk/00ae620b9f72cc751672c2fd92a04fc86704a87a -> trunk/00ae620b9f72cc751672c2fd92a04fc86704a87a 2025-07-17T08:34:15.8473609Z * [new tag] trunk/0105cd89ab508ec56126c1de85c8f5b5acc446b5 -> trunk/0105cd89ab508ec56126c1de85c8f5b5acc446b5 2025-07-17T08:34:15.8475680Z * [new tag] trunk/011026205a9d4c38458130f8ca242028f6184bf0 -> trunk/011026205a9d4c38458130f8ca242028f6184bf0 2025-07-17T08:34:15.8477785Z * [new tag] trunk/013cf1e3302d27de36588cf7a7130d76a5686bad -> trunk/013cf1e3302d27de36588cf7a7130d76a5686bad 2025-07-17T08:34:15.8479912Z * [new tag] trunk/013dfeabb405274546f19637c04f8a5f75923316 -> trunk/013dfeabb405274546f19637c04f8a5f75923316 2025-07-17T08:34:15.8482225Z * [new tag] trunk/018e9826a2ed09cfcf7424e0b3215bef5ad6499e -> trunk/018e9826a2ed09cfcf7424e0b3215bef5ad6499e 2025-07-17T08:34:15.8484326Z * [new tag] trunk/019e30e3b80d091d64253df4cdd149713e3e910e -> trunk/019e30e3b80d091d64253df4cdd149713e3e910e 2025-07-17T08:34:15.8486391Z * [new tag] trunk/01b0f09931d47bd2716398a0c335b2807dc3074d -> trunk/01b0f09931d47bd2716398a0c335b2807dc3074d 2025-07-17T08:34:15.8488544Z * [new tag] trunk/01b8f5e685fdb34aa50b73a84a55fc285e6b904a -> trunk/01b8f5e685fdb34aa50b73a84a55fc285e6b904a 2025-07-17T08:34:15.8490728Z * [new tag] trunk/02080c2cd94b37ad92e3d8803bc773f6abd2ae2f -> trunk/02080c2cd94b37ad92e3d8803bc773f6abd2ae2f 2025-07-17T08:34:15.8492843Z * [new tag] trunk/020da744370f6ee23e377357e9acc330b5610a67 -> trunk/020da744370f6ee23e377357e9acc330b5610a67 2025-07-17T08:34:15.8494951Z * [new tag] trunk/023887fc5af5a7273d7eb0388fdf1f0e87eafb28 -> trunk/023887fc5af5a7273d7eb0388fdf1f0e87eafb28 2025-07-17T08:34:15.8497164Z * [new tag] trunk/02608e560a1d7b669ad450a89542f7e9ba068658 -> trunk/02608e560a1d7b669ad450a89542f7e9ba068658 2025-07-17T08:34:15.8499288Z * [new tag] trunk/02715d0876bc66f46b9c7b4f277bedf8e251cc82 -> trunk/02715d0876bc66f46b9c7b4f277bedf8e251cc82 2025-07-17T08:34:15.8501784Z * [new tag] trunk/02724b5f649b93ef7960962bdde7a667c0893d21 -> trunk/02724b5f649b93ef7960962bdde7a667c0893d21 2025-07-17T08:34:15.8503938Z * [new tag] trunk/029e2b05c225588098d3eba445fd04189691f77d -> trunk/029e2b05c225588098d3eba445fd04189691f77d 2025-07-17T08:34:15.8506231Z * [new tag] trunk/02a9d9095f397f56fe64ef07ced8e7ffed6dcba1 -> trunk/02a9d9095f397f56fe64ef07ced8e7ffed6dcba1 2025-07-17T08:34:15.8508623Z * [new tag] trunk/02c7ab2f9baac05bd199392b70bc016d55f99b13 -> trunk/02c7ab2f9baac05bd199392b70bc016d55f99b13 2025-07-17T08:34:15.8510320Z * [new tag] trunk/03023f178c611249d41c62369ba95fe54316fe90 -> trunk/03023f178c611249d41c62369ba95fe54316fe90 2025-07-17T08:34:15.8512454Z * [new tag] trunk/03488d820c292b8ec4bfd9a4e25d5f28068c9375 -> trunk/03488d820c292b8ec4bfd9a4e25d5f28068c9375 2025-07-17T08:34:15.8514638Z * [new tag] trunk/034a7f6437bec853a938dbc19b549b9319b67f69 -> trunk/034a7f6437bec853a938dbc19b549b9319b67f69 2025-07-17T08:34:15.8516571Z * [new tag] trunk/0364db7cd14ffa67b48ef8c27fefbb3eed2b065d -> trunk/0364db7cd14ffa67b48ef8c27fefbb3eed2b065d 2025-07-17T08:34:15.8518613Z * [new tag] trunk/03852ddc22350eb8b6ed6b61777639ce6080f3dc -> trunk/03852ddc22350eb8b6ed6b61777639ce6080f3dc 2025-07-17T08:34:15.8520656Z * [new tag] trunk/039a1ce0ebbeb0adacbf9537c4085d977dba150a -> trunk/039a1ce0ebbeb0adacbf9537c4085d977dba150a 2025-07-17T08:34:15.8522925Z * [new tag] trunk/03b307575a98dc1d953c9d3521a9489e0e61e70c -> trunk/03b307575a98dc1d953c9d3521a9489e0e61e70c 2025-07-17T08:34:15.8525048Z * [new tag] trunk/04178d347c6baec5613f8063a2c77cf6f9657ee2 -> trunk/04178d347c6baec5613f8063a2c77cf6f9657ee2 2025-07-17T08:34:15.8527047Z * [new tag] trunk/04349f9ee541c7d07cc057bbe739f46bd4c30dcc -> trunk/04349f9ee541c7d07cc057bbe739f46bd4c30dcc 2025-07-17T08:34:15.8540859Z * [new tag] trunk/049dc48d1edf49c26d493fdb271fe5e5adc9c985 -> trunk/049dc48d1edf49c26d493fdb271fe5e5adc9c985 2025-07-17T08:34:15.8541323Z * [new tag] trunk/04b91a9e43518fcd34a474a832bac05d4583fcc3 -> trunk/04b91a9e43518fcd34a474a832bac05d4583fcc3 2025-07-17T08:34:15.8541576Z * [new tag] trunk/04bd7e6850e8efec77994963ffee87549555b9c3 -> trunk/04bd7e6850e8efec77994963ffee87549555b9c3 2025-07-17T08:34:15.8541822Z * [new tag] trunk/04cf2c9d24b7d9e7f7cdd4cf98c8bd0908c38110 -> trunk/04cf2c9d24b7d9e7f7cdd4cf98c8bd0908c38110 2025-07-17T08:34:15.8542063Z * [new tag] trunk/0504480f37714a289b2ba32c9cf32a5e50e86d38 -> trunk/0504480f37714a289b2ba32c9cf32a5e50e86d38 2025-07-17T08:34:15.8542278Z * [new tag] trunk/054cd4ca28d17507df46054fe355c697f6a07ce8 -> trunk/054cd4ca28d17507df46054fe355c697f6a07ce8 2025-07-17T08:34:15.8542536Z * [new tag] trunk/058fb1790f2c474cd4ecb5ec625eef896c554544 -> trunk/058fb1790f2c474cd4ecb5ec625eef896c554544 2025-07-17T08:34:15.8544287Z * [new tag] trunk/0596323c35d245099a4f4d3172b0efa20a639c84 -> trunk/0596323c35d245099a4f4d3172b0efa20a639c84 2025-07-17T08:34:15.8546482Z * [new tag] trunk/05d7288e316ae5c9c661c4529f9f130a46263e5b -> trunk/05d7288e316ae5c9c661c4529f9f130a46263e5b 2025-07-17T08:34:15.8548688Z * [new tag] trunk/05dd638ee98b36254c84095894c36fd0e7d95544 -> trunk/05dd638ee98b36254c84095894c36fd0e7d95544 2025-07-17T08:34:15.8550936Z * [new tag] trunk/05dfd312cfbfdecc6cb1e7d1d0bb4ee18370ae7e -> trunk/05dfd312cfbfdecc6cb1e7d1d0bb4ee18370ae7e 2025-07-17T08:34:15.8552930Z * [new tag] trunk/05faba40287cf7d8734da96cb2e904f39710bf29 -> trunk/05faba40287cf7d8734da96cb2e904f39710bf29 2025-07-17T08:34:15.8556024Z * [new tag] trunk/060838c2312ad207c7afe2c86f8a484afea5f328 -> trunk/060838c2312ad207c7afe2c86f8a484afea5f328 2025-07-17T08:34:15.8558181Z * [new tag] trunk/0629dfb860b00a10550d91dd395968f663f45fdd -> trunk/0629dfb860b00a10550d91dd395968f663f45fdd 2025-07-17T08:34:15.8560359Z * [new tag] trunk/06408dae49d06b6146fdd9d7a37eb5dde4f5e78d -> trunk/06408dae49d06b6146fdd9d7a37eb5dde4f5e78d 2025-07-17T08:34:15.8562498Z * [new tag] trunk/0640cfa38c1426a41ab4a0b3e3dab7c730cdc2ad -> trunk/0640cfa38c1426a41ab4a0b3e3dab7c730cdc2ad 2025-07-17T08:34:15.8564862Z * [new tag] trunk/064288cbab94c9931ca2296a2b9723e864f9050a -> trunk/064288cbab94c9931ca2296a2b9723e864f9050a 2025-07-17T08:34:15.8566780Z * [new tag] trunk/064a7db7fc3b06eb8e14d6c7d767f87fcf396ab6 -> trunk/064a7db7fc3b06eb8e14d6c7d767f87fcf396ab6 2025-07-17T08:34:15.8568901Z * [new tag] trunk/067fd0b3abd28f1b7dae883ffec5bc7459d35970 -> trunk/067fd0b3abd28f1b7dae883ffec5bc7459d35970 2025-07-17T08:34:15.8571037Z * [new tag] trunk/06930706a195144cd6172a5f2eb505cec56e9ec3 -> trunk/06930706a195144cd6172a5f2eb505cec56e9ec3 2025-07-17T08:34:15.8573231Z * [new tag] trunk/06a40b685056f60c4fbdc10d09a85b9fc3fca34f -> trunk/06a40b685056f60c4fbdc10d09a85b9fc3fca34f 2025-07-17T08:34:15.8575447Z * [new tag] trunk/06a67a8948dac9d02f22b3e2591a43b60981cdb4 -> trunk/06a67a8948dac9d02f22b3e2591a43b60981cdb4 2025-07-17T08:34:15.8577559Z * [new tag] trunk/06b3265cb1a27ea3090dc1fc86a6ce17518f989c -> trunk/06b3265cb1a27ea3090dc1fc86a6ce17518f989c 2025-07-17T08:34:15.8579638Z * [new tag] trunk/06f39a71b6cd14e0844b5eb1e0e085f7ae78d221 -> trunk/06f39a71b6cd14e0844b5eb1e0e085f7ae78d221 2025-07-17T08:34:15.8581592Z * [new tag] trunk/070aa59e491aa6987459cd1941ef647f2ad13858 -> trunk/070aa59e491aa6987459cd1941ef647f2ad13858 2025-07-17T08:34:15.8583766Z * [new tag] trunk/070e580d301efd626e327d1f88e0921b476d0f30 -> trunk/070e580d301efd626e327d1f88e0921b476d0f30 2025-07-17T08:34:15.8585972Z * [new tag] trunk/0756ebcd4890b19072e586fb2cbd2b25a80f91f4 -> trunk/0756ebcd4890b19072e586fb2cbd2b25a80f91f4 2025-07-17T08:34:15.8588182Z * [new tag] trunk/0797b2b6a80cf70a7accc3d5413186e7693d4451 -> trunk/0797b2b6a80cf70a7accc3d5413186e7693d4451 2025-07-17T08:34:15.8590298Z * [new tag] trunk/07bb097698223fe42d54b019638dd026c75f09a9 -> trunk/07bb097698223fe42d54b019638dd026c75f09a9 2025-07-17T08:34:15.8592477Z * [new tag] trunk/07da8a469bdd69966e1f832e3d09d991798f854c -> trunk/07da8a469bdd69966e1f832e3d09d991798f854c 2025-07-17T08:34:15.8594667Z * [new tag] trunk/07e340e29ccbe18ef59e8f60ec3f03ccc44e5e9c -> trunk/07e340e29ccbe18ef59e8f60ec3f03ccc44e5e9c 2025-07-17T08:34:15.8596948Z * [new tag] trunk/07eb374e7eefaa84a997d0adaaeecb4706d60e74 -> trunk/07eb374e7eefaa84a997d0adaaeecb4706d60e74 2025-07-17T08:34:15.8599071Z * [new tag] trunk/08274640028f68418d8f889d01af577b90ac2ec1 -> trunk/08274640028f68418d8f889d01af577b90ac2ec1 2025-07-17T08:34:15.8601289Z * [new tag] trunk/085f270a00b4452bbb005d6b3d448e9d0b9d6fa0 -> trunk/085f270a00b4452bbb005d6b3d448e9d0b9d6fa0 2025-07-17T08:34:15.8603426Z * [new tag] trunk/0860606729d16b9855157404da9b0d4654f3c626 -> trunk/0860606729d16b9855157404da9b0d4654f3c626 2025-07-17T08:34:15.8605449Z * [new tag] trunk/08799217aeb17128d89d675ce5b537761286417a -> trunk/08799217aeb17128d89d675ce5b537761286417a 2025-07-17T08:34:15.8607605Z * [new tag] trunk/08d15d3ec15b9b3fce81713cde20901806ecef42 -> trunk/08d15d3ec15b9b3fce81713cde20901806ecef42 2025-07-17T08:34:15.8609562Z * [new tag] trunk/08dae945ae380d80efbaf140a95abfc5d96e5100 -> trunk/08dae945ae380d80efbaf140a95abfc5d96e5100 2025-07-17T08:34:15.8611815Z * [new tag] trunk/08e9dd280f497fc820e35c458c843dc44f0282c6 -> trunk/08e9dd280f497fc820e35c458c843dc44f0282c6 2025-07-17T08:34:15.8613957Z * [new tag] trunk/092aed1b18c31042d7772a6f9ec1959df4bf5c3c -> trunk/092aed1b18c31042d7772a6f9ec1959df4bf5c3c 2025-07-17T08:34:15.8616083Z * [new tag] trunk/09328eb02f5412d2211b5fd638ce82d0e03b9c1f -> trunk/09328eb02f5412d2211b5fd638ce82d0e03b9c1f 2025-07-17T08:34:15.8618344Z * [new tag] trunk/0935a97d959918727700ae2c1ebeadc5b12a2b6c -> trunk/0935a97d959918727700ae2c1ebeadc5b12a2b6c 2025-07-17T08:34:15.8620437Z * [new tag] trunk/093aaccae254355b3637ef8760e762a3f9c3783c -> trunk/093aaccae254355b3637ef8760e762a3f9c3783c 2025-07-17T08:34:15.8622791Z * [new tag] trunk/093fd47dbef4f08691210de6c08cf269f28e4de5 -> trunk/093fd47dbef4f08691210de6c08cf269f28e4de5 2025-07-17T08:34:15.8624779Z * [new tag] trunk/099d0d6121125062ebc05771c8330cb7cd8d053a -> trunk/099d0d6121125062ebc05771c8330cb7cd8d053a 2025-07-17T08:34:15.8627006Z * [new tag] trunk/09db3a22e8783c4841697317688ba9467c7cc457 -> trunk/09db3a22e8783c4841697317688ba9467c7cc457 2025-07-17T08:34:15.8629240Z * [new tag] trunk/09ffba3cf79eeedf48a0fb39a26dcc0294872480 -> trunk/09ffba3cf79eeedf48a0fb39a26dcc0294872480 2025-07-17T08:34:15.8631516Z * [new tag] trunk/0a0023d9840570938a10472418eaac85098ec41b -> trunk/0a0023d9840570938a10472418eaac85098ec41b 2025-07-17T08:34:15.8633657Z * [new tag] trunk/0a092c7de67767eed3adb7abc0d49dfd3d26d0cd -> trunk/0a092c7de67767eed3adb7abc0d49dfd3d26d0cd 2025-07-17T08:34:15.8635866Z * [new tag] trunk/0a16818d5b3fdf0fb8148dd6b849687251a56376 -> trunk/0a16818d5b3fdf0fb8148dd6b849687251a56376 2025-07-17T08:34:15.8637969Z * [new tag] trunk/0a2ec7681d2af973d8daaf7905431a088739dc90 -> trunk/0a2ec7681d2af973d8daaf7905431a088739dc90 2025-07-17T08:34:15.8640047Z * [new tag] trunk/0a624c2dc5675e35917976467a8c533baf2a98a0 -> trunk/0a624c2dc5675e35917976467a8c533baf2a98a0 2025-07-17T08:34:15.8642155Z * [new tag] trunk/0a63053fe90405cc6ee5e42fa007e32ed1e2cbac -> trunk/0a63053fe90405cc6ee5e42fa007e32ed1e2cbac 2025-07-17T08:34:15.8644152Z * [new tag] trunk/0a6b66c881cba3f6a6c1a3cb8ddf698846d99822 -> trunk/0a6b66c881cba3f6a6c1a3cb8ddf698846d99822 2025-07-17T08:34:15.8646400Z * [new tag] trunk/0a99b026d6bd0f67dc2c0a20fe3228ddc4144854 -> trunk/0a99b026d6bd0f67dc2c0a20fe3228ddc4144854 2025-07-17T08:34:15.8648485Z * [new tag] trunk/0a9d450168ce58b2bb7f2cedc27a61012123564f -> trunk/0a9d450168ce58b2bb7f2cedc27a61012123564f 2025-07-17T08:34:15.8650575Z * [new tag] trunk/0ab075a69e4577a60c4dcbff7bcc2ecd0a15ce46 -> trunk/0ab075a69e4577a60c4dcbff7bcc2ecd0a15ce46 2025-07-17T08:34:15.8652773Z * [new tag] trunk/0ad88a2224e9f0a2ed4f405e827a745945d505ce -> trunk/0ad88a2224e9f0a2ed4f405e827a745945d505ce 2025-07-17T08:34:15.8654926Z * [new tag] trunk/0aed855b2bde6d9bd045bb20cc24544a9f2fb72b -> trunk/0aed855b2bde6d9bd045bb20cc24544a9f2fb72b 2025-07-17T08:34:15.8657026Z * [new tag] trunk/0b19d463d963a0b2ee5558d2c0bb79b2cbff6e64 -> trunk/0b19d463d963a0b2ee5558d2c0bb79b2cbff6e64 2025-07-17T08:34:15.8659204Z * [new tag] trunk/0b62465b99b23cb4afcd07424676cce34a676041 -> trunk/0b62465b99b23cb4afcd07424676cce34a676041 2025-07-17T08:34:15.8661469Z * [new tag] trunk/0b677560e686d828f9f5ed1cb87b1d7a54ee2781 -> trunk/0b677560e686d828f9f5ed1cb87b1d7a54ee2781 2025-07-17T08:34:15.8663523Z * [new tag] trunk/0b6c0898e6c352c8ea93daec854e704b41485375 -> trunk/0b6c0898e6c352c8ea93daec854e704b41485375 2025-07-17T08:34:15.8665711Z * [new tag] trunk/0bb733ba230051301b3fb3fa49d1d6662744b395 -> trunk/0bb733ba230051301b3fb3fa49d1d6662744b395 2025-07-17T08:34:15.8667943Z * [new tag] trunk/0bce39026917d10995ff47352c3da9fff0ec31ff -> trunk/0bce39026917d10995ff47352c3da9fff0ec31ff 2025-07-17T08:34:15.8670259Z * [new tag] trunk/0c58bdd8fb5f269aef100af8e2c43cfcf5f1f9dd -> trunk/0c58bdd8fb5f269aef100af8e2c43cfcf5f1f9dd 2025-07-17T08:34:15.8672546Z * [new tag] trunk/0ca2a79f5b80c1ec8be95e6f7de4182dd90f3502 -> trunk/0ca2a79f5b80c1ec8be95e6f7de4182dd90f3502 2025-07-17T08:34:15.8674735Z * [new tag] trunk/0cb36e2d62c811fcddea4c6d28b1c65246cdd160 -> trunk/0cb36e2d62c811fcddea4c6d28b1c65246cdd160 2025-07-17T08:34:15.8676897Z * [new tag] trunk/0cb85c188f3665c23766cda25c8169f1a436d36b -> trunk/0cb85c188f3665c23766cda25c8169f1a436d36b 2025-07-17T08:34:15.8679212Z * [new tag] trunk/0d01bafc34fc99a0b3e46cbf1ecfd8f97563efa6 -> trunk/0d01bafc34fc99a0b3e46cbf1ecfd8f97563efa6 2025-07-17T08:34:15.8681036Z * [new tag] trunk/0d17029fea3d96bb88b19912946648b47f8e003d -> trunk/0d17029fea3d96bb88b19912946648b47f8e003d 2025-07-17T08:34:15.8683268Z * [new tag] trunk/0d3d84d866040fad1b21a618c44802951a3cb15e -> trunk/0d3d84d866040fad1b21a618c44802951a3cb15e 2025-07-17T08:34:15.8685384Z * [new tag] trunk/0d77364ee3ff6435aa93df3b0303db66b5fc3254 -> trunk/0d77364ee3ff6435aa93df3b0303db66b5fc3254 2025-07-17T08:34:15.8687650Z * [new tag] trunk/0d8c029584b61da059b6225537764eadd2fa1223 -> trunk/0d8c029584b61da059b6225537764eadd2fa1223 2025-07-17T08:34:15.8689904Z * [new tag] trunk/0d8e4e2327137b370d91bd23931cd7691a95cc33 -> trunk/0d8e4e2327137b370d91bd23931cd7691a95cc33 2025-07-17T08:34:15.8692131Z * [new tag] trunk/0db3e0cf29604dae1007a678603e4dffd1c57562 -> trunk/0db3e0cf29604dae1007a678603e4dffd1c57562 2025-07-17T08:34:15.8694273Z * [new tag] trunk/0decd966af9cdcb7ab4410cf475d2fc09f2dea0c -> trunk/0decd966af9cdcb7ab4410cf475d2fc09f2dea0c 2025-07-17T08:34:15.8696423Z * [new tag] trunk/0e2013a12da944930012265bd1b75d0a19af4d7c -> trunk/0e2013a12da944930012265bd1b75d0a19af4d7c 2025-07-17T08:34:15.8698535Z * [new tag] trunk/0e47312ae5a687f0aed61db753d03180118cddc4 -> trunk/0e47312ae5a687f0aed61db753d03180118cddc4 2025-07-17T08:34:15.8700680Z * [new tag] trunk/0e9d8032a3431505c8a6a341375af9cb27afd537 -> trunk/0e9d8032a3431505c8a6a341375af9cb27afd537 2025-07-17T08:34:15.8702866Z * [new tag] trunk/0edc1b91f708c7635cf0f286f43724665934dc89 -> trunk/0edc1b91f708c7635cf0f286f43724665934dc89 2025-07-17T08:34:15.8704948Z * [new tag] trunk/0f0c01071476145fca7d70c68d029a88aeefe72c -> trunk/0f0c01071476145fca7d70c68d029a88aeefe72c 2025-07-17T08:34:15.8708326Z * [new tag] trunk/0f21fa84fb605c61482e4218df89f8bb1ef70c14 -> trunk/0f21fa84fb605c61482e4218df89f8bb1ef70c14 2025-07-17T08:34:15.8710613Z * [new tag] trunk/0f31445139d7ffaeeae95a230c536ef97970af5c -> trunk/0f31445139d7ffaeeae95a230c536ef97970af5c 2025-07-17T08:34:15.8712591Z * [new tag] trunk/0f47e76937f092263dd579e71d45e3193fbbc5df -> trunk/0f47e76937f092263dd579e71d45e3193fbbc5df 2025-07-17T08:34:15.8714612Z * [new tag] trunk/0f9c1b374fbb6f3b999855b26ebb869387b33dfb -> trunk/0f9c1b374fbb6f3b999855b26ebb869387b33dfb 2025-07-17T08:34:15.8716697Z * [new tag] trunk/0fa361e4295bbd8ac8b1f1ea74852535326f08f2 -> trunk/0fa361e4295bbd8ac8b1f1ea74852535326f08f2 2025-07-17T08:34:15.8718801Z * [new tag] trunk/0fd711df19a6523c9de6c820640ae53e32de14c6 -> trunk/0fd711df19a6523c9de6c820640ae53e32de14c6 2025-07-17T08:34:15.8720939Z * [new tag] trunk/1036f6d114bc22a9b4cf620cf7f8364ea2fd7a60 -> trunk/1036f6d114bc22a9b4cf620cf7f8364ea2fd7a60 2025-07-17T08:34:15.8723299Z * [new tag] trunk/104493487852f0cc232547ecbf7bebf5c7fe5982 -> trunk/104493487852f0cc232547ecbf7bebf5c7fe5982 2025-07-17T08:34:15.8725289Z * [new tag] trunk/1051b93192710d2e4bdf5fecd8e66c61731b55e1 -> trunk/1051b93192710d2e4bdf5fecd8e66c61731b55e1 2025-07-17T08:34:15.8727412Z * [new tag] trunk/10cd1de5183e4fcd6e316be497904b9c86d4db67 -> trunk/10cd1de5183e4fcd6e316be497904b9c86d4db67 2025-07-17T08:34:15.8729546Z * [new tag] trunk/10cef1e25d39c1ff1ac93036302ce1b008cd4469 -> trunk/10cef1e25d39c1ff1ac93036302ce1b008cd4469 2025-07-17T08:34:15.8731682Z * [new tag] trunk/10d41c7d20747829a4ee5b994bf466071e4c7e32 -> trunk/10d41c7d20747829a4ee5b994bf466071e4c7e32 2025-07-17T08:34:15.8733869Z * [new tag] trunk/10fb98a004e129ea2a2074cc05e5173c056f728c -> trunk/10fb98a004e129ea2a2074cc05e5173c056f728c 2025-07-17T08:34:15.8735914Z * [new tag] trunk/110ae0f4333b289b1ee798ab93447e6ede16300f -> trunk/110ae0f4333b289b1ee798ab93447e6ede16300f 2025-07-17T08:34:15.8738202Z * [new tag] trunk/1155c53e7d8c25dabc618d7f7b324ee97c89f40e -> trunk/1155c53e7d8c25dabc618d7f7b324ee97c89f40e 2025-07-17T08:34:15.8740194Z * [new tag] trunk/117db5601d78cbc746b35eef71fc815e042e903f -> trunk/117db5601d78cbc746b35eef71fc815e042e903f 2025-07-17T08:34:15.8742213Z * [new tag] trunk/11a86ad2fa3be5cf91a7a2b99c4df44017bc92c7 -> trunk/11a86ad2fa3be5cf91a7a2b99c4df44017bc92c7 2025-07-17T08:34:15.8744328Z * [new tag] trunk/11bb1ece50ade57b963afe29b27cab6a0a56ff7d -> trunk/11bb1ece50ade57b963afe29b27cab6a0a56ff7d 2025-07-17T08:34:15.8746551Z * [new tag] trunk/11bc29856d8180571146209cea987ac9397ab444 -> trunk/11bc29856d8180571146209cea987ac9397ab444 2025-07-17T08:34:15.8748830Z * [new tag] trunk/11d6ad8b2e7359e2f654474f31692ba1aec67980 -> trunk/11d6ad8b2e7359e2f654474f31692ba1aec67980 2025-07-17T08:34:15.8750981Z * [new tag] trunk/11f7e2f1459b09b1ece26ea785b6be77daa36923 -> trunk/11f7e2f1459b09b1ece26ea785b6be77daa36923 2025-07-17T08:34:15.8753256Z * [new tag] trunk/12151c96d9202875638ea2c695d5647c38368c46 -> trunk/12151c96d9202875638ea2c695d5647c38368c46 2025-07-17T08:34:15.8755390Z * [new tag] trunk/127695eb5c973f9fdba24c47b465e30a19292582 -> trunk/127695eb5c973f9fdba24c47b465e30a19292582 2025-07-17T08:34:15.8757943Z * [new tag] trunk/12b02137af5cd6743adfe5a0ffe0d9b453cd013e -> trunk/12b02137af5cd6743adfe5a0ffe0d9b453cd013e 2025-07-17T08:34:15.8760076Z * [new tag] trunk/12cb06e574738a15a59ee9384493682fb5bc6d2b -> trunk/12cb06e574738a15a59ee9384493682fb5bc6d2b 2025-07-17T08:34:15.8762244Z * [new tag] trunk/12f9942b107acc9d7acf9591818c826ef972a0f5 -> trunk/12f9942b107acc9d7acf9591818c826ef972a0f5 2025-07-17T08:34:15.8764392Z * [new tag] trunk/130d4973bd036e539e995dd0bc20080c2570b6da -> trunk/130d4973bd036e539e995dd0bc20080c2570b6da 2025-07-17T08:34:15.8766508Z * [new tag] trunk/132babe7e0b668d31e6efdf339687d48b2ccedc7 -> trunk/132babe7e0b668d31e6efdf339687d48b2ccedc7 2025-07-17T08:34:15.8768571Z * [new tag] trunk/1339e88105cf72586d728d4f7e0d71b81e08d933 -> trunk/1339e88105cf72586d728d4f7e0d71b81e08d933 2025-07-17T08:34:15.8770724Z * [new tag] trunk/134dfb3fe64323d7c615e866c43ea346ad987556 -> trunk/134dfb3fe64323d7c615e866c43ea346ad987556 2025-07-17T08:34:15.8772825Z * [new tag] trunk/1393f71e0714d067ed5ec1f57f135431d20619bc -> trunk/1393f71e0714d067ed5ec1f57f135431d20619bc 2025-07-17T08:34:15.8775032Z * [new tag] trunk/13bf2655c1404aff64e05e50dd7b0ce4c8dc0fcf -> trunk/13bf2655c1404aff64e05e50dd7b0ce4c8dc0fcf 2025-07-17T08:34:15.8777294Z * [new tag] trunk/13ea0f2c0a06101d9aab9ce12c30043be7c92d38 -> trunk/13ea0f2c0a06101d9aab9ce12c30043be7c92d38 2025-07-17T08:34:15.8779376Z * [new tag] trunk/13efb2c858c41141776ad42f7c44f1709948de71 -> trunk/13efb2c858c41141776ad42f7c44f1709948de71 2025-07-17T08:34:15.8781485Z * [new tag] trunk/144965ca9af478515736665b0577cded22fa692e -> trunk/144965ca9af478515736665b0577cded22fa692e 2025-07-17T08:34:15.8783625Z * [new tag] trunk/14ecc0336185f2ca5591858bc74cd4aadf2d1161 -> trunk/14ecc0336185f2ca5591858bc74cd4aadf2d1161 2025-07-17T08:34:15.8785832Z * [new tag] trunk/14f3639e09d692e53c9b9714776e3ca48fed7c4c -> trunk/14f3639e09d692e53c9b9714776e3ca48fed7c4c 2025-07-17T08:34:15.8787989Z * [new tag] trunk/154a39bfbd6e0cc6b7e6f3bce708ab0157ce1c50 -> trunk/154a39bfbd6e0cc6b7e6f3bce708ab0157ce1c50 2025-07-17T08:34:15.8790096Z * [new tag] trunk/156a377f4cf9b5b5255575e26d27f745c111a6ae -> trunk/156a377f4cf9b5b5255575e26d27f745c111a6ae 2025-07-17T08:34:15.8792314Z * [new tag] trunk/156b28e62a225214a5685088a848c5efe6a4b95e -> trunk/156b28e62a225214a5685088a848c5efe6a4b95e 2025-07-17T08:34:15.8794660Z * [new tag] trunk/156bc243f0edbf79d4a24d5affb48198551b58dd -> trunk/156bc243f0edbf79d4a24d5affb48198551b58dd 2025-07-17T08:34:15.8796677Z * [new tag] trunk/157683d8623a683a7e05755176f86a41c7bf518a -> trunk/157683d8623a683a7e05755176f86a41c7bf518a 2025-07-17T08:34:15.8798818Z * [new tag] trunk/1586521461c8dc642735466fc143b7d366a858d0 -> trunk/1586521461c8dc642735466fc143b7d366a858d0 2025-07-17T08:34:15.8800947Z * [new tag] trunk/159a39ad344cca84347e7b3be653ec13834cefdc -> trunk/159a39ad344cca84347e7b3be653ec13834cefdc 2025-07-17T08:34:15.8803073Z * [new tag] trunk/162ca185ff06fc7440e9f52d249e0b465976449a -> trunk/162ca185ff06fc7440e9f52d249e0b465976449a 2025-07-17T08:34:15.8805328Z * [new tag] trunk/163f0d8f2ab0a602a16f606db6d873298088e3a7 -> trunk/163f0d8f2ab0a602a16f606db6d873298088e3a7 2025-07-17T08:34:15.8807624Z * [new tag] trunk/16c3b4143b6fdabbee45ea5a64bb922a5969145a -> trunk/16c3b4143b6fdabbee45ea5a64bb922a5969145a 2025-07-17T08:34:15.8809807Z * [new tag] trunk/172853547ac842bb5ed8bf6e07702c76ff2cf043 -> trunk/172853547ac842bb5ed8bf6e07702c76ff2cf043 2025-07-17T08:34:15.8811841Z * [new tag] trunk/178fe7aa98987111a73534375099f4ad255e8b59 -> trunk/178fe7aa98987111a73534375099f4ad255e8b59 2025-07-17T08:34:15.8813945Z * [new tag] trunk/179dcc10e4e0c742fb7d93b832021d0c177798bf -> trunk/179dcc10e4e0c742fb7d93b832021d0c177798bf 2025-07-17T08:34:15.8816064Z * [new tag] trunk/17b38b850e5362c2f5d7cd75e6552e3d149232f2 -> trunk/17b38b850e5362c2f5d7cd75e6552e3d149232f2 2025-07-17T08:34:15.8818235Z * [new tag] trunk/17eb649d5596c52bae65a069e03b4550155ad57f -> trunk/17eb649d5596c52bae65a069e03b4550155ad57f 2025-07-17T08:34:15.8820364Z * [new tag] trunk/1839e8d04b81ee6eda0cff6fbfc218a7a600f6f7 -> trunk/1839e8d04b81ee6eda0cff6fbfc218a7a600f6f7 2025-07-17T08:34:15.8822653Z * [new tag] trunk/1851f50866a11e7ddede5d07c583e2c5407e7708 -> trunk/1851f50866a11e7ddede5d07c583e2c5407e7708 2025-07-17T08:34:15.8824795Z * [new tag] trunk/187828dcb4145cdfda916ff670207bfb17abdef8 -> trunk/187828dcb4145cdfda916ff670207bfb17abdef8 2025-07-17T08:34:15.8827159Z * [new tag] trunk/18b01afa9ed1dbc696a06de1b69bf5c021c18c10 -> trunk/18b01afa9ed1dbc696a06de1b69bf5c021c18c10 2025-07-17T08:34:15.8829320Z * [new tag] trunk/18bf6addc4741852953ac0b8d555411c347e67aa -> trunk/18bf6addc4741852953ac0b8d555411c347e67aa 2025-07-17T08:34:15.8831413Z * [new tag] trunk/18e4c461fb4e2bbdef889c3b1a2cdb29c0fb31cd -> trunk/18e4c461fb4e2bbdef889c3b1a2cdb29c0fb31cd 2025-07-17T08:34:15.8833552Z * [new tag] trunk/190f76fa313410df8dbb4111c586a516bf55515c -> trunk/190f76fa313410df8dbb4111c586a516bf55515c 2025-07-17T08:34:15.8835644Z * [new tag] trunk/1913c915e0d2b08e7af00889dc50e78d0a9b9198 -> trunk/1913c915e0d2b08e7af00889dc50e78d0a9b9198 2025-07-17T08:34:15.8837866Z * [new tag] trunk/191693ac854de1bb5fcf4280b7a93ab29cc12b5c -> trunk/191693ac854de1bb5fcf4280b7a93ab29cc12b5c 2025-07-17T08:34:15.8839958Z * [new tag] trunk/194539e9c33dc793fe67fbb68c7cee12f399e276 -> trunk/194539e9c33dc793fe67fbb68c7cee12f399e276 2025-07-17T08:34:15.8842112Z * [new tag] trunk/194c221e0abc9daf178e14ca2608c5d6804a3eb7 -> trunk/194c221e0abc9daf178e14ca2608c5d6804a3eb7 2025-07-17T08:34:15.8844312Z * [new tag] trunk/195ef1bce8f17419139fb81406b91f476eba7257 -> trunk/195ef1bce8f17419139fb81406b91f476eba7257 2025-07-17T08:34:15.8846428Z * [new tag] trunk/19625daf889f0a6192a76e200205817e3ee27f26 -> trunk/19625daf889f0a6192a76e200205817e3ee27f26 2025-07-17T08:34:15.8848702Z * [new tag] trunk/196c95d463367f15999c0cddc9eb89031e9988ab -> trunk/196c95d463367f15999c0cddc9eb89031e9988ab 2025-07-17T08:34:15.8850816Z * [new tag] trunk/197c1869f5467b4cfa1e197bbe394be82ba40554 -> trunk/197c1869f5467b4cfa1e197bbe394be82ba40554 2025-07-17T08:34:15.8853121Z * [new tag] trunk/1982ec2d22c5145e4ffeec08064c5ca17e969c25 -> trunk/1982ec2d22c5145e4ffeec08064c5ca17e969c25 2025-07-17T08:34:15.8854982Z * [new tag] trunk/19a01382bc623cf30cdfa7215e47a6b69d8598ff -> trunk/19a01382bc623cf30cdfa7215e47a6b69d8598ff 2025-07-17T08:34:15.8856975Z * [new tag] trunk/19ae5afdaa3a59135e6adbaea197b7a2456f1865 -> trunk/19ae5afdaa3a59135e6adbaea197b7a2456f1865 2025-07-17T08:34:15.8858950Z * [new tag] trunk/19f851ce10b16f0ed11d18d937ca7b32746153b0 -> trunk/19f851ce10b16f0ed11d18d937ca7b32746153b0 2025-07-17T08:34:15.8861191Z * [new tag] trunk/19ffb5e6f7606436249742b0f3efc0bab244dc55 -> trunk/19ffb5e6f7606436249742b0f3efc0bab244dc55 2025-07-17T08:34:15.8863592Z * [new tag] trunk/19ffdf4ea053bc5befe7eaa737fd1fafaaeb34a2 -> trunk/19ffdf4ea053bc5befe7eaa737fd1fafaaeb34a2 2025-07-17T08:34:15.8865801Z * [new tag] trunk/1a195bf7d6155b027f7ca81a97d71a3af34ce11e -> trunk/1a195bf7d6155b027f7ca81a97d71a3af34ce11e 2025-07-17T08:34:15.8867982Z * [new tag] trunk/1a568f4e5d1c0e2e8994d64f01e1e8a3104a1a48 -> trunk/1a568f4e5d1c0e2e8994d64f01e1e8a3104a1a48 2025-07-17T08:34:15.8870050Z * [new tag] trunk/1b032384b186154bb140c7368e2e700cb1419b86 -> trunk/1b032384b186154bb140c7368e2e700cb1419b86 2025-07-17T08:34:15.8872320Z * [new tag] trunk/1b389025ba0cc640e07991314bfba8b6ca385bd2 -> trunk/1b389025ba0cc640e07991314bfba8b6ca385bd2 2025-07-17T08:34:15.8874448Z * [new tag] trunk/1b3d69b59f92383633731aada8383ab88da3ed60 -> trunk/1b3d69b59f92383633731aada8383ab88da3ed60 2025-07-17T08:34:15.8876572Z * [new tag] trunk/1b50c12584909bda00009f4f0fd0d38ec792d019 -> trunk/1b50c12584909bda00009f4f0fd0d38ec792d019 2025-07-17T08:34:15.8878938Z * [new tag] trunk/1b58e7adab91fe20bbfb1568403d72869317e75c -> trunk/1b58e7adab91fe20bbfb1568403d72869317e75c 2025-07-17T08:34:15.8881052Z * [new tag] trunk/1b6772a90f5c334cab7b9d055a7f819ce4c3478c -> trunk/1b6772a90f5c334cab7b9d055a7f819ce4c3478c 2025-07-17T08:34:15.8883222Z * [new tag] trunk/1bb9b1858b84ee07ff1bc33157d4ea7a980bb6b5 -> trunk/1bb9b1858b84ee07ff1bc33157d4ea7a980bb6b5 2025-07-17T08:34:15.8885359Z * [new tag] trunk/1c6057fd179b0373686a790b0a0b7fc68fe7f27d -> trunk/1c6057fd179b0373686a790b0a0b7fc68fe7f27d 2025-07-17T08:34:15.8887577Z * [new tag] trunk/1c8844d9e7b2d72fb80b67ed51df4f6a1295b3b5 -> trunk/1c8844d9e7b2d72fb80b67ed51df4f6a1295b3b5 2025-07-17T08:34:15.8889627Z * [new tag] trunk/1c960c5638b6f20f7a1cbe1e9036c14a4845a195 -> trunk/1c960c5638b6f20f7a1cbe1e9036c14a4845a195 2025-07-17T08:34:15.8891914Z * [new tag] trunk/1cb0597a890bc50b8ed3937d6bdf5a734691bd91 -> trunk/1cb0597a890bc50b8ed3937d6bdf5a734691bd91 2025-07-17T08:34:15.8893977Z * [new tag] trunk/1cc62c2cb91e56ae50494f88f369cd6ec466a118 -> trunk/1cc62c2cb91e56ae50494f88f369cd6ec466a118 2025-07-17T08:34:15.8896208Z * [new tag] trunk/1ccc57e4288e231210c9b2c29943b2752259bf44 -> trunk/1ccc57e4288e231210c9b2c29943b2752259bf44 2025-07-17T08:34:15.8898261Z * [new tag] trunk/1cce73b5f4806f266fbbcf3383057af5f2e3a0aa -> trunk/1cce73b5f4806f266fbbcf3383057af5f2e3a0aa 2025-07-17T08:34:15.8900390Z * [new tag] trunk/1cfdcb975a3b0c685c34c6ae0e378b9bc92e0050 -> trunk/1cfdcb975a3b0c685c34c6ae0e378b9bc92e0050 2025-07-17T08:34:15.8902505Z * [new tag] trunk/1d0f45d5d15389b83312b6942ac2017c8d7ebedc -> trunk/1d0f45d5d15389b83312b6942ac2017c8d7ebedc 2025-07-17T08:34:15.8904594Z * [new tag] trunk/1d584761622ff6e5519c5e3dbbb62a21b89ffe8a -> trunk/1d584761622ff6e5519c5e3dbbb62a21b89ffe8a 2025-07-17T08:34:15.8906732Z * [new tag] trunk/1d993fa3092e4f0b5745f2470024b35cac96da14 -> trunk/1d993fa3092e4f0b5745f2470024b35cac96da14 2025-07-17T08:34:15.8909071Z * [new tag] trunk/1dc1eedd4369f6e6bb79d5315e3ffc1bdc59b709 -> trunk/1dc1eedd4369f6e6bb79d5315e3ffc1bdc59b709 2025-07-17T08:34:15.8910991Z * [new tag] trunk/1dd0b1d12ba48d7879a57391cab6213742dcadb6 -> trunk/1dd0b1d12ba48d7879a57391cab6213742dcadb6 2025-07-17T08:34:15.8913241Z * [new tag] trunk/1e373d02d57ff65c70febc4ec427bf6d1708483e -> trunk/1e373d02d57ff65c70febc4ec427bf6d1708483e 2025-07-17T08:34:15.8915382Z * [new tag] trunk/1e474cc9c8ddab22ed314ad8be641002b0161498 -> trunk/1e474cc9c8ddab22ed314ad8be641002b0161498 2025-07-17T08:34:15.8917507Z * [new tag] trunk/1e4c5b666afe6434cbe3b830aaf70864074d40f5 -> trunk/1e4c5b666afe6434cbe3b830aaf70864074d40f5 2025-07-17T08:34:15.8919580Z * [new tag] trunk/1e4d8b5a4a67220473bf0027c58baaa08a036714 -> trunk/1e4d8b5a4a67220473bf0027c58baaa08a036714 2025-07-17T08:34:15.8921691Z * [new tag] trunk/1e690b6c41677bf1fe3147e3fc011eb9805365f5 -> trunk/1e690b6c41677bf1fe3147e3fc011eb9805365f5 2025-07-17T08:34:15.8923803Z * [new tag] trunk/1e6a653234c299ccecf702233a1eeb7455b1ce77 -> trunk/1e6a653234c299ccecf702233a1eeb7455b1ce77 2025-07-17T08:34:15.8925785Z * [new tag] trunk/1e7e21ec5dd6ea28b27c57fe92bcf31ea5983913 -> trunk/1e7e21ec5dd6ea28b27c57fe92bcf31ea5983913 2025-07-17T08:34:15.8927792Z * [new tag] trunk/1e8e9f745e43fa38bbfc7b67b30bc66c0e7ebbd6 -> trunk/1e8e9f745e43fa38bbfc7b67b30bc66c0e7ebbd6 2025-07-17T08:34:15.8929919Z * [new tag] trunk/1ea9cde598ead20194dbb6c5cb26e74e36e6ad55 -> trunk/1ea9cde598ead20194dbb6c5cb26e74e36e6ad55 2025-07-17T08:34:15.8932003Z * [new tag] trunk/1ed243f01c8efb329055c6124ba0aa5f48747cfe -> trunk/1ed243f01c8efb329055c6124ba0aa5f48747cfe 2025-07-17T08:34:15.8934052Z * [new tag] trunk/1eea2c4fe35ffbdcbfccbeb7ac6c3ec02137385d -> trunk/1eea2c4fe35ffbdcbfccbeb7ac6c3ec02137385d 2025-07-17T08:34:15.8936311Z * [new tag] trunk/1f0eb79e3e91f09f99b53aa4d331eb47a1f66101 -> trunk/1f0eb79e3e91f09f99b53aa4d331eb47a1f66101 2025-07-17T08:34:15.8938641Z * [new tag] trunk/1f1f22991dcca01ed27fad44c21f0827d47bdd6b -> trunk/1f1f22991dcca01ed27fad44c21f0827d47bdd6b 2025-07-17T08:34:15.8940584Z * [new tag] trunk/1f3cc4875cb9d9b579a41b6699f97820c16c3868 -> trunk/1f3cc4875cb9d9b579a41b6699f97820c16c3868 2025-07-17T08:34:15.8942472Z * [new tag] trunk/1f57e0e04da9d334e238cec346f7ae3667bed9d1 -> trunk/1f57e0e04da9d334e238cec346f7ae3667bed9d1 2025-07-17T08:34:15.8944707Z * [new tag] trunk/1fff6356d9f99ec2980052c498dc0b70d513bc6d -> trunk/1fff6356d9f99ec2980052c498dc0b70d513bc6d 2025-07-17T08:34:15.8947122Z * [new tag] trunk/2002e3a311968d18afd0fa5d195943676f134fa2 -> trunk/2002e3a311968d18afd0fa5d195943676f134fa2 2025-07-17T08:34:15.8949427Z * [new tag] trunk/2022588295295fa1fdee7a500adad3bd0c28b9d6 -> trunk/2022588295295fa1fdee7a500adad3bd0c28b9d6 2025-07-17T08:34:15.8951384Z * [new tag] trunk/202d2ae53a493700da813ed7d6055c0b62c7fc6c -> trunk/202d2ae53a493700da813ed7d6055c0b62c7fc6c 2025-07-17T08:34:15.8953493Z * [new tag] trunk/204db27a0c4478c36dbb15fa2d4caed783a90265 -> trunk/204db27a0c4478c36dbb15fa2d4caed783a90265 2025-07-17T08:34:15.8955508Z * [new tag] trunk/205241a0d5149d05e44dc113dc0273e8eceff9f0 -> trunk/205241a0d5149d05e44dc113dc0273e8eceff9f0 2025-07-17T08:34:15.8957664Z * [new tag] trunk/208ec60e72a63f366c757a5bc895089ceb323fcc -> trunk/208ec60e72a63f366c757a5bc895089ceb323fcc 2025-07-17T08:34:15.8959910Z * [new tag] trunk/20a74c370b8427cce369b1abf0c470344d01dc8e -> trunk/20a74c370b8427cce369b1abf0c470344d01dc8e 2025-07-17T08:34:15.8962066Z * [new tag] trunk/20e40492b046b9287726d3ec656117e4dc38f0e2 -> trunk/20e40492b046b9287726d3ec656117e4dc38f0e2 2025-07-17T08:34:15.8964374Z * [new tag] trunk/210632fae1aa2cd994645a70836ff6c602e1beb9 -> trunk/210632fae1aa2cd994645a70836ff6c602e1beb9 2025-07-17T08:34:15.8966434Z * [new tag] trunk/212575f994abbc362da2517510c49f7a34e0a838 -> trunk/212575f994abbc362da2517510c49f7a34e0a838 2025-07-17T08:34:15.8968720Z * [new tag] trunk/214e2959dcdbf91a999d5c0a5d40c91e4442e8c5 -> trunk/214e2959dcdbf91a999d5c0a5d40c91e4442e8c5 2025-07-17T08:34:15.8970979Z * [new tag] trunk/2161be849702330509b452fefc2e103d3be54cd4 -> trunk/2161be849702330509b452fefc2e103d3be54cd4 2025-07-17T08:34:15.8973138Z * [new tag] trunk/216bd6091ec52865052282eced7e6d5d2a4b4fb4 -> trunk/216bd6091ec52865052282eced7e6d5d2a4b4fb4 2025-07-17T08:34:15.8975331Z * [new tag] trunk/2179afd7149c117dace9e552419082094b10a386 -> trunk/2179afd7149c117dace9e552419082094b10a386 2025-07-17T08:34:15.8977485Z * [new tag] trunk/21990fbad97acec769f737b450033774c7be8737 -> trunk/21990fbad97acec769f737b450033774c7be8737 2025-07-17T08:34:15.8979743Z * [new tag] trunk/21b5dc7a6aadb5da6c3f5be61f6204719dae00ca -> trunk/21b5dc7a6aadb5da6c3f5be61f6204719dae00ca 2025-07-17T08:34:15.8981975Z * [new tag] trunk/22edb457c9bf13e0d66631dff4f4ace5c38e0f3b -> trunk/22edb457c9bf13e0d66631dff4f4ace5c38e0f3b 2025-07-17T08:34:15.8984206Z * [new tag] trunk/22f3347fd9482bdf1006c9aa92353b739b7e868b -> trunk/22f3347fd9482bdf1006c9aa92353b739b7e868b 2025-07-17T08:34:15.8986855Z * [new tag] trunk/231eb9902ba78a4ef70203243058f3c7c0ced15d -> trunk/231eb9902ba78a4ef70203243058f3c7c0ced15d 2025-07-17T08:34:15.8989090Z * [new tag] trunk/23491519d288dedb2a54cfad5fef7fcb2ad8eade -> trunk/23491519d288dedb2a54cfad5fef7fcb2ad8eade 2025-07-17T08:34:15.8991290Z * [new tag] trunk/2380115f9738f97cf706affefd647d2cb6dfbb3f -> trunk/2380115f9738f97cf706affefd647d2cb6dfbb3f 2025-07-17T08:34:15.8993558Z * [new tag] trunk/24063ad10994e728087b3958cae9cc27dd376630 -> trunk/24063ad10994e728087b3958cae9cc27dd376630 2025-07-17T08:34:15.8995771Z * [new tag] trunk/242eb19c8383b4b197963a8a564475d52c85ac66 -> trunk/242eb19c8383b4b197963a8a564475d52c85ac66 2025-07-17T08:34:15.8998111Z * [new tag] trunk/243b12e5657a516d6e7b1a0a3f55851ce99bd4cb -> trunk/243b12e5657a516d6e7b1a0a3f55851ce99bd4cb 2025-07-17T08:34:15.9000636Z * [new tag] trunk/247113e03e3fc3e933138b52f856f33a8be33071 -> trunk/247113e03e3fc3e933138b52f856f33a8be33071 2025-07-17T08:34:15.9002855Z * [new tag] trunk/2471cc33550fc00164e2759b1d9928960e9cfaf6 -> trunk/2471cc33550fc00164e2759b1d9928960e9cfaf6 2025-07-17T08:34:15.9005040Z * [new tag] trunk/247f83e0a475bc7eea97c76719f8b5ffa87edced -> trunk/247f83e0a475bc7eea97c76719f8b5ffa87edced 2025-07-17T08:34:15.9007190Z * [new tag] trunk/2481c4b2ea5db7a6d6f60f5d27426000c2779dc8 -> trunk/2481c4b2ea5db7a6d6f60f5d27426000c2779dc8 2025-07-17T08:34:15.9009490Z * [new tag] trunk/24b49b98810bb77f3cfa4c15baa9a15c9be3db61 -> trunk/24b49b98810bb77f3cfa4c15baa9a15c9be3db61 2025-07-17T08:34:15.9011579Z * [new tag] trunk/250ae2531c55dcc50f558ec739941324e3f9a4d4 -> trunk/250ae2531c55dcc50f558ec739941324e3f9a4d4 2025-07-17T08:34:15.9013665Z * [new tag] trunk/25717da8c869a06befed60bc1a0e3dcdac7fd7d9 -> trunk/25717da8c869a06befed60bc1a0e3dcdac7fd7d9 2025-07-17T08:34:15.9015574Z * [new tag] trunk/2578796e235d1d878272084253bee3e2cc02a5b1 -> trunk/2578796e235d1d878272084253bee3e2cc02a5b1 2025-07-17T08:34:15.9017733Z * [new tag] trunk/2585960b47d6429a119665216123cb00435efde9 -> trunk/2585960b47d6429a119665216123cb00435efde9 2025-07-17T08:34:15.9019924Z * [new tag] trunk/2596e3d0617852469241be8777cf46db5c83928c -> trunk/2596e3d0617852469241be8777cf46db5c83928c 2025-07-17T08:34:15.9022112Z * [new tag] trunk/2620361d19f9c4bf37a71c8477823d605191c93a -> trunk/2620361d19f9c4bf37a71c8477823d605191c93a 2025-07-17T08:34:15.9024341Z * [new tag] trunk/2625c70aecc6eced1dbe108279feab7509733bef -> trunk/2625c70aecc6eced1dbe108279feab7509733bef 2025-07-17T08:34:15.9026713Z * [new tag] trunk/262654ee518eb314678f53baf4e133e4767eca3d -> trunk/262654ee518eb314678f53baf4e133e4767eca3d 2025-07-17T08:34:15.9028867Z * [new tag] trunk/26807dcf277feb2d99ab88d7b6da526488baea93 -> trunk/26807dcf277feb2d99ab88d7b6da526488baea93 2025-07-17T08:34:15.9031068Z * [new tag] trunk/26f066bb614c2ee5c0eff598c97855b1df011ef8 -> trunk/26f066bb614c2ee5c0eff598c97855b1df011ef8 2025-07-17T08:34:15.9033300Z * [new tag] trunk/26f7ca39724ff43b7b1e22190feeea67d2212f2d -> trunk/26f7ca39724ff43b7b1e22190feeea67d2212f2d 2025-07-17T08:34:15.9035384Z * [new tag] trunk/271ca679a8a0e40f1ecca49db76b5f8d6a9713d6 -> trunk/271ca679a8a0e40f1ecca49db76b5f8d6a9713d6 2025-07-17T08:34:15.9037362Z * [new tag] trunk/276c790010b015b79a7ca110e28f8e2111cfdb79 -> trunk/276c790010b015b79a7ca110e28f8e2111cfdb79 2025-07-17T08:34:15.9039525Z * [new tag] trunk/2796f31b5e3c90268365e961e2374df3ea93ff53 -> trunk/2796f31b5e3c90268365e961e2374df3ea93ff53 2025-07-17T08:34:15.9041787Z * [new tag] trunk/279cae52e750d5b4e0b49f3f1abca6c2027dd063 -> trunk/279cae52e750d5b4e0b49f3f1abca6c2027dd063 2025-07-17T08:34:15.9044020Z * [new tag] trunk/27c50799c1ca156166bf49ce72a5e97097a44d7d -> trunk/27c50799c1ca156166bf49ce72a5e97097a44d7d 2025-07-17T08:34:15.9046258Z * [new tag] trunk/27df0c56b7c6b75a5f77f5714aeef2ef8f1faa2c -> trunk/27df0c56b7c6b75a5f77f5714aeef2ef8f1faa2c 2025-07-17T08:34:15.9048367Z * [new tag] trunk/2815ade9a80e1f557370e86500b21cbc465a8ffe -> trunk/2815ade9a80e1f557370e86500b21cbc465a8ffe 2025-07-17T08:34:15.9050459Z * [new tag] trunk/2815eea0d0e02eaa0243fa8d1b9d8935b13b3aa5 -> trunk/2815eea0d0e02eaa0243fa8d1b9d8935b13b3aa5 2025-07-17T08:34:15.9052602Z * [new tag] trunk/2860f5c4f5328224ab7998bc90b9fb395b5b068b -> trunk/2860f5c4f5328224ab7998bc90b9fb395b5b068b 2025-07-17T08:34:15.9054654Z * [new tag] trunk/28796f71d04302029290f473a286efc2aba339c2 -> trunk/28796f71d04302029290f473a286efc2aba339c2 2025-07-17T08:34:15.9056662Z * [new tag] trunk/28aae93f24556c6aa8c972c0479b01b908b67fbe -> trunk/28aae93f24556c6aa8c972c0479b01b908b67fbe 2025-07-17T08:34:15.9058677Z * [new tag] trunk/2903e5ad3c4304b90daa6e5ed44a379225571b3a -> trunk/2903e5ad3c4304b90daa6e5ed44a379225571b3a 2025-07-17T08:34:15.9060845Z * [new tag] trunk/2908c10259bac21b00e9b36318e364801e0ae910 -> trunk/2908c10259bac21b00e9b36318e364801e0ae910 2025-07-17T08:34:15.9063088Z * [new tag] trunk/29391c7cf927857123108fa5e9dae6ed3db489f1 -> trunk/29391c7cf927857123108fa5e9dae6ed3db489f1 2025-07-17T08:34:15.9065223Z * [new tag] trunk/297805fd8f59b76a28048a79e8bced2616ed8713 -> trunk/297805fd8f59b76a28048a79e8bced2616ed8713 2025-07-17T08:34:15.9067504Z * [new tag] trunk/297daa1d30c80826b939d8f2dcd07422dec72642 -> trunk/297daa1d30c80826b939d8f2dcd07422dec72642 2025-07-17T08:34:15.9069757Z * [new tag] trunk/29867b211ab74355a0e03bdc9e204d89668c291a -> trunk/29867b211ab74355a0e03bdc9e204d89668c291a 2025-07-17T08:34:15.9072051Z * [new tag] trunk/29e6033ff38f0deb8dde1146a1612e64ed00e3b7 -> trunk/29e6033ff38f0deb8dde1146a1612e64ed00e3b7 2025-07-17T08:34:15.9074128Z * [new tag] trunk/29f76ec0f3eccf619a0aee03e3abbd4914a1b4b2 -> trunk/29f76ec0f3eccf619a0aee03e3abbd4914a1b4b2 2025-07-17T08:34:15.9076253Z * [new tag] trunk/2a3b41cbd077b229bbdd59f6f068c9b643a78b0b -> trunk/2a3b41cbd077b229bbdd59f6f068c9b643a78b0b 2025-07-17T08:34:15.9078322Z * [new tag] trunk/2a4e357192a2b01c9acaeb2168349e5396c9192a -> trunk/2a4e357192a2b01c9acaeb2168349e5396c9192a 2025-07-17T08:34:15.9080675Z * [new tag] trunk/2a8795a981c02f57a57f8699d0e85e42c53b6117 -> trunk/2a8795a981c02f57a57f8699d0e85e42c53b6117 2025-07-17T08:34:15.9082723Z * [new tag] trunk/2aade5ee9fafe52aef4e60624e6c950bc25f8a3e -> trunk/2aade5ee9fafe52aef4e60624e6c950bc25f8a3e 2025-07-17T08:34:15.9084710Z * [new tag] trunk/2ad5c25cfc603c3656e6699d6137419dbb009495 -> trunk/2ad5c25cfc603c3656e6699d6137419dbb009495 2025-07-17T08:34:15.9086893Z * [new tag] trunk/2af7c67e48cad59d648a3e85501f74eade5a3268 -> trunk/2af7c67e48cad59d648a3e85501f74eade5a3268 2025-07-17T08:34:15.9089066Z * [new tag] trunk/2b0f9b1f6172a0d5817c7ac7406200897311da5f -> trunk/2b0f9b1f6172a0d5817c7ac7406200897311da5f 2025-07-17T08:34:15.9091272Z * [new tag] trunk/2b19d85d70ecf1b7121e6d35f2adff06e1eb2e06 -> trunk/2b19d85d70ecf1b7121e6d35f2adff06e1eb2e06 2025-07-17T08:34:15.9093318Z * [new tag] trunk/2b9d638e3333e6e9ae324e1486774e83292e1883 -> trunk/2b9d638e3333e6e9ae324e1486774e83292e1883 2025-07-17T08:34:15.9095409Z * [new tag] trunk/2ba930d4ce04f417195f3061e55479a1a8b16b9d -> trunk/2ba930d4ce04f417195f3061e55479a1a8b16b9d 2025-07-17T08:34:15.9097560Z * [new tag] trunk/2bb33e7a08c4710024b3cb249a13cd2f5d0f6473 -> trunk/2bb33e7a08c4710024b3cb249a13cd2f5d0f6473 2025-07-17T08:34:15.9099712Z * [new tag] trunk/2c0d94a7debe83e96a821548a6e0cb18a0f5cfc3 -> trunk/2c0d94a7debe83e96a821548a6e0cb18a0f5cfc3 2025-07-17T08:34:15.9101983Z * [new tag] trunk/2c1a93a0ae7ebd7963a5931d7b34c885d57844d9 -> trunk/2c1a93a0ae7ebd7963a5931d7b34c885d57844d9 2025-07-17T08:34:15.9104222Z * [new tag] trunk/2c6324a1ebf3e3f6a12be6d18fc0bcdbe00f2a0a -> trunk/2c6324a1ebf3e3f6a12be6d18fc0bcdbe00f2a0a 2025-07-17T08:34:15.9106715Z * [new tag] trunk/2c68c3e8d5e9a235f5861be6486de4959f80c840 -> trunk/2c68c3e8d5e9a235f5861be6486de4959f80c840 2025-07-17T08:34:15.9108929Z * [new tag] trunk/2c76f31221e117b217b8a6a96a5405f626d2218a -> trunk/2c76f31221e117b217b8a6a96a5405f626d2218a 2025-07-17T08:34:15.9111039Z * [new tag] trunk/2cdcd16e836a144f36996104b1340b03041eb07a -> trunk/2cdcd16e836a144f36996104b1340b03041eb07a 2025-07-17T08:34:15.9113171Z * [new tag] trunk/2d3615f577894c7a117a55e85bb8371bb598ec50 -> trunk/2d3615f577894c7a117a55e85bb8371bb598ec50 2025-07-17T08:34:15.9115081Z * [new tag] trunk/2d39a48d524021995269411bd49fe792e59d9f94 -> trunk/2d39a48d524021995269411bd49fe792e59d9f94 2025-07-17T08:34:15.9117116Z * [new tag] trunk/2d7e6c6241971106a56073d7a53c7d1336b11a51 -> trunk/2d7e6c6241971106a56073d7a53c7d1336b11a51 2025-07-17T08:34:15.9119244Z * [new tag] trunk/2d832c9587fd99db295b62d0c9b459d509c19d06 -> trunk/2d832c9587fd99db295b62d0c9b459d509c19d06 2025-07-17T08:34:15.9121510Z * [new tag] trunk/2db1a54465b6121993ad7827f1327ed319c81d70 -> trunk/2db1a54465b6121993ad7827f1327ed319c81d70 2025-07-17T08:34:15.9123687Z * [new tag] trunk/2dc16274519841dc2778cba6962cb0357d1ee8dc -> trunk/2dc16274519841dc2778cba6962cb0357d1ee8dc 2025-07-17T08:34:15.9125737Z * [new tag] trunk/2e0e08588e7cb04f49ee5bbc000b18b72864dfa1 -> trunk/2e0e08588e7cb04f49ee5bbc000b18b72864dfa1 2025-07-17T08:34:15.9127786Z * [new tag] trunk/2e14069081172faf9a51bd34c1e0b85dbf39cb4b -> trunk/2e14069081172faf9a51bd34c1e0b85dbf39cb4b 2025-07-17T08:34:15.9129946Z * [new tag] trunk/2e2ea7290a1cf2da3c3efd6e6ad4836bb94beaeb -> trunk/2e2ea7290a1cf2da3c3efd6e6ad4836bb94beaeb 2025-07-17T08:34:15.9132044Z * [new tag] trunk/2e64e45b0b260e2efc2bde641ec2b3b8ed910194 -> trunk/2e64e45b0b260e2efc2bde641ec2b3b8ed910194 2025-07-17T08:34:15.9134119Z * [new tag] trunk/2e9bd03f6075b9b93b070c3770bf58f60be5508e -> trunk/2e9bd03f6075b9b93b070c3770bf58f60be5508e 2025-07-17T08:34:15.9136246Z * [new tag] trunk/2eb744c08d600e84b167dbda7daa792243a2c235 -> trunk/2eb744c08d600e84b167dbda7daa792243a2c235 2025-07-17T08:34:15.9138442Z * [new tag] trunk/2ee23175d96aa022a9c94209c14bbb7f8e064760 -> trunk/2ee23175d96aa022a9c94209c14bbb7f8e064760 2025-07-17T08:34:15.9140467Z * [new tag] trunk/2efa5eaa652f9a6505635d96178e3f119b20adf7 -> trunk/2efa5eaa652f9a6505635d96178e3f119b20adf7 2025-07-17T08:34:15.9142696Z * [new tag] trunk/2eff14c4455de22628ea599a0d4ba047a7eba6ae -> trunk/2eff14c4455de22628ea599a0d4ba047a7eba6ae 2025-07-17T08:34:15.9144824Z * [new tag] trunk/2f1c5c4131047d25f822ac278bac8c13deff4079 -> trunk/2f1c5c4131047d25f822ac278bac8c13deff4079 2025-07-17T08:34:15.9147226Z * [new tag] trunk/2f94f69b7c83370ef0cc65e3ab96bb5bf11a7b1a -> trunk/2f94f69b7c83370ef0cc65e3ab96bb5bf11a7b1a 2025-07-17T08:34:15.9149343Z * [new tag] trunk/2fde2090d001a9ae63ef41413f11897761eb4de9 -> trunk/2fde2090d001a9ae63ef41413f11897761eb4de9 2025-07-17T08:34:15.9151372Z * [new tag] trunk/2ff3280c77c705e11c5211d4be8fef9853cd0559 -> trunk/2ff3280c77c705e11c5211d4be8fef9853cd0559 2025-07-17T08:34:15.9153555Z * [new tag] trunk/3003c681ef1f8efbfa83470173c62930d195364b -> trunk/3003c681ef1f8efbfa83470173c62930d195364b 2025-07-17T08:34:15.9155774Z * [new tag] trunk/30293b8b5edca86cfc80f24874b0968620a8e72d -> trunk/30293b8b5edca86cfc80f24874b0968620a8e72d 2025-07-17T08:34:15.9157913Z * [new tag] trunk/30387ab2e485384ab2e67084a1e2c5569190ba92 -> trunk/30387ab2e485384ab2e67084a1e2c5569190ba92 2025-07-17T08:34:15.9160178Z * [new tag] trunk/3040ca6d0f8558e39919b14eebeacc34ddf980f5 -> trunk/3040ca6d0f8558e39919b14eebeacc34ddf980f5 2025-07-17T08:34:15.9162520Z * [new tag] trunk/30587195d314eb5eb02ce63f39a9be4c943629ef -> trunk/30587195d314eb5eb02ce63f39a9be4c943629ef 2025-07-17T08:34:15.9164762Z * [new tag] trunk/306dd19216b656467143483395ef582feb5d7d07 -> trunk/306dd19216b656467143483395ef582feb5d7d07 2025-07-17T08:34:15.9166969Z * [new tag] trunk/30a1cc11a47e63c4612ac862f1f2d984ab4dbf24 -> trunk/30a1cc11a47e63c4612ac862f1f2d984ab4dbf24 2025-07-17T08:34:15.9169334Z * [new tag] trunk/30d2648a4ae1585e70189e6fdca6dc9ac3d074ec -> trunk/30d2648a4ae1585e70189e6fdca6dc9ac3d074ec 2025-07-17T08:34:15.9171625Z * [new tag] trunk/30d3cf62fb813819364007e26b3eda84f7cddf3b -> trunk/30d3cf62fb813819364007e26b3eda84f7cddf3b 2025-07-17T08:34:15.9173940Z * [new tag] trunk/3106a33e410db9d1c2b0e47f9e329611d344a827 -> trunk/3106a33e410db9d1c2b0e47f9e329611d344a827 2025-07-17T08:34:15.9176219Z * [new tag] trunk/310e8361c565ca1602e719e4c812dc3931ec84d7 -> trunk/310e8361c565ca1602e719e4c812dc3931ec84d7 2025-07-17T08:34:15.9178426Z * [new tag] trunk/31326a9ad7db53e5bf4e03f8e52b75da944d0298 -> trunk/31326a9ad7db53e5bf4e03f8e52b75da944d0298 2025-07-17T08:34:15.9180619Z * [new tag] trunk/313a6a8ef94d689331b2bd8161f95c23d42eb22d -> trunk/313a6a8ef94d689331b2bd8161f95c23d42eb22d 2025-07-17T08:34:15.9182967Z * [new tag] trunk/3159ee2ad3e586573edb977c4c1745a26d9f1007 -> trunk/3159ee2ad3e586573edb977c4c1745a26d9f1007 2025-07-17T08:34:15.9185061Z * [new tag] trunk/31659964a5b546a84a4c18bb9a0828a0f63b8ccb -> trunk/31659964a5b546a84a4c18bb9a0828a0f63b8ccb 2025-07-17T08:34:15.9187791Z * [new tag] trunk/3173616532ecf0d5c0b78595d6054f1b4bd2bd75 -> trunk/3173616532ecf0d5c0b78595d6054f1b4bd2bd75 2025-07-17T08:34:15.9189858Z * [new tag] trunk/317520bf6ed5c1f2ef324f841e8ec0d8d80cb58a -> trunk/317520bf6ed5c1f2ef324f841e8ec0d8d80cb58a 2025-07-17T08:34:15.9192069Z * [new tag] trunk/317af4c87b7b8b03b8a08a4ead84d4065dd920e0 -> trunk/317af4c87b7b8b03b8a08a4ead84d4065dd920e0 2025-07-17T08:34:15.9194049Z * [new tag] trunk/31e127459789f9d97b3b35001c50e76f1b580cd5 -> trunk/31e127459789f9d97b3b35001c50e76f1b580cd5 2025-07-17T08:34:15.9196466Z * [new tag] trunk/3232b57cd87fbd15c990fbf87d181716a1993a55 -> trunk/3232b57cd87fbd15c990fbf87d181716a1993a55 2025-07-17T08:34:15.9198430Z * [new tag] trunk/326e751d07b92f788bece086227da9288206a27a -> trunk/326e751d07b92f788bece086227da9288206a27a 2025-07-17T08:34:15.9200634Z * [new tag] trunk/32983ea698842a2ca331eabb06acce9c99acb082 -> trunk/32983ea698842a2ca331eabb06acce9c99acb082 2025-07-17T08:34:15.9203032Z * [new tag] trunk/32c1611263ecc8832a8d7494480654ad851911c0 -> trunk/32c1611263ecc8832a8d7494480654ad851911c0 2025-07-17T08:34:15.9205317Z * [new tag] trunk/32eee8ed225d9f10fbbcb38c24b8b44c24c0c97c -> trunk/32eee8ed225d9f10fbbcb38c24b8b44c24c0c97c 2025-07-17T08:34:15.9207960Z * [new tag] trunk/3321acc92e24859dbe2ac6499067d1afde5622c3 -> trunk/3321acc92e24859dbe2ac6499067d1afde5622c3 2025-07-17T08:34:15.9210253Z * [new tag] trunk/333e0e61472c71cb2ad51a59cc5c56f6a47bc747 -> trunk/333e0e61472c71cb2ad51a59cc5c56f6a47bc747 2025-07-17T08:34:15.9212636Z * [new tag] trunk/3341c131b767a4036c152624c1e43baaf24cadf9 -> trunk/3341c131b767a4036c152624c1e43baaf24cadf9 2025-07-17T08:34:15.9214897Z * [new tag] trunk/336bff6d58ceb50b12d9d67764fd9f238bc0adb5 -> trunk/336bff6d58ceb50b12d9d67764fd9f238bc0adb5 2025-07-17T08:34:15.9217137Z * [new tag] trunk/336f1e2d35dfc63925754e884543491cc3f98f8c -> trunk/336f1e2d35dfc63925754e884543491cc3f98f8c 2025-07-17T08:34:15.9219415Z * [new tag] trunk/338a8c7853ab9b3e67dbcb402d30fec066bdd856 -> trunk/338a8c7853ab9b3e67dbcb402d30fec066bdd856 2025-07-17T08:34:15.9221702Z * [new tag] trunk/3404c1f0cfe4c8fd41d718d38316c24a3c0474b0 -> trunk/3404c1f0cfe4c8fd41d718d38316c24a3c0474b0 2025-07-17T08:34:15.9223921Z * [new tag] trunk/344731fb257440ab04f2d634929821be9be3fa80 -> trunk/344731fb257440ab04f2d634929821be9be3fa80 2025-07-17T08:34:15.9227228Z * [new tag] trunk/347ace4c7ac2dbb14799089c30bd01a9ac312791 -> trunk/347ace4c7ac2dbb14799089c30bd01a9ac312791 2025-07-17T08:34:15.9229490Z * [new tag] trunk/348e2a76dfe9c8a1995ab6ce4902dbeba935c984 -> trunk/348e2a76dfe9c8a1995ab6ce4902dbeba935c984 2025-07-17T08:34:15.9231639Z * [new tag] trunk/348fd45065620f20080299774f37f45233ef8f6b -> trunk/348fd45065620f20080299774f37f45233ef8f6b 2025-07-17T08:34:15.9233843Z * [new tag] trunk/3490a4f906aeaa867a390c2e97b357f17b01fcf2 -> trunk/3490a4f906aeaa867a390c2e97b357f17b01fcf2 2025-07-17T08:34:15.9235949Z * [new tag] trunk/34c8033fd3dea404db1706e264761912d095f723 -> trunk/34c8033fd3dea404db1706e264761912d095f723 2025-07-17T08:34:15.9238269Z * [new tag] trunk/34d8e64ef64d88324092a2028884c54c13e086b3 -> trunk/34d8e64ef64d88324092a2028884c54c13e086b3 2025-07-17T08:34:15.9240586Z * [new tag] trunk/35321b2ad6e86a58ca5d5be90ad8edce5945e16c -> trunk/35321b2ad6e86a58ca5d5be90ad8edce5945e16c 2025-07-17T08:34:15.9242719Z * [new tag] trunk/3580b8dde44d8bf4f229537ae9897ddd5e70f5db -> trunk/3580b8dde44d8bf4f229537ae9897ddd5e70f5db 2025-07-17T08:34:15.9244944Z * [new tag] trunk/3584e84c2434a2681b7288bcbd3bdb163c793df5 -> trunk/3584e84c2434a2681b7288bcbd3bdb163c793df5 2025-07-17T08:34:15.9247307Z * [new tag] trunk/3596c0c77f28c74e6cfb50769402ed52dbe10c67 -> trunk/3596c0c77f28c74e6cfb50769402ed52dbe10c67 2025-07-17T08:34:15.9249442Z * [new tag] trunk/359e8f5d691cdbbc54beafea25c06fc0ce321ad6 -> trunk/359e8f5d691cdbbc54beafea25c06fc0ce321ad6 2025-07-17T08:34:15.9251468Z * [new tag] trunk/35d03398e511fa0921c9db928c661dc9531ff2fc -> trunk/35d03398e511fa0921c9db928c661dc9531ff2fc 2025-07-17T08:34:15.9253638Z * [new tag] trunk/35e44067c4d9cc9be2652c0b9098885c5a321029 -> trunk/35e44067c4d9cc9be2652c0b9098885c5a321029 2025-07-17T08:34:15.9255986Z * [new tag] trunk/35e8f2593cdb72036c88a4b8d01487747204ba03 -> trunk/35e8f2593cdb72036c88a4b8d01487747204ba03 2025-07-17T08:34:15.9258072Z * [new tag] trunk/35ecd7c2d44a4e370e21ddab76b6c28266188846 -> trunk/35ecd7c2d44a4e370e21ddab76b6c28266188846 2025-07-17T08:34:15.9260270Z * [new tag] trunk/3608737347bcbb1ec1e23d85a29811abfd75c7a1 -> trunk/3608737347bcbb1ec1e23d85a29811abfd75c7a1 2025-07-17T08:34:15.9262595Z * [new tag] trunk/3644b41a7ccda62c066d525d956a08c9eac686c0 -> trunk/3644b41a7ccda62c066d525d956a08c9eac686c0 2025-07-17T08:34:15.9264939Z * [new tag] trunk/365ce465f393a6426b4ab3148da9a92445bf61d3 -> trunk/365ce465f393a6426b4ab3148da9a92445bf61d3 2025-07-17T08:34:15.9267474Z * [new tag] trunk/3684be056d9af667400ba071a116be8b1112bba8 -> trunk/3684be056d9af667400ba071a116be8b1112bba8 2025-07-17T08:34:15.9269759Z * [new tag] trunk/36a722e20d081c1a5a6df417d0f8333f6c082476 -> trunk/36a722e20d081c1a5a6df417d0f8333f6c082476 2025-07-17T08:34:15.9272043Z * [new tag] trunk/36bf81e36396c5f08eb8cab7f7858555282ca6c2 -> trunk/36bf81e36396c5f08eb8cab7f7858555282ca6c2 2025-07-17T08:34:15.9274253Z * [new tag] trunk/36dd598bdac5c665e46f05d00a38d6863a99615f -> trunk/36dd598bdac5c665e46f05d00a38d6863a99615f 2025-07-17T08:34:15.9276365Z * [new tag] trunk/36f7a027b595083f3a3761b407120b3f1f4e8634 -> trunk/36f7a027b595083f3a3761b407120b3f1f4e8634 2025-07-17T08:34:15.9278619Z * [new tag] trunk/36fd1ac9324429c095f8fbc5f6d2bd4b71f18d61 -> trunk/36fd1ac9324429c095f8fbc5f6d2bd4b71f18d61 2025-07-17T08:34:15.9280848Z * [new tag] trunk/370fc49dde9e8635957d9a910d43154085264225 -> trunk/370fc49dde9e8635957d9a910d43154085264225 2025-07-17T08:34:15.9283260Z * [new tag] trunk/376c16703c7f55a7eab9d5751bfc0e91af26a735 -> trunk/376c16703c7f55a7eab9d5751bfc0e91af26a735 2025-07-17T08:34:15.9285468Z * [new tag] trunk/378c121d5e1c8d2c24124f0c0b6cf08a1341e9d9 -> trunk/378c121d5e1c8d2c24124f0c0b6cf08a1341e9d9 2025-07-17T08:34:15.9287779Z * [new tag] trunk/37ccc532f75d8537c5b5d74b1c92a7f20df96353 -> trunk/37ccc532f75d8537c5b5d74b1c92a7f20df96353 2025-07-17T08:34:15.9290030Z * [new tag] trunk/380e30a723c1fb3530cad12ac44706412df7aa71 -> trunk/380e30a723c1fb3530cad12ac44706412df7aa71 2025-07-17T08:34:15.9292570Z * [new tag] trunk/3819584f12e2a46463a9799612ace1f59d76b9cd -> trunk/3819584f12e2a46463a9799612ace1f59d76b9cd 2025-07-17T08:34:15.9294883Z * [new tag] trunk/382598ef872b2afb9a03f8d88277a6c2edeb507f -> trunk/382598ef872b2afb9a03f8d88277a6c2edeb507f 2025-07-17T08:34:15.9297192Z * [new tag] trunk/382c6190c1329e96e71eef21a19737a3eda0040b -> trunk/382c6190c1329e96e71eef21a19737a3eda0040b 2025-07-17T08:34:15.9299335Z * [new tag] trunk/38371f693b07a485705119407da2e5dc64cec4eb -> trunk/38371f693b07a485705119407da2e5dc64cec4eb 2025-07-17T08:34:15.9301745Z * [new tag] trunk/38410cf9b57079f3360c1e79601973a01cb2588c -> trunk/38410cf9b57079f3360c1e79601973a01cb2588c 2025-07-17T08:34:15.9303763Z * [new tag] trunk/3863bbb55b38985c7d64c8a0be7beb2005a9cc07 -> trunk/3863bbb55b38985c7d64c8a0be7beb2005a9cc07 2025-07-17T08:34:15.9306229Z * [new tag] trunk/386aa7200324519ea9a8eff5eb1b3c0517756d24 -> trunk/386aa7200324519ea9a8eff5eb1b3c0517756d24 2025-07-17T08:34:15.9308517Z * [new tag] trunk/386bc9e2e990a6ac29bb90ba97b71c5b85e11080 -> trunk/386bc9e2e990a6ac29bb90ba97b71c5b85e11080 2025-07-17T08:34:15.9311127Z * [new tag] trunk/38757d94f1b3d65295b5ca2d7527ea0d582a5d3b -> trunk/38757d94f1b3d65295b5ca2d7527ea0d582a5d3b 2025-07-17T08:34:15.9313031Z * [new tag] trunk/38bfd462b8fb035de08a8c0d2b7b13eba78ee870 -> trunk/38bfd462b8fb035de08a8c0d2b7b13eba78ee870 2025-07-17T08:34:15.9315296Z * [new tag] trunk/38c4d05535f4e031a2ad5f39a4a415cc3a919cfb -> trunk/38c4d05535f4e031a2ad5f39a4a415cc3a919cfb 2025-07-17T08:34:15.9317666Z * [new tag] trunk/38e1e5d54ce42d42f8920ff46d9102800b94482d -> trunk/38e1e5d54ce42d42f8920ff46d9102800b94482d 2025-07-17T08:34:15.9319606Z * [new tag] trunk/38e5e81e55fc5d85d6cf8a83c96c88578995e3fe -> trunk/38e5e81e55fc5d85d6cf8a83c96c88578995e3fe 2025-07-17T08:34:15.9321687Z * [new tag] trunk/391473cca0b0fa9667ad54c972e7b63bc589cd6d -> trunk/391473cca0b0fa9667ad54c972e7b63bc589cd6d 2025-07-17T08:34:15.9324115Z * [new tag] trunk/39270430c9e4dc7010a9754f522774d17ae9d578 -> trunk/39270430c9e4dc7010a9754f522774d17ae9d578 2025-07-17T08:34:15.9326219Z * [new tag] trunk/39456edbbad97a09e6507792e1df3ee1f2a16f98 -> trunk/39456edbbad97a09e6507792e1df3ee1f2a16f98 2025-07-17T08:34:15.9328192Z * [new tag] trunk/398fca9dcfcb9f90eae9258d728fab71f5b59db2 -> trunk/398fca9dcfcb9f90eae9258d728fab71f5b59db2 2025-07-17T08:34:15.9330301Z * [new tag] trunk/39a8f66d5939e892bcb07ef97462af47d3201491 -> trunk/39a8f66d5939e892bcb07ef97462af47d3201491 2025-07-17T08:34:15.9332236Z * [new tag] trunk/39ac189808c61588f3594dbc2fc1d69bb6194c47 -> trunk/39ac189808c61588f3594dbc2fc1d69bb6194c47 2025-07-17T08:34:15.9334193Z * [new tag] trunk/39b71d11fc2dd9b4da6d23a34eb29aefbb1df672 -> trunk/39b71d11fc2dd9b4da6d23a34eb29aefbb1df672 2025-07-17T08:34:15.9336335Z * [new tag] trunk/39c605e8b3c13e7f6b5b1bbbe3f4060e24b2a3e4 -> trunk/39c605e8b3c13e7f6b5b1bbbe3f4060e24b2a3e4 2025-07-17T08:34:15.9338412Z * [new tag] trunk/3a43dba21ff5d01bb8b259af3839c90e447c6ec0 -> trunk/3a43dba21ff5d01bb8b259af3839c90e447c6ec0 2025-07-17T08:34:15.9340550Z * [new tag] trunk/3a5677a380c79810cc370dce6ef79b0871ddcf8c -> trunk/3a5677a380c79810cc370dce6ef79b0871ddcf8c 2025-07-17T08:34:15.9342515Z * [new tag] trunk/3a7ff829c597ef26a271d52ae2a3c8354b2f447e -> trunk/3a7ff829c597ef26a271d52ae2a3c8354b2f447e 2025-07-17T08:34:15.9344687Z * [new tag] trunk/3b4b5f8d474a9c664f622012c6a61414ea7799a9 -> trunk/3b4b5f8d474a9c664f622012c6a61414ea7799a9 2025-07-17T08:34:15.9347104Z * [new tag] trunk/3b6569b1ef4b9ff25f5b75fe0a216d6d084d573f -> trunk/3b6569b1ef4b9ff25f5b75fe0a216d6d084d573f 2025-07-17T08:34:15.9349235Z * [new tag] trunk/3b7c5e6fa5c0be64ddde3a8edc5bcdc10390f1e3 -> trunk/3b7c5e6fa5c0be64ddde3a8edc5bcdc10390f1e3 2025-07-17T08:34:15.9351186Z * [new tag] trunk/3bc6bdc8660c052d932f550d5734da6f801c2630 -> trunk/3bc6bdc8660c052d932f550d5734da6f801c2630 2025-07-17T08:34:15.9353459Z * [new tag] trunk/3bdd5ae334b85a114a2b62fa17dcf204413eda32 -> trunk/3bdd5ae334b85a114a2b62fa17dcf204413eda32 2025-07-17T08:34:15.9355504Z * [new tag] trunk/3beb915004f4e26b1e7c5e7692e6e8ca9b75de46 -> trunk/3beb915004f4e26b1e7c5e7692e6e8ca9b75de46 2025-07-17T08:34:15.9357647Z * [new tag] trunk/3bec588bf5c4eda9a4d42ae4c25e6f87af7f078c -> trunk/3bec588bf5c4eda9a4d42ae4c25e6f87af7f078c 2025-07-17T08:34:15.9359659Z * [new tag] trunk/3c2324c64ac6b7497d630788a66c82705bbb044e -> trunk/3c2324c64ac6b7497d630788a66c82705bbb044e 2025-07-17T08:34:15.9361798Z * [new tag] trunk/3c7079959c8e61d7acb4f704a0ecf74c61425c2e -> trunk/3c7079959c8e61d7acb4f704a0ecf74c61425c2e 2025-07-17T08:34:15.9363857Z * [new tag] trunk/3c72b9fd8feed4588a040bc681ffe83cc7acd26d -> trunk/3c72b9fd8feed4588a040bc681ffe83cc7acd26d 2025-07-17T08:34:15.9365916Z * [new tag] trunk/3cb11877aa30c04be7ffa9b4ca1722f1270a5828 -> trunk/3cb11877aa30c04be7ffa9b4ca1722f1270a5828 2025-07-17T08:34:15.9367853Z * [new tag] trunk/3cbae6dde8f2a9c3b4a3e4e079e97307c3aa52d8 -> trunk/3cbae6dde8f2a9c3b4a3e4e079e97307c3aa52d8 2025-07-17T08:34:15.9369989Z * [new tag] trunk/3d06ff82a84a118f0ed246864d4fc01ac4726328 -> trunk/3d06ff82a84a118f0ed246864d4fc01ac4726328 2025-07-17T08:34:15.9372205Z * [new tag] trunk/3d595fd5595f38bb5ed3d390dc50e1715e173ad6 -> trunk/3d595fd5595f38bb5ed3d390dc50e1715e173ad6 2025-07-17T08:34:15.9374244Z * [new tag] trunk/3d82a1dfb59fa5e248f7499a7ecdf784d4f61c0e -> trunk/3d82a1dfb59fa5e248f7499a7ecdf784d4f61c0e 2025-07-17T08:34:15.9376387Z * [new tag] trunk/3dabc351bb5581f69825eee6b24fbac9f9260241 -> trunk/3dabc351bb5581f69825eee6b24fbac9f9260241 2025-07-17T08:34:15.9378448Z * [new tag] trunk/3dd872e6d53560933d8d7fc11357617746d37168 -> trunk/3dd872e6d53560933d8d7fc11357617746d37168 2025-07-17T08:34:15.9380722Z * [new tag] trunk/3dda80e990121eaf156014fffe6e2a4602c8b195 -> trunk/3dda80e990121eaf156014fffe6e2a4602c8b195 2025-07-17T08:34:15.9383061Z * [new tag] trunk/3df6360e8c956edd25453b21f6b7f56e0366dcb4 -> trunk/3df6360e8c956edd25453b21f6b7f56e0366dcb4 2025-07-17T08:34:15.9385255Z * [new tag] trunk/3e131f7779af03b462f0598afb2569092b57c840 -> trunk/3e131f7779af03b462f0598afb2569092b57c840 2025-07-17T08:34:15.9387742Z * [new tag] trunk/3e38feb05fffdf5b181a1f4c7a6f43b00ef1c526 -> trunk/3e38feb05fffdf5b181a1f4c7a6f43b00ef1c526 2025-07-17T08:34:15.9389920Z * [new tag] trunk/3e56a9cdfb98a9b06568ee54e3157c800d98a17e -> trunk/3e56a9cdfb98a9b06568ee54e3157c800d98a17e 2025-07-17T08:34:15.9392067Z * [new tag] trunk/3eb7084f7a3657f4ba6626aca63721a0020f1bd7 -> trunk/3eb7084f7a3657f4ba6626aca63721a0020f1bd7 2025-07-17T08:34:15.9394102Z * [new tag] trunk/3ed4384f5b4bb7ae7d12298632a258385a51446e -> trunk/3ed4384f5b4bb7ae7d12298632a258385a51446e 2025-07-17T08:34:15.9396246Z * [new tag] trunk/3ee75b7eacef6758db602e87287ef9574609b327 -> trunk/3ee75b7eacef6758db602e87287ef9574609b327 2025-07-17T08:34:15.9398427Z * [new tag] trunk/3ee8828c87ce6186607c3d3ab3852518fca49228 -> trunk/3ee8828c87ce6186607c3d3ab3852518fca49228 2025-07-17T08:34:15.9400575Z * [new tag] trunk/3efb22e09111b92bedb01b2a8385c789fe69090a -> trunk/3efb22e09111b92bedb01b2a8385c789fe69090a 2025-07-17T08:34:15.9402831Z * [new tag] trunk/3effe0c293219b00a0eae7e139fe2d9aed84bc03 -> trunk/3effe0c293219b00a0eae7e139fe2d9aed84bc03 2025-07-17T08:34:15.9404991Z * [new tag] trunk/3f29642ecf039129032cc61c986d7b62807163c0 -> trunk/3f29642ecf039129032cc61c986d7b62807163c0 2025-07-17T08:34:15.9407108Z * [new tag] trunk/3f569f9af77d51d0328f16434e64252756681daa -> trunk/3f569f9af77d51d0328f16434e64252756681daa 2025-07-17T08:34:15.9409283Z * [new tag] trunk/3f65e38b73cb8f0d0b4fb2e9a6671085d371ec40 -> trunk/3f65e38b73cb8f0d0b4fb2e9a6671085d371ec40 2025-07-17T08:34:15.9411411Z * [new tag] trunk/3f69e3b3a07efd5e9aa86d37773bf2a24dc0ea70 -> trunk/3f69e3b3a07efd5e9aa86d37773bf2a24dc0ea70 2025-07-17T08:34:15.9413539Z * [new tag] trunk/3f83e3eeca0645f4b2cd16fa7d5a591e9cf810d4 -> trunk/3f83e3eeca0645f4b2cd16fa7d5a591e9cf810d4 2025-07-17T08:34:15.9415838Z * [new tag] trunk/3f920f3d8f5bd15d2222758f21f9a5d36e4dad1f -> trunk/3f920f3d8f5bd15d2222758f21f9a5d36e4dad1f 2025-07-17T08:34:15.9417883Z * [new tag] trunk/3fd84a8592a2a87d04f0e3f236a92605e2be12df -> trunk/3fd84a8592a2a87d04f0e3f236a92605e2be12df 2025-07-17T08:34:15.9420110Z * [new tag] trunk/400f439670ff8859c17a870eb83dc5cae5a9c2e4 -> trunk/400f439670ff8859c17a870eb83dc5cae5a9c2e4 2025-07-17T08:34:15.9422367Z * [new tag] trunk/402ae09e41005ebff686e97e2f120a6b79e2afb7 -> trunk/402ae09e41005ebff686e97e2f120a6b79e2afb7 2025-07-17T08:34:15.9424728Z * [new tag] trunk/404008e3efdabeaf5b140a3aff77131461c33a0a -> trunk/404008e3efdabeaf5b140a3aff77131461c33a0a 2025-07-17T08:34:15.9428446Z * [new tag] trunk/4048a144abf82041f9b6653d9d4aa600a6a5409a -> trunk/4048a144abf82041f9b6653d9d4aa600a6a5409a 2025-07-17T08:34:15.9430854Z * [new tag] trunk/408d9884b07cf7268961bae7138a6436916d4a43 -> trunk/408d9884b07cf7268961bae7138a6436916d4a43 2025-07-17T08:34:15.9433252Z * [new tag] trunk/40a785103cf94a1dbc3e0e43d1ed6c41fb60bedb -> trunk/40a785103cf94a1dbc3e0e43d1ed6c41fb60bedb 2025-07-17T08:34:15.9435246Z * [new tag] trunk/40d02eb481670325ba80dcb42b40c3ebb347f1c7 -> trunk/40d02eb481670325ba80dcb42b40c3ebb347f1c7 2025-07-17T08:34:15.9437343Z * [new tag] trunk/40e39ae21f15f200828ab32826bed1a4b62532ba -> trunk/40e39ae21f15f200828ab32826bed1a4b62532ba 2025-07-17T08:34:15.9439579Z * [new tag] trunk/40fefe2871a5561b67ec268bab19db60e733f2d4 -> trunk/40fefe2871a5561b67ec268bab19db60e733f2d4 2025-07-17T08:34:15.9441672Z * [new tag] trunk/414ad470450c654d97e73bef704a7b596b5b4cbc -> trunk/414ad470450c654d97e73bef704a7b596b5b4cbc 2025-07-17T08:34:15.9443828Z * [new tag] trunk/415dfabe9b569b71098a2f874f3fc67ad2a4fc2e -> trunk/415dfabe9b569b71098a2f874f3fc67ad2a4fc2e 2025-07-17T08:34:15.9445885Z * [new tag] trunk/4162c0f70297818abd70f2fe7424dcd3cc9b2543 -> trunk/4162c0f70297818abd70f2fe7424dcd3cc9b2543 2025-07-17T08:34:15.9447903Z * [new tag] trunk/41910d7a94d2f3ffe06f7c0c30971c71b9c3b09e -> trunk/41910d7a94d2f3ffe06f7c0c30971c71b9c3b09e 2025-07-17T08:34:15.9450036Z * [new tag] trunk/41971335c98b0881e0784085096eceace575d563 -> trunk/41971335c98b0881e0784085096eceace575d563 2025-07-17T08:34:15.9452181Z * [new tag] trunk/41e8b826d07bbf707bc2c64a78b4dc15e8f3c358 -> trunk/41e8b826d07bbf707bc2c64a78b4dc15e8f3c358 2025-07-17T08:34:15.9454394Z * [new tag] trunk/41f6acef83d280a18909d58f8442e145c9e7ea6f -> trunk/41f6acef83d280a18909d58f8442e145c9e7ea6f 2025-07-17T08:34:15.9456549Z * [new tag] trunk/42015db6a9602f40250f9afc18a3fbad4ca4ef39 -> trunk/42015db6a9602f40250f9afc18a3fbad4ca4ef39 2025-07-17T08:34:15.9458705Z * [new tag] trunk/4237ee3c33b0edb01db96d8c1ecf8f5d2cf184bc -> trunk/4237ee3c33b0edb01db96d8c1ecf8f5d2cf184bc 2025-07-17T08:34:15.9460821Z * [new tag] trunk/4283d96bcdf9aaa4289985267186d74ba6534ee5 -> trunk/4283d96bcdf9aaa4289985267186d74ba6534ee5 2025-07-17T08:34:15.9462902Z * [new tag] trunk/42b48ee67229286127390000f103a11dfc8901f5 -> trunk/42b48ee67229286127390000f103a11dfc8901f5 2025-07-17T08:34:15.9464989Z * [new tag] trunk/42ff6a4a5c4e0d77bd18fcc5426622f1b8f20add -> trunk/42ff6a4a5c4e0d77bd18fcc5426622f1b8f20add 2025-07-17T08:34:15.9467187Z * [new tag] trunk/430cc1c636380a7d50652df646274f7008a76747 -> trunk/430cc1c636380a7d50652df646274f7008a76747 2025-07-17T08:34:15.9469304Z * [new tag] trunk/4311aea5e7568c9956a9f0d694f1980c387b6a37 -> trunk/4311aea5e7568c9956a9f0d694f1980c387b6a37 2025-07-17T08:34:15.9471284Z * [new tag] trunk/433a2471023f77a4e62e880ef59f22caf939c227 -> trunk/433a2471023f77a4e62e880ef59f22caf939c227 2025-07-17T08:34:15.9473476Z * [new tag] trunk/43523bf1682c2926a84a1f65f00fabd3d34db4f2 -> trunk/43523bf1682c2926a84a1f65f00fabd3d34db4f2 2025-07-17T08:34:15.9475563Z * [new tag] trunk/43a09189c68fe02bd9d8433c4a144ffc9bbf895c -> trunk/43a09189c68fe02bd9d8433c4a144ffc9bbf895c 2025-07-17T08:34:15.9477624Z * [new tag] trunk/43f72163275ce1503b1de1480642c61d20e6158d -> trunk/43f72163275ce1503b1de1480642c61d20e6158d 2025-07-17T08:34:15.9479826Z * [new tag] trunk/442aca44d603ae6c2b7d2aa2190cc91f970c4202 -> trunk/442aca44d603ae6c2b7d2aa2190cc91f970c4202 2025-07-17T08:34:15.9481917Z * [new tag] trunk/44303caabfa0bdc5688d693d491ba7c3fdb40f3a -> trunk/44303caabfa0bdc5688d693d491ba7c3fdb40f3a 2025-07-17T08:34:15.9484080Z * [new tag] trunk/443b5b43c3d40b89bbb5db70f3f05a44b2a4ec66 -> trunk/443b5b43c3d40b89bbb5db70f3f05a44b2a4ec66 2025-07-17T08:34:15.9486111Z * [new tag] trunk/4486a6dbfd65ef490cfe73e0630929e85f61ee16 -> trunk/4486a6dbfd65ef490cfe73e0630929e85f61ee16 2025-07-17T08:34:15.9488215Z * [new tag] trunk/4491326fb0c0e67eca1598ae33c41cdfced2cd33 -> trunk/4491326fb0c0e67eca1598ae33c41cdfced2cd33 2025-07-17T08:34:15.9490132Z * [new tag] trunk/44a5f93462bd4aa4f36ae2573685ed7ba458c4c9 -> trunk/44a5f93462bd4aa4f36ae2573685ed7ba458c4c9 2025-07-17T08:34:15.9492301Z * [new tag] trunk/44d0800d60e78fef8ab332e307c3134e3c276ba4 -> trunk/44d0800d60e78fef8ab332e307c3134e3c276ba4 2025-07-17T08:34:15.9494378Z * [new tag] trunk/44df7cf28dd37a4d900eb6b2f78dabf72b209d9b -> trunk/44df7cf28dd37a4d900eb6b2f78dabf72b209d9b 2025-07-17T08:34:15.9496433Z * [new tag] trunk/44f5b9312290866584088e2e42228c484f669faf -> trunk/44f5b9312290866584088e2e42228c484f669faf 2025-07-17T08:34:15.9498405Z * [new tag] trunk/4500a4aa50141ed30e093ef8491b30d1d1287348 -> trunk/4500a4aa50141ed30e093ef8491b30d1d1287348 2025-07-17T08:34:15.9500490Z * [new tag] trunk/451b525bf0cb8840adbde1b6280c7b199f733ced -> trunk/451b525bf0cb8840adbde1b6280c7b199f733ced 2025-07-17T08:34:15.9502614Z * [new tag] trunk/45382b284d03015f3e6eb83a959f0e54a3ebd688 -> trunk/45382b284d03015f3e6eb83a959f0e54a3ebd688 2025-07-17T08:34:15.9504750Z * [new tag] trunk/453bc9fbdfb97fef925477cbbf4948e93fd22756 -> trunk/453bc9fbdfb97fef925477cbbf4948e93fd22756 2025-07-17T08:34:15.9506920Z * [new tag] trunk/45596ec58f5bc5489911ee932c2a55a7ff40d0a0 -> trunk/45596ec58f5bc5489911ee932c2a55a7ff40d0a0 2025-07-17T08:34:15.9509080Z * [new tag] trunk/455dfd258980294f0745bd90aee12a323e37224d -> trunk/455dfd258980294f0745bd90aee12a323e37224d 2025-07-17T08:34:15.9511104Z * [new tag] trunk/456b7451c78096fd512c60e26cc618386a251bc7 -> trunk/456b7451c78096fd512c60e26cc618386a251bc7 2025-07-17T08:34:15.9513184Z * [new tag] trunk/456f40cb09c6e5e0cd268d1601b89fc5ac1987ea -> trunk/456f40cb09c6e5e0cd268d1601b89fc5ac1987ea 2025-07-17T08:34:15.9515380Z * [new tag] trunk/4574b39aa45f0250cba04aa3cb053a686c4888b2 -> trunk/4574b39aa45f0250cba04aa3cb053a686c4888b2 2025-07-17T08:34:15.9517476Z * [new tag] trunk/457dd79927db6ae7f1c53f36768b7ac93b61e507 -> trunk/457dd79927db6ae7f1c53f36768b7ac93b61e507 2025-07-17T08:34:15.9519567Z * [new tag] trunk/4585c33e74079af8e9067bf39970b93c3f13629f -> trunk/4585c33e74079af8e9067bf39970b93c3f13629f 2025-07-17T08:34:15.9521771Z * [new tag] trunk/458cc7213baf01ab742a2250c7a31f7796fcb1ef -> trunk/458cc7213baf01ab742a2250c7a31f7796fcb1ef 2025-07-17T08:34:15.9523830Z * [new tag] trunk/45c5a232373cac1b1524259f20758bfb56dde5f2 -> trunk/45c5a232373cac1b1524259f20758bfb56dde5f2 2025-07-17T08:34:15.9526008Z * [new tag] trunk/4609699bfd440d1fe603c5ad9a942e45410094c1 -> trunk/4609699bfd440d1fe603c5ad9a942e45410094c1 2025-07-17T08:34:15.9528134Z * [new tag] trunk/4628f1b7a9313df98346d582d9a789661a823e27 -> trunk/4628f1b7a9313df98346d582d9a789661a823e27 2025-07-17T08:34:15.9530244Z * [new tag] trunk/463fe36532bc165b46c12cef4948f039434e0924 -> trunk/463fe36532bc165b46c12cef4948f039434e0924 2025-07-17T08:34:15.9532373Z * [new tag] trunk/4657a84bc55b6ce12f21706de2b90e1d43784f57 -> trunk/4657a84bc55b6ce12f21706de2b90e1d43784f57 2025-07-17T08:34:15.9534449Z * [new tag] trunk/46915b13614dbac90724d0f1802b8e0db037c9e4 -> trunk/46915b13614dbac90724d0f1802b8e0db037c9e4 2025-07-17T08:34:15.9536542Z * [new tag] trunk/473208cb18d543e8f968918a6b3c9defa8a4ae10 -> trunk/473208cb18d543e8f968918a6b3c9defa8a4ae10 2025-07-17T08:34:15.9538590Z * [new tag] trunk/476874b37fff42a46d25dfac720ef4c71ec74fe0 -> trunk/476874b37fff42a46d25dfac720ef4c71ec74fe0 2025-07-17T08:34:15.9540885Z * [new tag] trunk/4781b0ee6057fdf2a82d2f9ed30299f219267a71 -> trunk/4781b0ee6057fdf2a82d2f9ed30299f219267a71 2025-07-17T08:34:15.9543051Z * [new tag] trunk/4781d72faa6b72bf96fde9bedfca06a5285eebfb -> trunk/4781d72faa6b72bf96fde9bedfca06a5285eebfb 2025-07-17T08:34:15.9545247Z * [new tag] trunk/47c8810b5275179833d6b33ca3d70922f485272c -> trunk/47c8810b5275179833d6b33ca3d70922f485272c 2025-07-17T08:34:15.9548357Z * [new tag] trunk/47f10d0ad0dda281c886ff08ac2f938207027316 -> trunk/47f10d0ad0dda281c886ff08ac2f938207027316 2025-07-17T08:34:15.9550387Z * [new tag] trunk/4805a6ead6f1e7f32351056e2602be4e908f69b7 -> trunk/4805a6ead6f1e7f32351056e2602be4e908f69b7 2025-07-17T08:34:15.9552487Z * [new tag] trunk/48315181c75e43cab5957197d42e053d66b3fe1c -> trunk/48315181c75e43cab5957197d42e053d66b3fe1c 2025-07-17T08:34:15.9554861Z * [new tag] trunk/4851863e3f98e1ffba1d5801f3533031ef4f905d -> trunk/4851863e3f98e1ffba1d5801f3533031ef4f905d 2025-07-17T08:34:15.9557008Z * [new tag] trunk/48560eef80e97e855cbb8e2814acefe8f5cc6fbd -> trunk/48560eef80e97e855cbb8e2814acefe8f5cc6fbd 2025-07-17T08:34:15.9559134Z * [new tag] trunk/4886ba64dceb24b8a0444e574abb907a18fdeee9 -> trunk/4886ba64dceb24b8a0444e574abb907a18fdeee9 2025-07-17T08:34:15.9561416Z * [new tag] trunk/48921721d8ed32a2c7f80605b434f9fcb2f948b4 -> trunk/48921721d8ed32a2c7f80605b434f9fcb2f948b4 2025-07-17T08:34:15.9563543Z * [new tag] trunk/48de3da2539cecaee14af8e3841c133c9c0c0f1c -> trunk/48de3da2539cecaee14af8e3841c133c9c0c0f1c 2025-07-17T08:34:15.9565550Z * [new tag] trunk/48e7b62d3abda4a3eac2f538c9f35e56093c4ece -> trunk/48e7b62d3abda4a3eac2f538c9f35e56093c4ece 2025-07-17T08:34:15.9567741Z * [new tag] trunk/4918502d2e685270cea9f1fd733a3414ac1ca5d7 -> trunk/4918502d2e685270cea9f1fd733a3414ac1ca5d7 2025-07-17T08:34:15.9570015Z * [new tag] trunk/493bd625e252dea02e871346beaa49745b4b2663 -> trunk/493bd625e252dea02e871346beaa49745b4b2663 2025-07-17T08:34:15.9572189Z * [new tag] trunk/493f42a5417a8ac33d1070478d1869ea4f207b5b -> trunk/493f42a5417a8ac33d1070478d1869ea4f207b5b 2025-07-17T08:34:15.9574261Z * [new tag] trunk/495c317005ad656dee752b6ad7bd5541b04e388e -> trunk/495c317005ad656dee752b6ad7bd5541b04e388e 2025-07-17T08:34:15.9576444Z * [new tag] trunk/49888e6be0dcc00ba546746425893bb17c287248 -> trunk/49888e6be0dcc00ba546746425893bb17c287248 2025-07-17T08:34:15.9578587Z * [new tag] trunk/49ee1e7106db00778345efd7c70cc12d2ca6a91a -> trunk/49ee1e7106db00778345efd7c70cc12d2ca6a91a 2025-07-17T08:34:15.9580634Z * [new tag] trunk/4a26bb8a12ba5cb437ed5b7f035b5b533e07549e -> trunk/4a26bb8a12ba5cb437ed5b7f035b5b533e07549e 2025-07-17T08:34:15.9582903Z * [new tag] trunk/4a4cac0cefea3661cc69cfdafdba64832ee0841a -> trunk/4a4cac0cefea3661cc69cfdafdba64832ee0841a 2025-07-17T08:34:15.9584971Z * [new tag] trunk/4a80ddfbe70bf6b75acc3177e5d2095b285da841 -> trunk/4a80ddfbe70bf6b75acc3177e5d2095b285da841 2025-07-17T08:34:15.9587299Z * [new tag] trunk/4a8f5e752beb5a6809ba866c83f32dd464a47bfd -> trunk/4a8f5e752beb5a6809ba866c83f32dd464a47bfd 2025-07-17T08:34:15.9589455Z * [new tag] trunk/4a954fc1857ff41be604e8cae68908d41124b419 -> trunk/4a954fc1857ff41be604e8cae68908d41124b419 2025-07-17T08:34:15.9591739Z * [new tag] trunk/4ab4d29cbee1f90daf14444d5ca3a53653c7784d -> trunk/4ab4d29cbee1f90daf14444d5ca3a53653c7784d 2025-07-17T08:34:15.9593949Z * [new tag] trunk/4b11428cb5b3d97f3068a2dc4c55cee6ddd41979 -> trunk/4b11428cb5b3d97f3068a2dc4c55cee6ddd41979 2025-07-17T08:34:15.9596131Z * [new tag] trunk/4b4c2a7b1dfd88313801878c5b4e3855fe5232df -> trunk/4b4c2a7b1dfd88313801878c5b4e3855fe5232df 2025-07-17T08:34:15.9598376Z * [new tag] trunk/4b55871e06d6bad54eac45e45a9af615d758a39f -> trunk/4b55871e06d6bad54eac45e45a9af615d758a39f 2025-07-17T08:34:15.9600533Z * [new tag] trunk/4b6cbf528b8f7b95e02e3a0233945e0bb8b00f44 -> trunk/4b6cbf528b8f7b95e02e3a0233945e0bb8b00f44 2025-07-17T08:34:15.9602983Z * [new tag] trunk/4b9a6f7211123511e856ac8c8524bc332a741241 -> trunk/4b9a6f7211123511e856ac8c8524bc332a741241 2025-07-17T08:34:15.9605123Z * [new tag] trunk/4bb936d8b77c959efbff0bdb68f63e5a5faf60a9 -> trunk/4bb936d8b77c959efbff0bdb68f63e5a5faf60a9 2025-07-17T08:34:15.9607218Z * [new tag] trunk/4bc3e4b497948af0a48d08b427339efc8960fedb -> trunk/4bc3e4b497948af0a48d08b427339efc8960fedb 2025-07-17T08:34:15.9609323Z * [new tag] trunk/4bd18e31e5a38d0e84ce915b1fa124058c6373fa -> trunk/4bd18e31e5a38d0e84ce915b1fa124058c6373fa 2025-07-17T08:34:15.9611505Z * [new tag] trunk/4c0091fda65b714fa73671a15e379f814af153e0 -> trunk/4c0091fda65b714fa73671a15e379f814af153e0 2025-07-17T08:34:15.9613990Z * [new tag] trunk/4c0aa37dda605e7cf3372c71940d7e3a93ecef8d -> trunk/4c0aa37dda605e7cf3372c71940d7e3a93ecef8d 2025-07-17T08:34:15.9616094Z * [new tag] trunk/4c3da611c2a80fa5073b164974d7edc0577b9ca7 -> trunk/4c3da611c2a80fa5073b164974d7edc0577b9ca7 2025-07-17T08:34:15.9618204Z * [new tag] trunk/4c59edf0c5838087cbe09c67c6e0e776467a5f2d -> trunk/4c59edf0c5838087cbe09c67c6e0e776467a5f2d 2025-07-17T08:34:15.9620157Z * [new tag] trunk/4c8eb65efb147cd263fc02f5588683f530363a0f -> trunk/4c8eb65efb147cd263fc02f5588683f530363a0f 2025-07-17T08:34:15.9622207Z * [new tag] trunk/4cbbc8b4583e9dac4af7e8d1d8535546de21178c -> trunk/4cbbc8b4583e9dac4af7e8d1d8535546de21178c 2025-07-17T08:34:15.9624258Z * [new tag] trunk/4cc13c4af6dbd5aa36545ef577d0bee517cf8c57 -> trunk/4cc13c4af6dbd5aa36545ef577d0bee517cf8c57 2025-07-17T08:34:15.9626236Z * [new tag] trunk/4cd6e96bf0d13f168772f6ad44267b1ac7632a97 -> trunk/4cd6e96bf0d13f168772f6ad44267b1ac7632a97 2025-07-17T08:34:15.9628328Z * [new tag] trunk/4cdbdcdbcf2b7f7a52a5c61cb35064f7b4aa2e44 -> trunk/4cdbdcdbcf2b7f7a52a5c61cb35064f7b4aa2e44 2025-07-17T08:34:15.9630413Z * [new tag] trunk/4ce6e6ec8890a3f6ee604c9efb3ff153825ce575 -> trunk/4ce6e6ec8890a3f6ee604c9efb3ff153825ce575 2025-07-17T08:34:15.9632478Z * [new tag] trunk/4cfc0a320897b6ec75b85cfc07bc29009a824177 -> trunk/4cfc0a320897b6ec75b85cfc07bc29009a824177 2025-07-17T08:34:15.9634606Z * [new tag] trunk/4d055982e38f59fdb2a4c9d8855e58548bc42c12 -> trunk/4d055982e38f59fdb2a4c9d8855e58548bc42c12 2025-07-17T08:34:15.9636872Z * [new tag] trunk/4d3ecefda5a41df678fd68b020c521db95e9fbde -> trunk/4d3ecefda5a41df678fd68b020c521db95e9fbde 2025-07-17T08:34:15.9638934Z * [new tag] trunk/4d5d627e5ff3310318f9df80348ee419324228df -> trunk/4d5d627e5ff3310318f9df80348ee419324228df 2025-07-17T08:34:15.9641059Z * [new tag] trunk/4d9d884c3f5adc89b59d25d5a080498b76f9bb39 -> trunk/4d9d884c3f5adc89b59d25d5a080498b76f9bb39 2025-07-17T08:34:15.9643458Z * [new tag] trunk/4da98351b9e231bbbe83b5c590cf7d2ea382333a -> trunk/4da98351b9e231bbbe83b5c590cf7d2ea382333a 2025-07-17T08:34:15.9645799Z * [new tag] trunk/4dce5b71a0751aa4287c3a80adf07afc773ec4db -> trunk/4dce5b71a0751aa4287c3a80adf07afc773ec4db 2025-07-17T08:34:15.9648008Z * [new tag] trunk/4e13eca713c60ca63c1116823b99d2461a7422ef -> trunk/4e13eca713c60ca63c1116823b99d2461a7422ef 2025-07-17T08:34:15.9650139Z * [new tag] trunk/4e19477196547eb2e8157d6d132689373ffcf0fa -> trunk/4e19477196547eb2e8157d6d132689373ffcf0fa 2025-07-17T08:34:15.9652283Z * [new tag] trunk/4e8dd11be17a974e0ee5503b3061d4d47467844f -> trunk/4e8dd11be17a974e0ee5503b3061d4d47467844f 2025-07-17T08:34:15.9654438Z * [new tag] trunk/4ebd2690654ffe487c43941ebaea41b99914d8b7 -> trunk/4ebd2690654ffe487c43941ebaea41b99914d8b7 2025-07-17T08:34:15.9656584Z * [new tag] trunk/4ed1b03f7275075b1783d1deab946ced4ceba4d6 -> trunk/4ed1b03f7275075b1783d1deab946ced4ceba4d6 2025-07-17T08:34:15.9658944Z * [new tag] trunk/4ee4863232b9e07728d85254768bcba3aadc9b9a -> trunk/4ee4863232b9e07728d85254768bcba3aadc9b9a 2025-07-17T08:34:15.9661105Z * [new tag] trunk/4f36743f5eef2d9c40357eb5d8d8b1aeeacfbb2a -> trunk/4f36743f5eef2d9c40357eb5d8d8b1aeeacfbb2a 2025-07-17T08:34:15.9663279Z * [new tag] trunk/4f5b34427b57e8b876d12e6ce551f04a7c31cacf -> trunk/4f5b34427b57e8b876d12e6ce551f04a7c31cacf 2025-07-17T08:34:15.9665707Z * [new tag] trunk/4f5be5661240427fbbfbe1d137dce53ca32846b5 -> trunk/4f5be5661240427fbbfbe1d137dce53ca32846b5 2025-07-17T08:34:15.9670070Z * [new tag] trunk/4f70fbbd16d1f0d62af082246a95e56cffccc860 -> trunk/4f70fbbd16d1f0d62af082246a95e56cffccc860 2025-07-17T08:34:15.9672409Z * [new tag] trunk/4ff0e033c16aae064f05ee9300053cd304891673 -> trunk/4ff0e033c16aae064f05ee9300053cd304891673 2025-07-17T08:34:15.9674480Z * [new tag] trunk/4ff9b7fa3116b1c429e577830ac6e816734ad029 -> trunk/4ff9b7fa3116b1c429e577830ac6e816734ad029 2025-07-17T08:34:15.9676974Z * [new tag] trunk/502486d9466e81cda76c91d33eb869235480bee9 -> trunk/502486d9466e81cda76c91d33eb869235480bee9 2025-07-17T08:34:15.9678829Z * [new tag] trunk/503362d019b3782581492af7767945dbd75ca1c9 -> trunk/503362d019b3782581492af7767945dbd75ca1c9 2025-07-17T08:34:15.9680841Z * [new tag] trunk/508cdc4fc9f7a91b5fd3f20de38d673daebf1af3 -> trunk/508cdc4fc9f7a91b5fd3f20de38d673daebf1af3 2025-07-17T08:34:15.9683157Z * [new tag] trunk/50940270ae179134cd4f9072f04ffdd55daf808e -> trunk/50940270ae179134cd4f9072f04ffdd55daf808e 2025-07-17T08:34:15.9685486Z * [new tag] trunk/50b2069b61942e923528c94ccbbc8ab5e92c381e -> trunk/50b2069b61942e923528c94ccbbc8ab5e92c381e 2025-07-17T08:34:15.9687748Z * [new tag] trunk/50d8168c8b62990fda86398c9dee1dc8cbd6908d -> trunk/50d8168c8b62990fda86398c9dee1dc8cbd6908d 2025-07-17T08:34:15.9690099Z * [new tag] trunk/510c398a4f1dff9217938159d605290149358332 -> trunk/510c398a4f1dff9217938159d605290149358332 2025-07-17T08:34:15.9692320Z * [new tag] trunk/5116293f7eb587ada6076f3c3ea1711a0ec7ab4a -> trunk/5116293f7eb587ada6076f3c3ea1711a0ec7ab4a 2025-07-17T08:34:15.9694583Z * [new tag] trunk/5118a8f8a5a7906e26bcbd35370de3416b5cdab0 -> trunk/5118a8f8a5a7906e26bcbd35370de3416b5cdab0 2025-07-17T08:34:15.9696742Z * [new tag] trunk/51560797ce70aea353585d8381ee88c2e6c81075 -> trunk/51560797ce70aea353585d8381ee88c2e6c81075 2025-07-17T08:34:15.9699062Z * [new tag] trunk/517d2995e09603017f07f065c1ae5b8d25962cd2 -> trunk/517d2995e09603017f07f065c1ae5b8d25962cd2 2025-07-17T08:34:15.9701292Z * [new tag] trunk/51853b358e698f6a42c1eef045ab8fa766129ee1 -> trunk/51853b358e698f6a42c1eef045ab8fa766129ee1 2025-07-17T08:34:15.9703425Z * [new tag] trunk/51a708ffc679b13f99e4c7cf19bc00082a3266a6 -> trunk/51a708ffc679b13f99e4c7cf19bc00082a3266a6 2025-07-17T08:34:15.9705685Z * [new tag] trunk/51eb8e8f84bb9aa901cff17dd649e18b17a8908c -> trunk/51eb8e8f84bb9aa901cff17dd649e18b17a8908c 2025-07-17T08:34:15.9708296Z * [new tag] trunk/52214485747ceb0ea47ce5a09babb0b82b3282b8 -> trunk/52214485747ceb0ea47ce5a09babb0b82b3282b8 2025-07-17T08:34:15.9710323Z * [new tag] trunk/522a18bd6c094c766c0de9ef539682d1a3f04a15 -> trunk/522a18bd6c094c766c0de9ef539682d1a3f04a15 2025-07-17T08:34:15.9712571Z * [new tag] trunk/523b637cbeb69665072a2cf489ec1c5313b57670 -> trunk/523b637cbeb69665072a2cf489ec1c5313b57670 2025-07-17T08:34:15.9714840Z * [new tag] trunk/524e8270955788c53473497fd2cc16c5aa0e4c67 -> trunk/524e8270955788c53473497fd2cc16c5aa0e4c67 2025-07-17T08:34:15.9719202Z * [new tag] trunk/5264f8cd8d08272003298cdefe6bd60b1b8c80b4 -> trunk/5264f8cd8d08272003298cdefe6bd60b1b8c80b4 2025-07-17T08:34:15.9721176Z * [new tag] trunk/52772765e034622d1a86476e4bb19c28e3945f74 -> trunk/52772765e034622d1a86476e4bb19c28e3945f74 2025-07-17T08:34:15.9722586Z * [new tag] trunk/5285d1024376396a00fe750fe3d628c65dc26254 -> trunk/5285d1024376396a00fe750fe3d628c65dc26254 2025-07-17T08:34:15.9724688Z * [new tag] trunk/529e0357c6c4e74f8cd32c29198c5f1c9f6e329d -> trunk/529e0357c6c4e74f8cd32c29198c5f1c9f6e329d 2025-07-17T08:34:15.9726845Z * [new tag] trunk/52e4e41cbc36a5cf44395ff84ca2d069263560de -> trunk/52e4e41cbc36a5cf44395ff84ca2d069263560de 2025-07-17T08:34:15.9729099Z * [new tag] trunk/52edfb2cbcdded865645cc82bb1bb501fdcbdb52 -> trunk/52edfb2cbcdded865645cc82bb1bb501fdcbdb52 2025-07-17T08:34:15.9731357Z * [new tag] trunk/52f873adc23e7069ce95933d97ee514022cfbf7d -> trunk/52f873adc23e7069ce95933d97ee514022cfbf7d 2025-07-17T08:34:15.9733660Z * [new tag] trunk/53057fc16af2d381a61fe891b52ac8731ee9dfae -> trunk/53057fc16af2d381a61fe891b52ac8731ee9dfae 2025-07-17T08:34:15.9736096Z * [new tag] trunk/534c454e77ac0eefd52d63c60d42911e7f9617ea -> trunk/534c454e77ac0eefd52d63c60d42911e7f9617ea 2025-07-17T08:34:15.9738338Z * [new tag] trunk/53ab73090eb66d96e45ec134e41cf67266208954 -> trunk/53ab73090eb66d96e45ec134e41cf67266208954 2025-07-17T08:34:15.9740690Z * [new tag] trunk/53cd18f6b31bc2ea62985fda87e96aa17cd11bc1 -> trunk/53cd18f6b31bc2ea62985fda87e96aa17cd11bc1 2025-07-17T08:34:15.9742787Z * [new tag] trunk/53d06e18d9b165cb8aa0a5a3cbb6837ac3000c59 -> trunk/53d06e18d9b165cb8aa0a5a3cbb6837ac3000c59 2025-07-17T08:34:15.9745167Z * [new tag] trunk/53e0b9c3936176521ed8d71c00abd5b7499057c2 -> trunk/53e0b9c3936176521ed8d71c00abd5b7499057c2 2025-07-17T08:34:15.9747334Z * [new tag] trunk/541297daae63f74a90bac52c5db3540bf84bc971 -> trunk/541297daae63f74a90bac52c5db3540bf84bc971 2025-07-17T08:34:15.9749534Z * [new tag] trunk/5430990bd7d050f249b1cdf1f7d1016c4b17488d -> trunk/5430990bd7d050f249b1cdf1f7d1016c4b17488d 2025-07-17T08:34:15.9751686Z * [new tag] trunk/5435e7539930be7bd038683ce096038b30c5bb5f -> trunk/5435e7539930be7bd038683ce096038b30c5bb5f 2025-07-17T08:34:15.9753944Z * [new tag] trunk/545fbd58dc8b4f688f6b259cb671b3ec16a46810 -> trunk/545fbd58dc8b4f688f6b259cb671b3ec16a46810 2025-07-17T08:34:15.9756129Z * [new tag] trunk/54701a0c943245402fceeab0e55d7aa394303c20 -> trunk/54701a0c943245402fceeab0e55d7aa394303c20 2025-07-17T08:34:15.9758470Z * [new tag] trunk/5484890539823d9867c74209588abe095c9232a1 -> trunk/5484890539823d9867c74209588abe095c9232a1 2025-07-17T08:34:15.9760721Z * [new tag] trunk/548c9d8281d9b3d6d12e90c40b3387faf0e24c61 -> trunk/548c9d8281d9b3d6d12e90c40b3387faf0e24c61 2025-07-17T08:34:15.9762847Z * [new tag] trunk/54976bca103fcf2b5037cc0cd1b37c4639fcf779 -> trunk/54976bca103fcf2b5037cc0cd1b37c4639fcf779 2025-07-17T08:34:15.9765025Z * [new tag] trunk/54998c2daaf5b8919cf82367492dd3c5177ab935 -> trunk/54998c2daaf5b8919cf82367492dd3c5177ab935 2025-07-17T08:34:15.9767193Z * [new tag] trunk/54a4d34d100f4ebe45f486451967daba896e839c -> trunk/54a4d34d100f4ebe45f486451967daba896e839c 2025-07-17T08:34:15.9769177Z * [new tag] trunk/54a7e5b5983d237b324b50703bcb0919a6c4c296 -> trunk/54a7e5b5983d237b324b50703bcb0919a6c4c296 2025-07-17T08:34:15.9771360Z * [new tag] trunk/54b8087f638de57c1c93c4624d03f2f2c512b90e -> trunk/54b8087f638de57c1c93c4624d03f2f2c512b90e 2025-07-17T08:34:15.9773516Z * [new tag] trunk/55108074c0795be3b617d3b13b06794f63e1f8ca -> trunk/55108074c0795be3b617d3b13b06794f63e1f8ca 2025-07-17T08:34:15.9775674Z * [new tag] trunk/554b5680405e6197a985040ffe88157beb637450 -> trunk/554b5680405e6197a985040ffe88157beb637450 2025-07-17T08:34:15.9777872Z * [new tag] trunk/555f3562541992b66a550eca8e8740884b1247f8 -> trunk/555f3562541992b66a550eca8e8740884b1247f8 2025-07-17T08:34:15.9780165Z * [new tag] trunk/5596cefba623fcc0739f1e9568131a85973cf1f9 -> trunk/5596cefba623fcc0739f1e9568131a85973cf1f9 2025-07-17T08:34:15.9782085Z * [new tag] trunk/55d888a616be3c94d8e4073b4d1580541692997d -> trunk/55d888a616be3c94d8e4073b4d1580541692997d 2025-07-17T08:34:15.9784218Z * [new tag] trunk/55dae0bf7a4b501be91d0e022e675337dfe288ff -> trunk/55dae0bf7a4b501be91d0e022e675337dfe288ff 2025-07-17T08:34:15.9786344Z * [new tag] trunk/55ef7b15e0b2de903bfc26adfb0788ecfbcb4ed4 -> trunk/55ef7b15e0b2de903bfc26adfb0788ecfbcb4ed4 2025-07-17T08:34:15.9788498Z * [new tag] trunk/5606c516fd87e5c3594177e4ca64c3cac7fdafd5 -> trunk/5606c516fd87e5c3594177e4ca64c3cac7fdafd5 2025-07-17T08:34:15.9790497Z * [new tag] trunk/5633283574c458bd6a3cbb6a0a890f0cb9c8b2b5 -> trunk/5633283574c458bd6a3cbb6a0a890f0cb9c8b2b5 2025-07-17T08:34:15.9792755Z * [new tag] trunk/563fd95563c5edd732ae260b3bd3d0c38822ab57 -> trunk/563fd95563c5edd732ae260b3bd3d0c38822ab57 2025-07-17T08:34:15.9794782Z * [new tag] trunk/565fd079099d33a81c11d7b36581f09441ba6efa -> trunk/565fd079099d33a81c11d7b36581f09441ba6efa 2025-07-17T08:34:15.9796741Z * [new tag] trunk/568ca89bac9a80a66d664593a26ae69ac604796e -> trunk/568ca89bac9a80a66d664593a26ae69ac604796e 2025-07-17T08:34:15.9798875Z * [new tag] trunk/5692cbb818f1af9fcce9234e202d232afa5f3fa3 -> trunk/5692cbb818f1af9fcce9234e202d232afa5f3fa3 2025-07-17T08:34:15.9800965Z * [new tag] trunk/56b03df6ac5b4185a2b7b92f253565500a5b51ca -> trunk/56b03df6ac5b4185a2b7b92f253565500a5b51ca 2025-07-17T08:34:15.9803057Z * [new tag] trunk/56b3bf0c74f5837fd4fa6293bc515d353cb10295 -> trunk/56b3bf0c74f5837fd4fa6293bc515d353cb10295 2025-07-17T08:34:15.9805265Z * [new tag] trunk/56c69bedcc7e2211e5a3d6249e51b1674be5d10e -> trunk/56c69bedcc7e2211e5a3d6249e51b1674be5d10e 2025-07-17T08:34:15.9807562Z * [new tag] trunk/5763ec5f8d11df5eea962bedc74563394c0e273f -> trunk/5763ec5f8d11df5eea962bedc74563394c0e273f 2025-07-17T08:34:15.9809741Z * [new tag] trunk/577baa411675ed241c7d5cf79f25c13e29dac583 -> trunk/577baa411675ed241c7d5cf79f25c13e29dac583 2025-07-17T08:34:15.9812132Z * [new tag] trunk/57e4d7b5ccb8e35b434b62cc1f9aafb7d4d82b55 -> trunk/57e4d7b5ccb8e35b434b62cc1f9aafb7d4d82b55 2025-07-17T08:34:15.9814314Z * [new tag] trunk/584a0510b30b2472e54197d6b67b6f5f5e8ac807 -> trunk/584a0510b30b2472e54197d6b67b6f5f5e8ac807 2025-07-17T08:34:15.9816454Z * [new tag] trunk/588b5fb94bc6d2195ba5a4287b7feebe736da7db -> trunk/588b5fb94bc6d2195ba5a4287b7feebe736da7db 2025-07-17T08:34:15.9818525Z * [new tag] trunk/58e5d20c57e6baafd82b3b49c0e52931f5ae5d51 -> trunk/58e5d20c57e6baafd82b3b49c0e52931f5ae5d51 2025-07-17T08:34:15.9820568Z * [new tag] trunk/590607c5992d200b86361c5b68d53c93d8099193 -> trunk/590607c5992d200b86361c5b68d53c93d8099193 2025-07-17T08:34:15.9822664Z * [new tag] trunk/590fe4d2d7565f2045ef1ad4f4aad1f3b3de7aa3 -> trunk/590fe4d2d7565f2045ef1ad4f4aad1f3b3de7aa3 2025-07-17T08:34:15.9824811Z * [new tag] trunk/5951fcd50acc51bb91beae8488758f35219da849 -> trunk/5951fcd50acc51bb91beae8488758f35219da849 2025-07-17T08:34:15.9827075Z * [new tag] trunk/596b418391aa5d520e69310250c1f86c6c0a1107 -> trunk/596b418391aa5d520e69310250c1f86c6c0a1107 2025-07-17T08:34:15.9829159Z * [new tag] trunk/59c3cac4547aafd2f718b7c64053098cc5886878 -> trunk/59c3cac4547aafd2f718b7c64053098cc5886878 2025-07-17T08:34:15.9831431Z * [new tag] trunk/59eb61b2d1e4b64debbefa036acd0d8c7d55f0a3 -> trunk/59eb61b2d1e4b64debbefa036acd0d8c7d55f0a3 2025-07-17T08:34:15.9833533Z * [new tag] trunk/59f9b25f3cfc635053843372ea29ff4bf754da3f -> trunk/59f9b25f3cfc635053843372ea29ff4bf754da3f 2025-07-17T08:34:15.9835814Z * [new tag] trunk/5a2db5152d23f76dbb45d20008d9af68e761e8d1 -> trunk/5a2db5152d23f76dbb45d20008d9af68e761e8d1 2025-07-17T08:34:15.9838075Z * [new tag] trunk/5a533f74a160f6a570c2b9770ffdd89874ba59e2 -> trunk/5a533f74a160f6a570c2b9770ffdd89874ba59e2 2025-07-17T08:34:15.9839937Z * [new tag] trunk/5a54db14e3843cfa87fd8d27487dbf2f2dfb6c47 -> trunk/5a54db14e3843cfa87fd8d27487dbf2f2dfb6c47 2025-07-17T08:34:15.9842115Z * [new tag] trunk/5a5a05a6a3be376130848e235df73b752eef0230 -> trunk/5a5a05a6a3be376130848e235df73b752eef0230 2025-07-17T08:34:15.9844247Z * [new tag] trunk/5ab257c74c8e3ffe99380790de2134ba8013555e -> trunk/5ab257c74c8e3ffe99380790de2134ba8013555e 2025-07-17T08:34:15.9846352Z * [new tag] trunk/5ab6a3fb6fd37c542060c606edd4b95c7e3cae82 -> trunk/5ab6a3fb6fd37c542060c606edd4b95c7e3cae82 2025-07-17T08:34:15.9848454Z * [new tag] trunk/5ad2bee2c8a7defd2580bb138145a49c37146fcc -> trunk/5ad2bee2c8a7defd2580bb138145a49c37146fcc 2025-07-17T08:34:15.9850578Z * [new tag] trunk/5aee022d8b2bc9d31ddaf877315ffb8ad9d62985 -> trunk/5aee022d8b2bc9d31ddaf877315ffb8ad9d62985 2025-07-17T08:34:15.9852514Z * [new tag] trunk/5b10b0a96f9abf8c2751db324f0773aa433ec783 -> trunk/5b10b0a96f9abf8c2751db324f0773aa433ec783 2025-07-17T08:34:15.9854776Z * [new tag] trunk/5b4e0255d7ed756c312fb74ffcf17fe62c903bd7 -> trunk/5b4e0255d7ed756c312fb74ffcf17fe62c903bd7 2025-07-17T08:34:15.9857030Z * [new tag] trunk/5b656289064311e8bb08e1148db7d9b41f9d7ac8 -> trunk/5b656289064311e8bb08e1148db7d9b41f9d7ac8 2025-07-17T08:34:15.9860259Z * [new tag] trunk/5b9db4335e61c1c903cb0769282cbea588e49036 -> trunk/5b9db4335e61c1c903cb0769282cbea588e49036 2025-07-17T08:34:15.9862585Z * [new tag] trunk/5bd7804be2186f1756fba75ecc900d3b19016ad5 -> trunk/5bd7804be2186f1756fba75ecc900d3b19016ad5 2025-07-17T08:34:15.9864780Z * [new tag] trunk/5c79a55e7e58c6382c7ce02da1cd07d358239d94 -> trunk/5c79a55e7e58c6382c7ce02da1cd07d358239d94 2025-07-17T08:34:15.9867039Z * [new tag] trunk/5c7e1d39ab81647300d60e7cc22b24acf2641457 -> trunk/5c7e1d39ab81647300d60e7cc22b24acf2641457 2025-07-17T08:34:15.9869282Z * [new tag] trunk/5cc4e856fda4accb2e9291527693a91dc2a18d89 -> trunk/5cc4e856fda4accb2e9291527693a91dc2a18d89 2025-07-17T08:34:15.9871401Z * [new tag] trunk/5cfe4377d6f3b5845ba1f67122e5fbda8d642ecd -> trunk/5cfe4377d6f3b5845ba1f67122e5fbda8d642ecd 2025-07-17T08:34:15.9873608Z * [new tag] trunk/5d5a5b3501dfb0759ed36d0a88b65cdcd87c1e27 -> trunk/5d5a5b3501dfb0759ed36d0a88b65cdcd87c1e27 2025-07-17T08:34:15.9875791Z * [new tag] trunk/5d8d126249f83a9581f6b086f0891753bbb7175e -> trunk/5d8d126249f83a9581f6b086f0891753bbb7175e 2025-07-17T08:34:15.9878441Z * [new tag] trunk/5db9a2b54ae69917753be5b2eef0e15ad3cfd19b -> trunk/5db9a2b54ae69917753be5b2eef0e15ad3cfd19b 2025-07-17T08:34:15.9880740Z * [new tag] trunk/5dc75f72d4ede12067ddca459e02c8ee8a95e02c -> trunk/5dc75f72d4ede12067ddca459e02c8ee8a95e02c 2025-07-17T08:34:15.9882907Z * [new tag] trunk/5dd07c70e53a86b73f49711b8186d86dc4f1b32a -> trunk/5dd07c70e53a86b73f49711b8186d86dc4f1b32a 2025-07-17T08:34:15.9884911Z * [new tag] trunk/5dd9652389ed7959a842323e4ce063f553710e47 -> trunk/5dd9652389ed7959a842323e4ce063f553710e47 2025-07-17T08:34:15.9887267Z * [new tag] trunk/5df3bf13ec4e436abefe9d3822230727c04c2ab7 -> trunk/5df3bf13ec4e436abefe9d3822230727c04c2ab7 2025-07-17T08:34:15.9889367Z * [new tag] trunk/5dfd8a9c7a464bb42e81b8594eefd2fa865e5423 -> trunk/5dfd8a9c7a464bb42e81b8594eefd2fa865e5423 2025-07-17T08:34:15.9891562Z * [new tag] trunk/5dfe1787b5ed372fe1e9f0d73fab4e53a61bd17a -> trunk/5dfe1787b5ed372fe1e9f0d73fab4e53a61bd17a 2025-07-17T08:34:15.9893530Z * [new tag] trunk/5e18bc333144473f1f10bc8a5ba05dba7950fb8a -> trunk/5e18bc333144473f1f10bc8a5ba05dba7950fb8a 2025-07-17T08:34:15.9896105Z * [new tag] trunk/5e636d664ae0ccfdc1f0c5b060eb50fabf56e91b -> trunk/5e636d664ae0ccfdc1f0c5b060eb50fabf56e91b 2025-07-17T08:34:15.9898030Z * [new tag] trunk/5e93abe3c0106cf6fc694a38433909f20ee6a92c -> trunk/5e93abe3c0106cf6fc694a38433909f20ee6a92c 2025-07-17T08:34:15.9900255Z * [new tag] trunk/5eb5c3700bf51015e6a46f9f20c0e0e20cf55ab6 -> trunk/5eb5c3700bf51015e6a46f9f20c0e0e20cf55ab6 2025-07-17T08:34:15.9902448Z * [new tag] trunk/5f1225ef487a4c3954499a8ae5b5a55751e3ef2b -> trunk/5f1225ef487a4c3954499a8ae5b5a55751e3ef2b 2025-07-17T08:34:15.9904542Z * [new tag] trunk/5fb07acbc32874a932cd26087cf752b2f4cc72df -> trunk/5fb07acbc32874a932cd26087cf752b2f4cc72df 2025-07-17T08:34:15.9908119Z * [new tag] trunk/5fbaa041e75af11c7ff892e544707820232bd750 -> trunk/5fbaa041e75af11c7ff892e544707820232bd750 2025-07-17T08:34:15.9910364Z * [new tag] trunk/602044068359a3be08b2030a1bb1efb4dc107767 -> trunk/602044068359a3be08b2030a1bb1efb4dc107767 2025-07-17T08:34:15.9912533Z * [new tag] trunk/603a54a9b33e1aabe1407721d7935b881a160968 -> trunk/603a54a9b33e1aabe1407721d7935b881a160968 2025-07-17T08:34:15.9914589Z * [new tag] trunk/606d73bde495b055984206986fb7dd8918570e89 -> trunk/606d73bde495b055984206986fb7dd8918570e89 2025-07-17T08:34:15.9916812Z * [new tag] trunk/6098209bfffa453ec2cea08b0c9991b3b7e2bfeb -> trunk/6098209bfffa453ec2cea08b0c9991b3b7e2bfeb 2025-07-17T08:34:15.9918909Z * [new tag] trunk/60abb0d3273749cb2a7d583c7c2863bd2819e87e -> trunk/60abb0d3273749cb2a7d583c7c2863bd2819e87e 2025-07-17T08:34:15.9921065Z * [new tag] trunk/60b41de0ca0e5240eccf15c26a2b55b4a34d6f47 -> trunk/60b41de0ca0e5240eccf15c26a2b55b4a34d6f47 2025-07-17T08:34:15.9923304Z * [new tag] trunk/60e66d11ab3d62273cafa7f6b104db7e20f75dfd -> trunk/60e66d11ab3d62273cafa7f6b104db7e20f75dfd 2025-07-17T08:34:15.9925502Z * [new tag] trunk/61712e6f2ba58cce354a742d918934ec7293ee43 -> trunk/61712e6f2ba58cce354a742d918934ec7293ee43 2025-07-17T08:34:15.9927914Z * [new tag] trunk/617e3f69f8479197be57a28cc31e001c3feec407 -> trunk/617e3f69f8479197be57a28cc31e001c3feec407 2025-07-17T08:34:15.9930092Z * [new tag] trunk/61a7b09ef39907b6c4b47445f965c4c77cc6c2ed -> trunk/61a7b09ef39907b6c4b47445f965c4c77cc6c2ed 2025-07-17T08:34:15.9932246Z * [new tag] trunk/61b271e0f3f93209325dea9dccb1e97e7bc16b41 -> trunk/61b271e0f3f93209325dea9dccb1e97e7bc16b41 2025-07-17T08:34:15.9934633Z * [new tag] trunk/61e13782ddddf9e957c984ef11d7bb7643b871e7 -> trunk/61e13782ddddf9e957c984ef11d7bb7643b871e7 2025-07-17T08:34:15.9936877Z * [new tag] trunk/61eaaa21a42398941ea3fb01585a0926e9544831 -> trunk/61eaaa21a42398941ea3fb01585a0926e9544831 2025-07-17T08:34:15.9938914Z * [new tag] trunk/61f6aa36b9f9a168bd3c710c449f0a78c0301972 -> trunk/61f6aa36b9f9a168bd3c710c449f0a78c0301972 2025-07-17T08:34:15.9941128Z * [new tag] trunk/6200584193b770411b7f91880bbff6f746acfcb0 -> trunk/6200584193b770411b7f91880bbff6f746acfcb0 2025-07-17T08:34:15.9943327Z * [new tag] trunk/620415e018cd67e45d5c0a87964aff8751c9bf71 -> trunk/620415e018cd67e45d5c0a87964aff8751c9bf71 2025-07-17T08:34:15.9945591Z * [new tag] trunk/6215e90b7b9af8275c5dbfaa5fd58d7ec08b6764 -> trunk/6215e90b7b9af8275c5dbfaa5fd58d7ec08b6764 2025-07-17T08:34:15.9947884Z * [new tag] trunk/62272d5b24e7f505a02175de3c56ecc287557d2a -> trunk/62272d5b24e7f505a02175de3c56ecc287557d2a 2025-07-17T08:34:15.9950050Z * [new tag] trunk/627ba411366bcc15019c49756d3f22fd3914bd50 -> trunk/627ba411366bcc15019c49756d3f22fd3914bd50 2025-07-17T08:34:15.9952255Z * [new tag] trunk/62fa3f5aebfe763f5de27e687e4c81df7c7080e3 -> trunk/62fa3f5aebfe763f5de27e687e4c81df7c7080e3 2025-07-17T08:34:15.9954427Z * [new tag] trunk/6303cc41b71c02af9c855f3cc85b1146a97fc7a8 -> trunk/6303cc41b71c02af9c855f3cc85b1146a97fc7a8 2025-07-17T08:34:15.9956880Z * [new tag] trunk/63360e64da814de8ce271f1e4b6e2380a03b585e -> trunk/63360e64da814de8ce271f1e4b6e2380a03b585e 2025-07-17T08:34:15.9958718Z * [new tag] trunk/63a96eaeb84f5af6e83c7360c09f5540a44d19ca -> trunk/63a96eaeb84f5af6e83c7360c09f5540a44d19ca 2025-07-17T08:34:15.9960921Z * [new tag] trunk/63e87d6d05dc05ed7cc642c0cf8fd1cd2e3b93a1 -> trunk/63e87d6d05dc05ed7cc642c0cf8fd1cd2e3b93a1 2025-07-17T08:34:15.9963009Z * [new tag] trunk/6401d1d53d13cb2d564d48a30a8cf4f952ff773d -> trunk/6401d1d53d13cb2d564d48a30a8cf4f952ff773d 2025-07-17T08:34:15.9965267Z * [new tag] trunk/640703d95f210876f20f8f16c868442dad17b477 -> trunk/640703d95f210876f20f8f16c868442dad17b477 2025-07-17T08:34:15.9967432Z * [new tag] trunk/640f5a70905b01405f1ce397d25308d3e6a1d421 -> trunk/640f5a70905b01405f1ce397d25308d3e6a1d421 2025-07-17T08:34:15.9969667Z * [new tag] trunk/6442ae9256a6936bc8efe31fc5326d5c1c1f660b -> trunk/6442ae9256a6936bc8efe31fc5326d5c1c1f660b 2025-07-17T08:34:15.9972003Z * [new tag] trunk/64436c38c956b252d8c9f6f68e94fb46c34efdac -> trunk/64436c38c956b252d8c9f6f68e94fb46c34efdac 2025-07-17T08:34:15.9974061Z * [new tag] trunk/644cc58dfffe1b5bd15688495551b49462c163f6 -> trunk/644cc58dfffe1b5bd15688495551b49462c163f6 2025-07-17T08:34:15.9976233Z * [new tag] trunk/64bb6317a57d14c37339d86cded7c6b860c7d638 -> trunk/64bb6317a57d14c37339d86cded7c6b860c7d638 2025-07-17T08:34:15.9978319Z * [new tag] trunk/64f2ec77f869a7d495694519fb482e9ecaaa6da1 -> trunk/64f2ec77f869a7d495694519fb482e9ecaaa6da1 2025-07-17T08:34:15.9980625Z * [new tag] trunk/651b4a68f2a60d55d266e40776709247ef347d68 -> trunk/651b4a68f2a60d55d266e40776709247ef347d68 2025-07-17T08:34:15.9982754Z * [new tag] trunk/653c52fe52254e6783d75a73c3a8abbfd9cd2b3c -> trunk/653c52fe52254e6783d75a73c3a8abbfd9cd2b3c 2025-07-17T08:34:15.9984822Z * [new tag] trunk/655b3b14ffba4ae73e26a63b4289329e8d160a6f -> trunk/655b3b14ffba4ae73e26a63b4289329e8d160a6f 2025-07-17T08:34:15.9987075Z * [new tag] trunk/65b9c13cce43a7a8666ca1ef12b554a5dcfd2492 -> trunk/65b9c13cce43a7a8666ca1ef12b554a5dcfd2492 2025-07-17T08:34:15.9989186Z * [new tag] trunk/65fcca4f8c97de82d35d51ad9b790d10433e9b91 -> trunk/65fcca4f8c97de82d35d51ad9b790d10433e9b91 2025-07-17T08:34:15.9991381Z * [new tag] trunk/660695f11dd73efbe2694be0ed5826e4624f22e4 -> trunk/660695f11dd73efbe2694be0ed5826e4624f22e4 2025-07-17T08:34:15.9993430Z * [new tag] trunk/660dbea909b98f19dc0239a7ae565f8a52cfe846 -> trunk/660dbea909b98f19dc0239a7ae565f8a52cfe846 2025-07-17T08:34:15.9995680Z * [new tag] trunk/6629eaf0c658b523eed47a98c0a3e6b44fd89c6b -> trunk/6629eaf0c658b523eed47a98c0a3e6b44fd89c6b 2025-07-17T08:34:15.9997809Z * [new tag] trunk/662c1cfed2bf3262709f1150fcb11e96bc29e142 -> trunk/662c1cfed2bf3262709f1150fcb11e96bc29e142 2025-07-17T08:34:16.0000040Z * [new tag] trunk/670dab6c630552b32189911f22896ec453e55ab7 -> trunk/670dab6c630552b32189911f22896ec453e55ab7 2025-07-17T08:34:16.0002243Z * [new tag] trunk/671a9d175b7e15b4947c97f50b931dba1cc35a63 -> trunk/671a9d175b7e15b4947c97f50b931dba1cc35a63 2025-07-17T08:34:16.0004575Z * [new tag] trunk/672ac2ec86d8ffe080399363df96597437e51115 -> trunk/672ac2ec86d8ffe080399363df96597437e51115 2025-07-17T08:34:16.0006728Z * [new tag] trunk/67ee0c6725e8dd2d0372ff902775b7040631aaf7 -> trunk/67ee0c6725e8dd2d0372ff902775b7040631aaf7 2025-07-17T08:34:16.0008894Z * [new tag] trunk/67f8270516ef877aec85801e4a8d0b533687e938 -> trunk/67f8270516ef877aec85801e4a8d0b533687e938 2025-07-17T08:34:16.0010915Z * [new tag] trunk/6835ba1b3409d9764c6e704cdc0311d53ea0841a -> trunk/6835ba1b3409d9764c6e704cdc0311d53ea0841a 2025-07-17T08:34:16.0013289Z * [new tag] trunk/68996dc18306ac0320ac5e6979f1a7a7a226b3cc -> trunk/68996dc18306ac0320ac5e6979f1a7a7a226b3cc 2025-07-17T08:34:16.0015251Z * [new tag] trunk/68f36683f0c0dfe7befeba2b65dee30faf88f7cc -> trunk/68f36683f0c0dfe7befeba2b65dee30faf88f7cc 2025-07-17T08:34:16.0017789Z * [new tag] trunk/6918758f554790e1155fe9f3ee4120692347680a -> trunk/6918758f554790e1155fe9f3ee4120692347680a 2025-07-17T08:34:16.0019994Z * [new tag] trunk/693116f765ede6632d3dd8ef3ffe01cc40b960b9 -> trunk/693116f765ede6632d3dd8ef3ffe01cc40b960b9 2025-07-17T08:34:16.0022395Z * [new tag] trunk/694028f50269c9f34721db813e9ee1a8221c99b2 -> trunk/694028f50269c9f34721db813e9ee1a8221c99b2 2025-07-17T08:34:16.0024575Z * [new tag] trunk/6959b5febe090375e261e678902681533d2e2528 -> trunk/6959b5febe090375e261e678902681533d2e2528 2025-07-17T08:34:16.0026802Z * [new tag] trunk/69f2e09cc241176d6128a8f92f9490422c5b81b3 -> trunk/69f2e09cc241176d6128a8f92f9490422c5b81b3 2025-07-17T08:34:16.0028962Z * [new tag] trunk/6a3d00aa3b1e54feeb040891bfe2c59c36b9443c -> trunk/6a3d00aa3b1e54feeb040891bfe2c59c36b9443c 2025-07-17T08:34:16.0031093Z * [new tag] trunk/6abe450a6fda580714a276ed2dd695ab99074b93 -> trunk/6abe450a6fda580714a276ed2dd695ab99074b93 2025-07-17T08:34:16.0033263Z * [new tag] trunk/6b05842e4785f52ab89a8fa2a88f9e6f2316cbbd -> trunk/6b05842e4785f52ab89a8fa2a88f9e6f2316cbbd 2025-07-17T08:34:16.0035426Z * [new tag] trunk/6b1211df294e57d59c1e1717b1fedc671ec5bd5a -> trunk/6b1211df294e57d59c1e1717b1fedc671ec5bd5a 2025-07-17T08:34:16.0037530Z * [new tag] trunk/6b2bef10afae4acb18f230a496392b673c954ce7 -> trunk/6b2bef10afae4acb18f230a496392b673c954ce7 2025-07-17T08:34:16.0039707Z * [new tag] trunk/6b3eef6d316ce33ef0f0416c2f3f6a827ac4fe30 -> trunk/6b3eef6d316ce33ef0f0416c2f3f6a827ac4fe30 2025-07-17T08:34:16.0041899Z * [new tag] trunk/6b45af38a5991134f45d9750b79c767688ba3761 -> trunk/6b45af38a5991134f45d9750b79c767688ba3761 2025-07-17T08:34:16.0044145Z * [new tag] trunk/6b7767fc8d759017b5fc2a7002051cf5c05615a9 -> trunk/6b7767fc8d759017b5fc2a7002051cf5c05615a9 2025-07-17T08:34:16.0046290Z * [new tag] trunk/6b84cb29f97957032c5aa91b4e2a6bfa166db04d -> trunk/6b84cb29f97957032c5aa91b4e2a6bfa166db04d 2025-07-17T08:34:16.0048538Z * [new tag] trunk/6bc263809de610e2fcdf594c122a0f99c6c7d96f -> trunk/6bc263809de610e2fcdf594c122a0f99c6c7d96f 2025-07-17T08:34:16.0050764Z * [new tag] trunk/6c008e2fb5a94342c5c4047d034c61bb067fa2b7 -> trunk/6c008e2fb5a94342c5c4047d034c61bb067fa2b7 2025-07-17T08:34:16.0052863Z * [new tag] trunk/6c05f2fca049344fbbb55eed7a6c866f34c8a477 -> trunk/6c05f2fca049344fbbb55eed7a6c866f34c8a477 2025-07-17T08:34:16.0054940Z * [new tag] trunk/6c0b42fd2f2c070f4c7ef9a3914698726f61ef3a -> trunk/6c0b42fd2f2c070f4c7ef9a3914698726f61ef3a 2025-07-17T08:34:16.0057180Z * [new tag] trunk/6c24c6633a00608d8c20805a77fae55f6b8fe9a6 -> trunk/6c24c6633a00608d8c20805a77fae55f6b8fe9a6 2025-07-17T08:34:16.0059477Z * [new tag] trunk/6c42afe1964496e1b02be1442470f254e35eb199 -> trunk/6c42afe1964496e1b02be1442470f254e35eb199 2025-07-17T08:34:16.0061612Z * [new tag] trunk/6c5227ba00a2904365af566c24b4681cd01a041c -> trunk/6c5227ba00a2904365af566c24b4681cd01a041c 2025-07-17T08:34:16.0064101Z * [new tag] trunk/6c795306378c47341d58109da03371bba2bec46e -> trunk/6c795306378c47341d58109da03371bba2bec46e 2025-07-17T08:34:16.0066387Z * [new tag] trunk/6cc490d40b16b3be7ecc8943fe1bc594246caab3 -> trunk/6cc490d40b16b3be7ecc8943fe1bc594246caab3 2025-07-17T08:34:16.0068620Z * [new tag] trunk/6d02321472ee0761092166dd273eb3ec386cf0c0 -> trunk/6d02321472ee0761092166dd273eb3ec386cf0c0 2025-07-17T08:34:16.0071101Z * [new tag] trunk/6d2155db498375317e2101068a220439dba80939 -> trunk/6d2155db498375317e2101068a220439dba80939 2025-07-17T08:34:16.0073115Z * [new tag] trunk/6d3a4356f61b28a14abd95f641e2615deb186365 -> trunk/6d3a4356f61b28a14abd95f641e2615deb186365 2025-07-17T08:34:16.0075403Z * [new tag] trunk/6d5c789ad5f5d162426a5a10c84813ec923dd8ee -> trunk/6d5c789ad5f5d162426a5a10c84813ec923dd8ee 2025-07-17T08:34:16.0077704Z * [new tag] trunk/6dc2b22269f82a1feec06b5e3e79b184704cc0ba -> trunk/6dc2b22269f82a1feec06b5e3e79b184704cc0ba 2025-07-17T08:34:16.0079845Z * [new tag] trunk/6de41ce0f899604c3f8b33e1f8d37eb89b3a963e -> trunk/6de41ce0f899604c3f8b33e1f8d37eb89b3a963e 2025-07-17T08:34:16.0082104Z * [new tag] trunk/6defd5084e3fd330561095252c30ea50efa8e0ca -> trunk/6defd5084e3fd330561095252c30ea50efa8e0ca 2025-07-17T08:34:16.0084465Z * [new tag] trunk/6dfada220e4f46c28df3a6bdf57260ca0717c4bd -> trunk/6dfada220e4f46c28df3a6bdf57260ca0717c4bd 2025-07-17T08:34:16.0086730Z * [new tag] trunk/6e17315cd33892919dc6deb6a9ecb568dda6195e -> trunk/6e17315cd33892919dc6deb6a9ecb568dda6195e 2025-07-17T08:34:16.0089313Z * [new tag] trunk/6e185c53124e1b5a0fe391959060c1249178bcb6 -> trunk/6e185c53124e1b5a0fe391959060c1249178bcb6 2025-07-17T08:34:16.0091604Z * [new tag] trunk/6e2992a9984f2c3f6469564008c7e45869b84678 -> trunk/6e2992a9984f2c3f6469564008c7e45869b84678 2025-07-17T08:34:16.0093911Z * [new tag] trunk/6ea91f067256447cda6fae533f806c1f8baafbe2 -> trunk/6ea91f067256447cda6fae533f806c1f8baafbe2 2025-07-17T08:34:16.0096150Z * [new tag] trunk/6eb6f198e1a28e904ce00a132c43aa2536419664 -> trunk/6eb6f198e1a28e904ce00a132c43aa2536419664 2025-07-17T08:34:16.0098383Z * [new tag] trunk/6ebe9a4f47e9cd1c9ccd467bcdfdea9445fd98d6 -> trunk/6ebe9a4f47e9cd1c9ccd467bcdfdea9445fd98d6 2025-07-17T08:34:16.0100603Z * [new tag] trunk/6ed85bfe6a1704a87a492a3381414186eb439f5e -> trunk/6ed85bfe6a1704a87a492a3381414186eb439f5e 2025-07-17T08:34:16.0102849Z * [new tag] trunk/6ef70edd9a9acee76a607bdd22bac400c8e29434 -> trunk/6ef70edd9a9acee76a607bdd22bac400c8e29434 2025-07-17T08:34:16.0105156Z * [new tag] trunk/6f05d58f2b0bc4779fbed9db5b0f80af1c686dcd -> trunk/6f05d58f2b0bc4779fbed9db5b0f80af1c686dcd 2025-07-17T08:34:16.0107640Z * [new tag] trunk/6f23f53599629a47d6e097b2a027048658a142d4 -> trunk/6f23f53599629a47d6e097b2a027048658a142d4 2025-07-17T08:34:16.0109837Z * [new tag] trunk/6f60cfe9b1445271f8b83f58b4c4f12578d837d3 -> trunk/6f60cfe9b1445271f8b83f58b4c4f12578d837d3 2025-07-17T08:34:16.0112222Z * [new tag] trunk/6fb62931593fc10252e4994cd1a0595ebf8e990f -> trunk/6fb62931593fc10252e4994cd1a0595ebf8e990f 2025-07-17T08:34:16.0114514Z * [new tag] trunk/6fe7456aa1a2d025d1d06e15ba3896e6adba94b8 -> trunk/6fe7456aa1a2d025d1d06e15ba3896e6adba94b8 2025-07-17T08:34:16.0116892Z * [new tag] trunk/6ffa03ef9e18299f41da076f421abbd32e51e175 -> trunk/6ffa03ef9e18299f41da076f421abbd32e51e175 2025-07-17T08:34:16.0119116Z * [new tag] trunk/702a304b0785d2dfe3d9bd4eed535c38e969818d -> trunk/702a304b0785d2dfe3d9bd4eed535c38e969818d 2025-07-17T08:34:16.0121334Z * [new tag] trunk/706bc41c4c3caa2654a926382c2ef0ef4d0bc55e -> trunk/706bc41c4c3caa2654a926382c2ef0ef4d0bc55e 2025-07-17T08:34:16.0123505Z * [new tag] trunk/706e236b080cadd5b8a016e4bf972cdb0886d57b -> trunk/706e236b080cadd5b8a016e4bf972cdb0886d57b 2025-07-17T08:34:16.0125703Z * [new tag] trunk/7070ab318061d44f195a83b6dc11ef9299603dfe -> trunk/7070ab318061d44f195a83b6dc11ef9299603dfe 2025-07-17T08:34:16.0128013Z * [new tag] trunk/7081b8233a64c350c64e9f00c9b9d00e52020241 -> trunk/7081b8233a64c350c64e9f00c9b9d00e52020241 2025-07-17T08:34:16.0130378Z * [new tag] trunk/70b68caf58815419924ddeda231cbf6535181c53 -> trunk/70b68caf58815419924ddeda231cbf6535181c53 2025-07-17T08:34:16.0132788Z * [new tag] trunk/70bb34929aabc9c11d9ec9bbb53a4f9fc32e5e19 -> trunk/70bb34929aabc9c11d9ec9bbb53a4f9fc32e5e19 2025-07-17T08:34:16.0134853Z * [new tag] trunk/710b92cf3b577036d551708b351b4043817233f5 -> trunk/710b92cf3b577036d551708b351b4043817233f5 2025-07-17T08:34:16.0137256Z * [new tag] trunk/717a099d427d97a62c26fee58f9da9a0893d4233 -> trunk/717a099d427d97a62c26fee58f9da9a0893d4233 2025-07-17T08:34:16.0139498Z * [new tag] trunk/71a650ad565fe75ca752acbc8b40fb9ce677f40f -> trunk/71a650ad565fe75ca752acbc8b40fb9ce677f40f 2025-07-17T08:34:16.0141689Z * [new tag] trunk/720c2c46b181ad446bf970d70a27fd0ce149114d -> trunk/720c2c46b181ad446bf970d70a27fd0ce149114d 2025-07-17T08:34:16.0143892Z * [new tag] trunk/721d2580dbf9a4922adc1c7d1cc8237126d3cdd6 -> trunk/721d2580dbf9a4922adc1c7d1cc8237126d3cdd6 2025-07-17T08:34:16.0147708Z * [new tag] trunk/72453a66769294e93a183dfb5bdefa23de873d89 -> trunk/72453a66769294e93a183dfb5bdefa23de873d89 2025-07-17T08:34:16.0150073Z * [new tag] trunk/725c3272848c408d0fa2cba4de76affe90f793b5 -> trunk/725c3272848c408d0fa2cba4de76affe90f793b5 2025-07-17T08:34:16.0152340Z * [new tag] trunk/7275f280454f790414b24147a2ba7f94d0eabcf6 -> trunk/7275f280454f790414b24147a2ba7f94d0eabcf6 2025-07-17T08:34:16.0154491Z * [new tag] trunk/728cf6721e2996490922d0eacb23081953e45fc7 -> trunk/728cf6721e2996490922d0eacb23081953e45fc7 2025-07-17T08:34:16.0156702Z * [new tag] trunk/72c8751b61e0b2c7b88003a41ed737a65768063c -> trunk/72c8751b61e0b2c7b88003a41ed737a65768063c 2025-07-17T08:34:16.0158951Z * [new tag] trunk/731351bb4ac572fb47d46c70b7425c209c81570a -> trunk/731351bb4ac572fb47d46c70b7425c209c81570a 2025-07-17T08:34:16.0161285Z * [new tag] trunk/73220d52fd67b5f4f5b15e0e0433e09733c93f31 -> trunk/73220d52fd67b5f4f5b15e0e0433e09733c93f31 2025-07-17T08:34:16.0163650Z * [new tag] trunk/73772919d2db560c9cc8ed617c94c77450542a94 -> trunk/73772919d2db560c9cc8ed617c94c77450542a94 2025-07-17T08:34:16.0165584Z * [new tag] trunk/7381c777245a836f88e9622005c6ea16009a3a0d -> trunk/7381c777245a836f88e9622005c6ea16009a3a0d 2025-07-17T08:34:16.0167797Z * [new tag] trunk/7392470da4386e654f303eb526dfba7e7777b06b -> trunk/7392470da4386e654f303eb526dfba7e7777b06b 2025-07-17T08:34:16.0169898Z * [new tag] trunk/7444debaca7de16b2cf71d0cd82dc8998c3a73c4 -> trunk/7444debaca7de16b2cf71d0cd82dc8998c3a73c4 2025-07-17T08:34:16.0171940Z * [new tag] trunk/7485ef078f182af981b5f17d06602de68816492c -> trunk/7485ef078f182af981b5f17d06602de68816492c 2025-07-17T08:34:16.0174024Z * [new tag] trunk/749757ac1b66485459e751b0e58b5a7c64d5f561 -> trunk/749757ac1b66485459e751b0e58b5a7c64d5f561 2025-07-17T08:34:16.0176222Z * [new tag] trunk/74ebd8d14e0d612ed35557ab254a90facf73b029 -> trunk/74ebd8d14e0d612ed35557ab254a90facf73b029 2025-07-17T08:34:16.0178263Z * [new tag] trunk/7521cd91118c1c1a55b8f7146d7e22bf60fe06bf -> trunk/7521cd91118c1c1a55b8f7146d7e22bf60fe06bf 2025-07-17T08:34:16.0180408Z * [new tag] trunk/752f202ef3445d2e28e2abeb6feed56eee9da5a9 -> trunk/752f202ef3445d2e28e2abeb6feed56eee9da5a9 2025-07-17T08:34:16.0182505Z * [new tag] trunk/7531bd64911774fc2656f290601834d5d02d3925 -> trunk/7531bd64911774fc2656f290601834d5d02d3925 2025-07-17T08:34:16.0184766Z * [new tag] trunk/754699610b0abec2fe3f5a73269b1dd09a330445 -> trunk/754699610b0abec2fe3f5a73269b1dd09a330445 2025-07-17T08:34:16.0187030Z * [new tag] trunk/754c04aa062d8f3c0449aec4bbcaab00bfca4bf2 -> trunk/754c04aa062d8f3c0449aec4bbcaab00bfca4bf2 2025-07-17T08:34:16.0189208Z * [new tag] trunk/75824035d392595de3bd0b8588e63c2fc6d09139 -> trunk/75824035d392595de3bd0b8588e63c2fc6d09139 2025-07-17T08:34:16.0191344Z * [new tag] trunk/7597988f1b5a41c0b91d379e0ce51111fd7cc95a -> trunk/7597988f1b5a41c0b91d379e0ce51111fd7cc95a 2025-07-17T08:34:16.0193308Z * [new tag] trunk/7599bebead35dd21bad3d3e85f62f400ad2c3139 -> trunk/7599bebead35dd21bad3d3e85f62f400ad2c3139 2025-07-17T08:34:16.0195411Z * [new tag] trunk/75a7d9e86842b57f9001a0fa6fd716927614dbf9 -> trunk/75a7d9e86842b57f9001a0fa6fd716927614dbf9 2025-07-17T08:34:16.0197483Z * [new tag] trunk/75f258dd1feeb90b8084849155e049fb57ba3521 -> trunk/75f258dd1feeb90b8084849155e049fb57ba3521 2025-07-17T08:34:16.0199526Z * [new tag] trunk/75f3e5a88df60caef27fd9c9df3fd51161378fcc -> trunk/75f3e5a88df60caef27fd9c9df3fd51161378fcc 2025-07-17T08:34:16.0201764Z * [new tag] trunk/75f489d37f048ec8a9106163ca16201156f05499 -> trunk/75f489d37f048ec8a9106163ca16201156f05499 2025-07-17T08:34:16.0203934Z * [new tag] trunk/764c02b78b935ba61c46277cb7409e6419460058 -> trunk/764c02b78b935ba61c46277cb7409e6419460058 2025-07-17T08:34:16.0206139Z * [new tag] trunk/76644c9ff58285c95764ffd27d629df219aab140 -> trunk/76644c9ff58285c95764ffd27d629df219aab140 2025-07-17T08:34:16.0208322Z * [new tag] trunk/769d754ab2469813a3b790ec58c25c466099dd3d -> trunk/769d754ab2469813a3b790ec58c25c466099dd3d 2025-07-17T08:34:16.0210745Z * [new tag] trunk/76ca23c41c7edcf7b4c60ff6162eedcdf06ef359 -> trunk/76ca23c41c7edcf7b4c60ff6162eedcdf06ef359 2025-07-17T08:34:16.0212829Z * [new tag] trunk/76d07e919f66c24a55746bbc060c14b3df02ffa8 -> trunk/76d07e919f66c24a55746bbc060c14b3df02ffa8 2025-07-17T08:34:16.0215126Z * [new tag] trunk/76fe88fa56c8aac7377127fb5553de94e16e9070 -> trunk/76fe88fa56c8aac7377127fb5553de94e16e9070 2025-07-17T08:34:16.0217359Z * [new tag] trunk/770285522805745959221b902f23776a13d56df8 -> trunk/770285522805745959221b902f23776a13d56df8 2025-07-17T08:34:16.0219837Z * [new tag] trunk/7709ff55123dbdcaaa44004738fb767324f05dbd -> trunk/7709ff55123dbdcaaa44004738fb767324f05dbd 2025-07-17T08:34:16.0222155Z * [new tag] trunk/771be857043bf794cd219a9b925e308e31f12314 -> trunk/771be857043bf794cd219a9b925e308e31f12314 2025-07-17T08:34:16.0224996Z * [new tag] trunk/772d5904152abc9702bf49037e46ab6203b83f55 -> trunk/772d5904152abc9702bf49037e46ab6203b83f55 2025-07-17T08:34:16.0227364Z * [new tag] trunk/77518d1a13cc105637814bc157387478384dc897 -> trunk/77518d1a13cc105637814bc157387478384dc897 2025-07-17T08:34:16.0229577Z * [new tag] trunk/77676753ecabf6a6645bdd3abfe01939e5751e76 -> trunk/77676753ecabf6a6645bdd3abfe01939e5751e76 2025-07-17T08:34:16.0231878Z * [new tag] trunk/777eca9f16aeecd7c362a235cf25e6b8e6eda57f -> trunk/777eca9f16aeecd7c362a235cf25e6b8e6eda57f 2025-07-17T08:34:16.0234258Z * [new tag] trunk/77ac3a096532ee08211dae38c2a9336c970d1785 -> trunk/77ac3a096532ee08211dae38c2a9336c970d1785 2025-07-17T08:34:16.0236585Z * [new tag] trunk/77f884c2ec62df9df930ae86e9b8437364900346 -> trunk/77f884c2ec62df9df930ae86e9b8437364900346 2025-07-17T08:34:16.0238941Z * [new tag] trunk/783a4c1f5002bf1749833821c450360a417d40c7 -> trunk/783a4c1f5002bf1749833821c450360a417d40c7 2025-07-17T08:34:16.0241192Z * [new tag] trunk/78684e27ace1ebd51ccc93d4b6b8a6a3ec3b4c14 -> trunk/78684e27ace1ebd51ccc93d4b6b8a6a3ec3b4c14 2025-07-17T08:34:16.0243418Z * [new tag] trunk/78ee2ee90eed957aec3dc80423b108b16938a8ae -> trunk/78ee2ee90eed957aec3dc80423b108b16938a8ae 2025-07-17T08:34:16.0245643Z * [new tag] trunk/7918978653ff4f21c284b34809acf88784deb0de -> trunk/7918978653ff4f21c284b34809acf88784deb0de 2025-07-17T08:34:16.0247893Z * [new tag] trunk/794b95d54bb2c49d704169ddb777acfd8da8cfad -> trunk/794b95d54bb2c49d704169ddb777acfd8da8cfad 2025-07-17T08:34:16.0250077Z * [new tag] trunk/794ef6c9b8f173a0cbab5288efb2d68aef9c90b9 -> trunk/794ef6c9b8f173a0cbab5288efb2d68aef9c90b9 2025-07-17T08:34:16.0252468Z * [new tag] trunk/795a6a0affd349adfb4e3df298b604b74f27b44e -> trunk/795a6a0affd349adfb4e3df298b604b74f27b44e 2025-07-17T08:34:16.0254444Z * [new tag] trunk/7982b8c703e24a36c0e0a01b8cf22cd272369b28 -> trunk/7982b8c703e24a36c0e0a01b8cf22cd272369b28 2025-07-17T08:34:16.0256846Z * [new tag] trunk/7986c0dba6e1044d90b7f607f9cca15922339bb4 -> trunk/7986c0dba6e1044d90b7f607f9cca15922339bb4 2025-07-17T08:34:16.0259096Z * [new tag] trunk/799443605bffbd0d80fb3f2edbfe5517e0e2b4a3 -> trunk/799443605bffbd0d80fb3f2edbfe5517e0e2b4a3 2025-07-17T08:34:16.0261433Z * [new tag] trunk/7999735d23aeca844d4a7b23de6ac2370767099a -> trunk/7999735d23aeca844d4a7b23de6ac2370767099a 2025-07-17T08:34:16.0263663Z * [new tag] trunk/79ab84e9b8fe561a55931b2108af45993a670276 -> trunk/79ab84e9b8fe561a55931b2108af45993a670276 2025-07-17T08:34:16.0265905Z * [new tag] trunk/79aef141695f2daea4a9aeb0f385726c5794a242 -> trunk/79aef141695f2daea4a9aeb0f385726c5794a242 2025-07-17T08:34:16.0268761Z * [new tag] trunk/79bdafe5b61f6f073dcd276e135076ff3277a951 -> trunk/79bdafe5b61f6f073dcd276e135076ff3277a951 2025-07-17T08:34:16.0270455Z * [new tag] trunk/79d7c754ab8ae0e5c3a614521632d2cfbfa0fdba -> trunk/79d7c754ab8ae0e5c3a614521632d2cfbfa0fdba 2025-07-17T08:34:16.0272759Z * [new tag] trunk/7a03b0d2ca3946b770bc817571fc36bd5c608b38 -> trunk/7a03b0d2ca3946b770bc817571fc36bd5c608b38 2025-07-17T08:34:16.0274932Z * [new tag] trunk/7a08755c5f3630150c50d09e16c0abf9501dea1e -> trunk/7a08755c5f3630150c50d09e16c0abf9501dea1e 2025-07-17T08:34:16.0277277Z * [new tag] trunk/7a41f207943cc6c88160d7a531fc57bdbe149253 -> trunk/7a41f207943cc6c88160d7a531fc57bdbe149253 2025-07-17T08:34:16.0279311Z * [new tag] trunk/7a48cc699006879a6ec185ac5bd4eaad0c0b7e36 -> trunk/7a48cc699006879a6ec185ac5bd4eaad0c0b7e36 2025-07-17T08:34:16.0281520Z * [new tag] trunk/7a92b5119654c07d15f5c0818e6ae804b01e836c -> trunk/7a92b5119654c07d15f5c0818e6ae804b01e836c 2025-07-17T08:34:16.0283537Z * [new tag] trunk/7ae7c1414354ad225c4a36b3228e30a2baf94923 -> trunk/7ae7c1414354ad225c4a36b3228e30a2baf94923 2025-07-17T08:34:16.0285675Z * [new tag] trunk/7afb834f939eccbb3262e646f0922eed070074a7 -> trunk/7afb834f939eccbb3262e646f0922eed070074a7 2025-07-17T08:34:16.0287808Z * [new tag] trunk/7b0118884e0855e4918af53d44c3fbfc185a1ee9 -> trunk/7b0118884e0855e4918af53d44c3fbfc185a1ee9 2025-07-17T08:34:16.0290100Z * [new tag] trunk/7b392bac13a8007d1e0c6c789aa9bc515119faba -> trunk/7b392bac13a8007d1e0c6c789aa9bc515119faba 2025-07-17T08:34:16.0292372Z * [new tag] trunk/7b7cd56f5e2a965ae8a48c69441dd4f58bf68ceb -> trunk/7b7cd56f5e2a965ae8a48c69441dd4f58bf68ceb 2025-07-17T08:34:16.0294944Z * [new tag] trunk/7be862ab8f2b9040b34c58e1b77ce74c2512f062 -> trunk/7be862ab8f2b9040b34c58e1b77ce74c2512f062 2025-07-17T08:34:16.0297153Z * [new tag] trunk/7c1f62782875a4ac70b96aed0e6c9525b6e4eaf3 -> trunk/7c1f62782875a4ac70b96aed0e6c9525b6e4eaf3 2025-07-17T08:34:16.0299494Z * [new tag] trunk/7c51619e7fbd346e2299255c468cc43bd69425d7 -> trunk/7c51619e7fbd346e2299255c468cc43bd69425d7 2025-07-17T08:34:16.0301972Z * [new tag] trunk/7caf6c801ddfaf556a3ca191173b50002c4261f4 -> trunk/7caf6c801ddfaf556a3ca191173b50002c4261f4 2025-07-17T08:34:16.0304257Z * [new tag] trunk/7cda4017ddda554752e89069ae205be5e8388f59 -> trunk/7cda4017ddda554752e89069ae205be5e8388f59 2025-07-17T08:34:16.0306984Z * [new tag] trunk/7cf31b4a426f3791af30159cea420687f347cd7a -> trunk/7cf31b4a426f3791af30159cea420687f347cd7a 2025-07-17T08:34:16.0309209Z * [new tag] trunk/7cf38d2a0521c5ca292a720bce00617be749a0e7 -> trunk/7cf38d2a0521c5ca292a720bce00617be749a0e7 2025-07-17T08:34:16.0311833Z * [new tag] trunk/7cfd054075b0713642fab8dc4313fc1e5d992599 -> trunk/7cfd054075b0713642fab8dc4313fc1e5d992599 2025-07-17T08:34:16.0313788Z * [new tag] trunk/7d1b3f599d6968a3efe70b7401310e3127949f13 -> trunk/7d1b3f599d6968a3efe70b7401310e3127949f13 2025-07-17T08:34:16.0316492Z * [new tag] trunk/7d4228dbfd13d1ac8fac2c78c042dbb8314f042d -> trunk/7d4228dbfd13d1ac8fac2c78c042dbb8314f042d 2025-07-17T08:34:16.0318835Z * [new tag] trunk/7dcc77e422dcf97ce35991a138ab635a5cb88731 -> trunk/7dcc77e422dcf97ce35991a138ab635a5cb88731 2025-07-17T08:34:16.0321076Z * [new tag] trunk/7e433d5f423248914c5e9838d3ea145db7964923 -> trunk/7e433d5f423248914c5e9838d3ea145db7964923 2025-07-17T08:34:16.0323788Z * [new tag] trunk/7e4c097b0752ae79a8b5dd1de21a51aaafba2ef9 -> trunk/7e4c097b0752ae79a8b5dd1de21a51aaafba2ef9 2025-07-17T08:34:16.0326085Z * [new tag] trunk/7e54c02a35b905e758497b856a1953eb009ba836 -> trunk/7e54c02a35b905e758497b856a1953eb009ba836 2025-07-17T08:34:16.0329985Z * [new tag] trunk/7e83d5084588bdb51151f869b53c92c07bc9f544 -> trunk/7e83d5084588bdb51151f869b53c92c07bc9f544 2025-07-17T08:34:16.0333084Z * [new tag] trunk/7f0cddfb55d426b414cf7b4482c6a371618b349c -> trunk/7f0cddfb55d426b414cf7b4482c6a371618b349c 2025-07-17T08:34:16.0335959Z * [new tag] trunk/7f14b42adf70196d82340c59a9981ffcadf0c53c -> trunk/7f14b42adf70196d82340c59a9981ffcadf0c53c 2025-07-17T08:34:16.0338844Z * [new tag] trunk/7f6e7103a3aefd2d96b3cc6702be931a66b44977 -> trunk/7f6e7103a3aefd2d96b3cc6702be931a66b44977 2025-07-17T08:34:16.0341483Z * [new tag] trunk/7f9fc7e67ce9853a1bb4b16c901c708f78c1c5cd -> trunk/7f9fc7e67ce9853a1bb4b16c901c708f78c1c5cd 2025-07-17T08:34:16.0344572Z * [new tag] trunk/7fcad0231cb0ae9ba6a0004098b5ffd82c62858f -> trunk/7fcad0231cb0ae9ba6a0004098b5ffd82c62858f 2025-07-17T08:34:16.0347469Z * [new tag] trunk/805297981ae4e2aa08d133161477a477f5da274f -> trunk/805297981ae4e2aa08d133161477a477f5da274f 2025-07-17T08:34:16.0350218Z * [new tag] trunk/8088958793841191eba1faf98849904ee769bbfd -> trunk/8088958793841191eba1faf98849904ee769bbfd 2025-07-17T08:34:16.0352947Z * [new tag] trunk/80bcaa4195944d0f00f16abcbb702bde4d582d14 -> trunk/80bcaa4195944d0f00f16abcbb702bde4d582d14 2025-07-17T08:34:16.0356116Z * [new tag] trunk/80d89974c173c9d7a2c59fe9dfce2fe1301e2833 -> trunk/80d89974c173c9d7a2c59fe9dfce2fe1301e2833 2025-07-17T08:34:16.0358814Z * [new tag] trunk/8134684d44f3f9d09e775f3adc513bdef72245c9 -> trunk/8134684d44f3f9d09e775f3adc513bdef72245c9 2025-07-17T08:34:16.0362108Z * [new tag] trunk/8142a0286016e63a0e91b5667e1fb1a5e868ffd7 -> trunk/8142a0286016e63a0e91b5667e1fb1a5e868ffd7 2025-07-17T08:34:16.0365123Z * [new tag] trunk/8147c4a9044913e62ba3aab7f1eaaafead4f5930 -> trunk/8147c4a9044913e62ba3aab7f1eaaafead4f5930 2025-07-17T08:34:16.0367976Z * [new tag] trunk/8153340d1073c190ecf221e51abbd983286782a8 -> trunk/8153340d1073c190ecf221e51abbd983286782a8 2025-07-17T08:34:16.0370840Z * [new tag] trunk/815545f2dd6ade563cb1263f8bb7813f355edb2e -> trunk/815545f2dd6ade563cb1263f8bb7813f355edb2e 2025-07-17T08:34:16.0373737Z * [new tag] trunk/81759afed4fa4fa3a03f9ff671abe270679cc006 -> trunk/81759afed4fa4fa3a03f9ff671abe270679cc006 2025-07-17T08:34:16.0376340Z * [new tag] trunk/8186af5a26bc6a628f283c4791c8d68ef6fa1336 -> trunk/8186af5a26bc6a628f283c4791c8d68ef6fa1336 2025-07-17T08:34:16.0379045Z * [new tag] trunk/81b0b308cafae44339b8a123e8b1bec3bf9eb8ad -> trunk/81b0b308cafae44339b8a123e8b1bec3bf9eb8ad 2025-07-17T08:34:16.0381898Z * [new tag] trunk/81bf278537c081279a574157e1359ec7f0b65daf -> trunk/81bf278537c081279a574157e1359ec7f0b65daf 2025-07-17T08:34:16.0384696Z * [new tag] trunk/81c7445eb97f910ed89da9b11140d914651a1675 -> trunk/81c7445eb97f910ed89da9b11140d914651a1675 2025-07-17T08:34:16.0389454Z * [new tag] trunk/82672206b724a767dcb8c2541074449100de701b -> trunk/82672206b724a767dcb8c2541074449100de701b 2025-07-17T08:34:16.0391676Z * [new tag] trunk/826f12b829070e3d5bfd050f001b61aaf78e5447 -> trunk/826f12b829070e3d5bfd050f001b61aaf78e5447 2025-07-17T08:34:16.0394730Z * [new tag] trunk/82765dad16d7cda1ea382497dbf8c1ab2d07bfdd -> trunk/82765dad16d7cda1ea382497dbf8c1ab2d07bfdd 2025-07-17T08:34:16.0397616Z * [new tag] trunk/82a1ee1135b054d371d10081883b848ac7b7419f -> trunk/82a1ee1135b054d371d10081883b848ac7b7419f 2025-07-17T08:34:16.0400528Z * [new tag] trunk/82e6475d920991ef3be5d5637a72bf49313cc604 -> trunk/82e6475d920991ef3be5d5637a72bf49313cc604 2025-07-17T08:34:16.0403446Z * [new tag] trunk/82eefaedd98b63de8a87e34275af781f8eb177e1 -> trunk/82eefaedd98b63de8a87e34275af781f8eb177e1 2025-07-17T08:34:16.0405925Z * [new tag] trunk/82fb904140d258604a185154287b9c6fde047fc5 -> trunk/82fb904140d258604a185154287b9c6fde047fc5 2025-07-17T08:34:16.0408402Z * [new tag] trunk/830a335a7da5fec00395d440ba568749cb4e2e9e -> trunk/830a335a7da5fec00395d440ba568749cb4e2e9e 2025-07-17T08:34:16.0411229Z * [new tag] trunk/831c9010c7d7460fd78bc59b3fa28a65a0339e5d -> trunk/831c9010c7d7460fd78bc59b3fa28a65a0339e5d 2025-07-17T08:34:16.0414119Z * [new tag] trunk/8347268edcf3cc4f638474d860450bb0cd7a9908 -> trunk/8347268edcf3cc4f638474d860450bb0cd7a9908 2025-07-17T08:34:16.0416579Z * [new tag] trunk/836bb1941b593063f002394c1de3ec382c2ce50e -> trunk/836bb1941b593063f002394c1de3ec382c2ce50e 2025-07-17T08:34:16.0419361Z * [new tag] trunk/83700b448874cac8e89356ab06292e4289577269 -> trunk/83700b448874cac8e89356ab06292e4289577269 2025-07-17T08:34:16.0422869Z * [new tag] trunk/8372d0986a495d41f41c5d7684f1428ff847c699 -> trunk/8372d0986a495d41f41c5d7684f1428ff847c699 2025-07-17T08:34:16.0425976Z * [new tag] trunk/838798425731e6c90df4a885e90ec03f18eea10b -> trunk/838798425731e6c90df4a885e90ec03f18eea10b 2025-07-17T08:34:16.0428449Z * [new tag] trunk/83d22256f84232c5440b25a08459c649a32b9a4f -> trunk/83d22256f84232c5440b25a08459c649a32b9a4f 2025-07-17T08:34:16.0431154Z * [new tag] trunk/84085229765698166f07c9220d5544023ab80d47 -> trunk/84085229765698166f07c9220d5544023ab80d47 2025-07-17T08:34:16.0433590Z * [new tag] trunk/8485f1950710d383fd8d0d772fc978933c6fc175 -> trunk/8485f1950710d383fd8d0d772fc978933c6fc175 2025-07-17T08:34:16.0435976Z * [new tag] trunk/84b77ec1280a56bece8408a2e0e179b9eb95e1e2 -> trunk/84b77ec1280a56bece8408a2e0e179b9eb95e1e2 2025-07-17T08:34:16.0438272Z * [new tag] trunk/84c14361c28e1b553ea405ef5020cf0a90468850 -> trunk/84c14361c28e1b553ea405ef5020cf0a90468850 2025-07-17T08:34:16.0440515Z * [new tag] trunk/84c588e5eada9e7921608065edc444a15c22cb1c -> trunk/84c588e5eada9e7921608065edc444a15c22cb1c 2025-07-17T08:34:16.0442706Z * [new tag] trunk/84dec060b79078e68dcb6be1e7f308dad05d00e2 -> trunk/84dec060b79078e68dcb6be1e7f308dad05d00e2 2025-07-17T08:34:16.0444956Z * [new tag] trunk/85111cd165f108ffabb4a90083d59d7a867ebd9f -> trunk/85111cd165f108ffabb4a90083d59d7a867ebd9f 2025-07-17T08:34:16.0447300Z * [new tag] trunk/851a6fa82df251fbc368f0284d941ce78f68e7b1 -> trunk/851a6fa82df251fbc368f0284d941ce78f68e7b1 2025-07-17T08:34:16.0449793Z * [new tag] trunk/85320336799e38411d15c0e159b41248cda01218 -> trunk/85320336799e38411d15c0e159b41248cda01218 2025-07-17T08:34:16.0452355Z * [new tag] trunk/8554c8007ddaa8029e7e01bb1af12f358bf597c2 -> trunk/8554c8007ddaa8029e7e01bb1af12f358bf597c2 2025-07-17T08:34:16.0454880Z * [new tag] trunk/85857181ebca86e9c709e9922a9d9ef41a9c4ef9 -> trunk/85857181ebca86e9c709e9922a9d9ef41a9c4ef9 2025-07-17T08:34:16.0457772Z * [new tag] trunk/85df746892d9b0e87e7a5dfa78ef81a84aec6de0 -> trunk/85df746892d9b0e87e7a5dfa78ef81a84aec6de0 2025-07-17T08:34:16.0460145Z * [new tag] trunk/86251eff4069c468efebdb481dd18fe8d42856f0 -> trunk/86251eff4069c468efebdb481dd18fe8d42856f0 2025-07-17T08:34:16.0462429Z * [new tag] trunk/863327ae496471654344e1e04ccaa713a44a135d -> trunk/863327ae496471654344e1e04ccaa713a44a135d 2025-07-17T08:34:16.0464877Z * [new tag] trunk/86670b39fa3df63a652a9a06b59b73f92d70c392 -> trunk/86670b39fa3df63a652a9a06b59b73f92d70c392 2025-07-17T08:34:16.0467503Z * [new tag] trunk/86996c15dc4294c400c3a552d1a9e8e90aa6c7f6 -> trunk/86996c15dc4294c400c3a552d1a9e8e90aa6c7f6 2025-07-17T08:34:16.0469635Z * [new tag] trunk/86ced144534c2c78460a2d0f1d9ed2ba048f2514 -> trunk/86ced144534c2c78460a2d0f1d9ed2ba048f2514 2025-07-17T08:34:16.0471784Z * [new tag] trunk/86d8af6a6cc648134289de89d393d0dce5b3a5f4 -> trunk/86d8af6a6cc648134289de89d393d0dce5b3a5f4 2025-07-17T08:34:16.0473874Z * [new tag] trunk/86eaf452c330bd871262b7590ff8bd1bf432e2d7 -> trunk/86eaf452c330bd871262b7590ff8bd1bf432e2d7 2025-07-17T08:34:16.0476152Z * [new tag] trunk/87d615efab194482a00d241e4d9aebc513635cf5 -> trunk/87d615efab194482a00d241e4d9aebc513635cf5 2025-07-17T08:34:16.0478336Z * [new tag] trunk/8823138e47a3200c313f6bf2d21eb689d8150f39 -> trunk/8823138e47a3200c313f6bf2d21eb689d8150f39 2025-07-17T08:34:16.0480417Z * [new tag] trunk/8892b782a85b9de9f51334e65e009b3d59899b02 -> trunk/8892b782a85b9de9f51334e65e009b3d59899b02 2025-07-17T08:34:16.0482669Z * [new tag] trunk/88b9c285e0eb68134c5f834ceccdb99bc1d255d0 -> trunk/88b9c285e0eb68134c5f834ceccdb99bc1d255d0 2025-07-17T08:34:16.0485018Z * [new tag] trunk/88c6199db09372b6e2d55a5349ab545527842727 -> trunk/88c6199db09372b6e2d55a5349ab545527842727 2025-07-17T08:34:16.0487215Z * [new tag] trunk/88cd9f34b00c22e4edefa223490fcdd6c4ff272a -> trunk/88cd9f34b00c22e4edefa223490fcdd6c4ff272a 2025-07-17T08:34:16.0489541Z * [new tag] trunk/898179331e82e5ee1ff64737f508fd6a6da711f9 -> trunk/898179331e82e5ee1ff64737f508fd6a6da711f9 2025-07-17T08:34:16.0491983Z * [new tag] trunk/899d3d3e9eec4756a2e759f0203334dbe08f664e -> trunk/899d3d3e9eec4756a2e759f0203334dbe08f664e 2025-07-17T08:34:16.0494167Z * [new tag] trunk/8a2255130025abc80e03adaeb0b90d6463f2f916 -> trunk/8a2255130025abc80e03adaeb0b90d6463f2f916 2025-07-17T08:34:16.0496113Z * [new tag] trunk/8a396c56350ade97c18a6e13b3d97939f1eea075 -> trunk/8a396c56350ade97c18a6e13b3d97939f1eea075 2025-07-17T08:34:16.0498285Z * [new tag] trunk/8a47f9d03be0e0118a43443ff5f60db7be85102b -> trunk/8a47f9d03be0e0118a43443ff5f60db7be85102b 2025-07-17T08:34:16.0500430Z * [new tag] trunk/8a88c6e85abe345d95d970d424a0295cbc89b69c -> trunk/8a88c6e85abe345d95d970d424a0295cbc89b69c 2025-07-17T08:34:16.0502500Z * [new tag] trunk/8a8fac11318778aa5e4479b369b37fdb9e10ec17 -> trunk/8a8fac11318778aa5e4479b369b37fdb9e10ec17 2025-07-17T08:34:16.0504668Z * [new tag] trunk/8ad6197b466e91eb5fbbde2c76688d40fa472ac3 -> trunk/8ad6197b466e91eb5fbbde2c76688d40fa472ac3 2025-07-17T08:34:16.0506815Z * [new tag] trunk/8b0e0e4f2301f4aeb9fccf52cd2c6d8ac13da131 -> trunk/8b0e0e4f2301f4aeb9fccf52cd2c6d8ac13da131 2025-07-17T08:34:16.0508890Z * [new tag] trunk/8b68e5b1bb4a0014a64eab25c54b716968e109a9 -> trunk/8b68e5b1bb4a0014a64eab25c54b716968e109a9 2025-07-17T08:34:16.0510992Z * [new tag] trunk/8b8684466a11ae6ac27c85dbc6a02091dadac749 -> trunk/8b8684466a11ae6ac27c85dbc6a02091dadac749 2025-07-17T08:34:16.0513099Z * [new tag] trunk/8b97e4dd8cbe23101801043fe343d7350e527540 -> trunk/8b97e4dd8cbe23101801043fe343d7350e527540 2025-07-17T08:34:16.0515373Z * [new tag] trunk/8bda95228fbefa6ce204bf4da8b632d1516431bb -> trunk/8bda95228fbefa6ce204bf4da8b632d1516431bb 2025-07-17T08:34:16.0517435Z * [new tag] trunk/8c0df6fe1769c813f57da766528ff3cc4e56943e -> trunk/8c0df6fe1769c813f57da766528ff3cc4e56943e 2025-07-17T08:34:16.0519589Z * [new tag] trunk/8c2e45008282cf5202b72a0ecb0c2951438abeea -> trunk/8c2e45008282cf5202b72a0ecb0c2951438abeea 2025-07-17T08:34:16.0521680Z * [new tag] trunk/8c3f206457a1b9d75bc95a6c30a101135fcee329 -> trunk/8c3f206457a1b9d75bc95a6c30a101135fcee329 2025-07-17T08:34:16.0523813Z * [new tag] trunk/8c5b070d1f7cf4a9b58b09a28a0aeb5fdca28679 -> trunk/8c5b070d1f7cf4a9b58b09a28a0aeb5fdca28679 2025-07-17T08:34:16.0525928Z * [new tag] trunk/8c928372b3560bb512e2cf98c94efd923caf5fa7 -> trunk/8c928372b3560bb512e2cf98c94efd923caf5fa7 2025-07-17T08:34:16.0528044Z * [new tag] trunk/8cb0c4a4da0e017834d63f3d7dc2ab29e0ad6424 -> trunk/8cb0c4a4da0e017834d63f3d7dc2ab29e0ad6424 2025-07-17T08:34:16.0530070Z * [new tag] trunk/8d070187e34d5474ae88cefe56754f729138756b -> trunk/8d070187e34d5474ae88cefe56754f729138756b 2025-07-17T08:34:16.0532033Z * [new tag] trunk/8d7ee0f4fb7e41e8a49a5a24b6686b2324c493ba -> trunk/8d7ee0f4fb7e41e8a49a5a24b6686b2324c493ba 2025-07-17T08:34:16.0534182Z * [new tag] trunk/8da774d81feace041ff39de4ba2ddd43086370d3 -> trunk/8da774d81feace041ff39de4ba2ddd43086370d3 2025-07-17T08:34:16.0536227Z * [new tag] trunk/8dff457f42c9a20f9936d22773239df33cb48c9d -> trunk/8dff457f42c9a20f9936d22773239df33cb48c9d 2025-07-17T08:34:16.0538269Z * [new tag] trunk/8e02cd9c5ab151faad80e18e648c82a204fc3735 -> trunk/8e02cd9c5ab151faad80e18e648c82a204fc3735 2025-07-17T08:34:16.0540340Z * [new tag] trunk/8e8bbfc80340f0bf0a31a03d61b9b4dd72a40469 -> trunk/8e8bbfc80340f0bf0a31a03d61b9b4dd72a40469 2025-07-17T08:34:16.0542259Z * [new tag] trunk/8e8d4b13b0f6358e249a1e6fea9831b91b41b6d0 -> trunk/8e8d4b13b0f6358e249a1e6fea9831b91b41b6d0 2025-07-17T08:34:16.0544398Z * [new tag] trunk/8eaa9f2701277f328d9d6aea1bfe7cba20792f7c -> trunk/8eaa9f2701277f328d9d6aea1bfe7cba20792f7c 2025-07-17T08:34:16.0546681Z * [new tag] trunk/8eb3c5b7a18205974fff0353b97e599ea3d0e2a4 -> trunk/8eb3c5b7a18205974fff0353b97e599ea3d0e2a4 2025-07-17T08:34:16.0548872Z * [new tag] trunk/8f02161d1012143263fdbca47ee62983448e2c7e -> trunk/8f02161d1012143263fdbca47ee62983448e2c7e 2025-07-17T08:34:16.0551253Z * [new tag] trunk/8f0998aafe5e9594ade50cd263d5c63a5aad9333 -> trunk/8f0998aafe5e9594ade50cd263d5c63a5aad9333 2025-07-17T08:34:16.0553398Z * [new tag] trunk/8f5f01bf19e01f4b74add7f3d558966152a398c7 -> trunk/8f5f01bf19e01f4b74add7f3d558966152a398c7 2025-07-17T08:34:16.0555458Z * [new tag] trunk/8f9a191db6efcf44510db87c8f6f6e96c67e9b11 -> trunk/8f9a191db6efcf44510db87c8f6f6e96c67e9b11 2025-07-17T08:34:16.0557542Z * [new tag] trunk/8fcda2c60d974e4970ce939bc828bece9903c304 -> trunk/8fcda2c60d974e4970ce939bc828bece9903c304 2025-07-17T08:34:16.0559463Z * [new tag] trunk/900fba4c073b121b6c9ce581ea27e25c13a354e5 -> trunk/900fba4c073b121b6c9ce581ea27e25c13a354e5 2025-07-17T08:34:16.0561528Z * [new tag] trunk/9056279f8159b052599a31b591a78da1acc4224c -> trunk/9056279f8159b052599a31b591a78da1acc4224c 2025-07-17T08:34:16.0563612Z * [new tag] trunk/905b0846903a0193ee651a44762e72c881f83950 -> trunk/905b0846903a0193ee651a44762e72c881f83950 2025-07-17T08:34:16.0565801Z * [new tag] trunk/905b194a2e3d1d1c9ec136dfc1b6be7e62ff4b2c -> trunk/905b194a2e3d1d1c9ec136dfc1b6be7e62ff4b2c 2025-07-17T08:34:16.0567934Z * [new tag] trunk/90618581e971d28ac6950305d72521af05ed3a42 -> trunk/90618581e971d28ac6950305d72521af05ed3a42 2025-07-17T08:34:16.0570008Z * [new tag] trunk/907aea032d2f709f26dec78b762481c4604cc186 -> trunk/907aea032d2f709f26dec78b762481c4604cc186 2025-07-17T08:34:16.0572215Z * [new tag] trunk/907d0931cc8868164c2890b0452eda5d4da49278 -> trunk/907d0931cc8868164c2890b0452eda5d4da49278 2025-07-17T08:34:16.0574443Z * [new tag] trunk/90b973a2e228f2a5e2c158e5f5cf8f59d9118fc8 -> trunk/90b973a2e228f2a5e2c158e5f5cf8f59d9118fc8 2025-07-17T08:34:16.0576563Z * [new tag] trunk/91ee9ee82d40bcc36afbe9fed8dfc2f8c19c7316 -> trunk/91ee9ee82d40bcc36afbe9fed8dfc2f8c19c7316 2025-07-17T08:34:16.0578608Z * [new tag] trunk/9222552572890fd7ff1d75884cc6a99a55ab90bc -> trunk/9222552572890fd7ff1d75884cc6a99a55ab90bc 2025-07-17T08:34:16.0580754Z * [new tag] trunk/92388bb2ab14dae7b63c2088de4d6add413d1c27 -> trunk/92388bb2ab14dae7b63c2088de4d6add413d1c27 2025-07-17T08:34:16.0582999Z * [new tag] trunk/92409b6c89fbfbd3caa79c81b1e3d9e7917d3bc7 -> trunk/92409b6c89fbfbd3caa79c81b1e3d9e7917d3bc7 2025-07-17T08:34:16.0585170Z * [new tag] trunk/924fc52e1842de756a7632e7b2777aeed77d60e5 -> trunk/924fc52e1842de756a7632e7b2777aeed77d60e5 2025-07-17T08:34:16.0587311Z * [new tag] trunk/925fbfca27580f2ec8d5f9bfd549b1fe2e665617 -> trunk/925fbfca27580f2ec8d5f9bfd549b1fe2e665617 2025-07-17T08:34:16.0589416Z * [new tag] trunk/92b7ed6d07fcd6af31cddc7b4276e626b023bd0b -> trunk/92b7ed6d07fcd6af31cddc7b4276e626b023bd0b 2025-07-17T08:34:16.0591498Z * [new tag] trunk/92c79f36dbbdb01e0aab6efb8f52df828e1046d5 -> trunk/92c79f36dbbdb01e0aab6efb8f52df828e1046d5 2025-07-17T08:34:16.0593555Z * [new tag] trunk/92ee5bd9f6bef38b3e3e81a37f4d5374395aeb40 -> trunk/92ee5bd9f6bef38b3e3e81a37f4d5374395aeb40 2025-07-17T08:34:16.0595802Z * [new tag] trunk/92f41ccc2651ab284118ec7087977ab455027eb0 -> trunk/92f41ccc2651ab284118ec7087977ab455027eb0 2025-07-17T08:34:16.0597908Z * [new tag] trunk/930b575389f9233efddf70ea7b7804ed06af80d5 -> trunk/930b575389f9233efddf70ea7b7804ed06af80d5 2025-07-17T08:34:16.0600160Z * [new tag] trunk/9328a7fb589f6372ea478e47cdae8d124030f383 -> trunk/9328a7fb589f6372ea478e47cdae8d124030f383 2025-07-17T08:34:16.0602338Z * [new tag] trunk/9338d85d4594f981c198bcbb7edfccf6b92643be -> trunk/9338d85d4594f981c198bcbb7edfccf6b92643be 2025-07-17T08:34:16.0604326Z * [new tag] trunk/9345279c6ebdbad95b7b53bc2cb6f63a4e57b2cc -> trunk/9345279c6ebdbad95b7b53bc2cb6f63a4e57b2cc 2025-07-17T08:34:16.0606263Z * [new tag] trunk/937529f0b31788726e53890f5601886c64dc9eec -> trunk/937529f0b31788726e53890f5601886c64dc9eec 2025-07-17T08:34:16.0608379Z * [new tag] trunk/938515fa75f4e9d28c62a2aaa3e0e5428c867c36 -> trunk/938515fa75f4e9d28c62a2aaa3e0e5428c867c36 2025-07-17T08:34:16.0610345Z * [new tag] trunk/93854e83b7bfde94090662e9b372d8bf44ccf5d4 -> trunk/93854e83b7bfde94090662e9b372d8bf44ccf5d4 2025-07-17T08:34:16.0612480Z * [new tag] trunk/944a140e90389eced1ec38e14cb4345811ed0b1a -> trunk/944a140e90389eced1ec38e14cb4345811ed0b1a 2025-07-17T08:34:16.0614692Z * [new tag] trunk/9462106b7e41d9a24308255e15613b464ab086ce -> trunk/9462106b7e41d9a24308255e15613b464ab086ce 2025-07-17T08:34:16.0616762Z * [new tag] trunk/94716db22214912896cf680dc3eb88574f611a42 -> trunk/94716db22214912896cf680dc3eb88574f611a42 2025-07-17T08:34:16.0618934Z * [new tag] trunk/94763f5ca75330206777f3c1b36ff124706bd9d9 -> trunk/94763f5ca75330206777f3c1b36ff124706bd9d9 2025-07-17T08:34:16.0621006Z * [new tag] trunk/94995eba07763890b86465d53c4647c089f48d0a -> trunk/94995eba07763890b86465d53c4647c089f48d0a 2025-07-17T08:34:16.0623125Z * [new tag] trunk/94ae61533719defecb08d11f63c5d9fe9a826c00 -> trunk/94ae61533719defecb08d11f63c5d9fe9a826c00 2025-07-17T08:34:16.0625374Z * [new tag] trunk/94c746bb43484787a3f5bbdc2f72bd4fb02f2964 -> trunk/94c746bb43484787a3f5bbdc2f72bd4fb02f2964 2025-07-17T08:34:16.0629368Z * [new tag] trunk/94da4523ec4362dabbf9f08f9f2efce79ab70b73 -> trunk/94da4523ec4362dabbf9f08f9f2efce79ab70b73 2025-07-17T08:34:16.0631124Z * [new tag] trunk/94f8679019ea4b1272f1ad58ad7cad87147cf5a7 -> trunk/94f8679019ea4b1272f1ad58ad7cad87147cf5a7 2025-07-17T08:34:16.0633179Z * [new tag] trunk/9508d73307b4bc1fe453677526a096e5e10a7575 -> trunk/9508d73307b4bc1fe453677526a096e5e10a7575 2025-07-17T08:34:16.0635285Z * [new tag] trunk/9513b9d03fa8950ba5d2b59cc0b1a1aab3a41c06 -> trunk/9513b9d03fa8950ba5d2b59cc0b1a1aab3a41c06 2025-07-17T08:34:16.0637485Z * [new tag] trunk/95448b2ce61c3995ecdae0bfec655a5ef81a117d -> trunk/95448b2ce61c3995ecdae0bfec655a5ef81a117d 2025-07-17T08:34:16.0639669Z * [new tag] trunk/954ce949500746763a487a8ed9800035e7cd87d4 -> trunk/954ce949500746763a487a8ed9800035e7cd87d4 2025-07-17T08:34:16.0641761Z * [new tag] trunk/95a7d1912a86d857a9f4adb55b73b57518ce1e7b -> trunk/95a7d1912a86d857a9f4adb55b73b57518ce1e7b 2025-07-17T08:34:16.0643868Z * [new tag] trunk/95bc3da9f8555897d5d2b90f75385e3b2ff7bc43 -> trunk/95bc3da9f8555897d5d2b90f75385e3b2ff7bc43 2025-07-17T08:34:16.0645899Z * [new tag] trunk/95cb42c45d17f532222611e8028c9307622cc3c9 -> trunk/95cb42c45d17f532222611e8028c9307622cc3c9 2025-07-17T08:34:16.0647976Z * [new tag] trunk/9620994067b18e846a097d1e99af85ec2426ef0a -> trunk/9620994067b18e846a097d1e99af85ec2426ef0a 2025-07-17T08:34:16.0650098Z * [new tag] trunk/9636e2cfd3e995ef977f670ad47e8e895296d992 -> trunk/9636e2cfd3e995ef977f670ad47e8e895296d992 2025-07-17T08:34:16.0652259Z * [new tag] trunk/9642c7568967ab424c5d0e04ef2cd1e82a54b5f8 -> trunk/9642c7568967ab424c5d0e04ef2cd1e82a54b5f8 2025-07-17T08:34:16.0654503Z * [new tag] trunk/9656251bb1bee12c0e2f21828dc14a4c3c06afdd -> trunk/9656251bb1bee12c0e2f21828dc14a4c3c06afdd 2025-07-17T08:34:16.0656546Z * [new tag] trunk/9665702c64af633ab23499228d552a49660a9fa2 -> trunk/9665702c64af633ab23499228d552a49660a9fa2 2025-07-17T08:34:16.0658594Z * [new tag] trunk/96897e721b76f3b72aa406696165711d74f8d260 -> trunk/96897e721b76f3b72aa406696165711d74f8d260 2025-07-17T08:34:16.0660648Z * [new tag] trunk/968f90ce7344223c788eeba59200cc22b9f94dcd -> trunk/968f90ce7344223c788eeba59200cc22b9f94dcd 2025-07-17T08:34:16.0662754Z * [new tag] trunk/96d082d06bda98addd4ad7903d315477404dc272 -> trunk/96d082d06bda98addd4ad7903d315477404dc272 2025-07-17T08:34:16.0664675Z * [new tag] trunk/96df86641048a282a4622ac93d19e4c7b6c38b8e -> trunk/96df86641048a282a4622ac93d19e4c7b6c38b8e 2025-07-17T08:34:16.0667007Z * [new tag] trunk/96e4c95cd8d03037765ffd4b8fcdaa06c4b2c51c -> trunk/96e4c95cd8d03037765ffd4b8fcdaa06c4b2c51c 2025-07-17T08:34:16.0669014Z * [new tag] trunk/9768d393fa62df8a508136f5b6634bf955d8365d -> trunk/9768d393fa62df8a508136f5b6634bf955d8365d 2025-07-17T08:34:16.0671199Z * [new tag] trunk/977abe786d907c1ff76528a550e3d53c9f3b1044 -> trunk/977abe786d907c1ff76528a550e3d53c9f3b1044 2025-07-17T08:34:16.0673256Z * [new tag] trunk/981c99fdffd9a4f510e4b89245d16aa427ee0978 -> trunk/981c99fdffd9a4f510e4b89245d16aa427ee0978 2025-07-17T08:34:16.0675347Z * [new tag] trunk/987314aa96fdf8aa051e3643b26f4209b7fe166d -> trunk/987314aa96fdf8aa051e3643b26f4209b7fe166d 2025-07-17T08:34:16.0677380Z * [new tag] trunk/98a34e8d4b4d73504afbf49f70284221e6303314 -> trunk/98a34e8d4b4d73504afbf49f70284221e6303314 2025-07-17T08:34:16.0679383Z * [new tag] trunk/98bb0c0e7888a84de1d22c4952de69a5d5b1a0b3 -> trunk/98bb0c0e7888a84de1d22c4952de69a5d5b1a0b3 2025-07-17T08:34:16.0681568Z * [new tag] trunk/9944cd0949e96d656746de6df0cb3a5a954597e4 -> trunk/9944cd0949e96d656746de6df0cb3a5a954597e4 2025-07-17T08:34:16.0683853Z * [new tag] trunk/996206e66fcafff25d0af5177497e8f792000869 -> trunk/996206e66fcafff25d0af5177497e8f792000869 2025-07-17T08:34:16.0686197Z * [new tag] trunk/9963845a4e55a3e8d35ea49deb8f8c1f6c53cb74 -> trunk/9963845a4e55a3e8d35ea49deb8f8c1f6c53cb74 2025-07-17T08:34:16.0688273Z * [new tag] trunk/9968c854b6a38857d05fb1da7bea797fbf23c880 -> trunk/9968c854b6a38857d05fb1da7bea797fbf23c880 2025-07-17T08:34:16.0690393Z * [new tag] trunk/9968edd00256fdb47e2e0129df918c5b23c06419 -> trunk/9968edd00256fdb47e2e0129df918c5b23c06419 2025-07-17T08:34:16.0692488Z * [new tag] trunk/99c1a6bdd98ecb1f9b2fb8310bcd891c8c6adf57 -> trunk/99c1a6bdd98ecb1f9b2fb8310bcd891c8c6adf57 2025-07-17T08:34:16.0694663Z * [new tag] trunk/99e99d5bfe57eddfce23f7dc9ede016cb1393c52 -> trunk/99e99d5bfe57eddfce23f7dc9ede016cb1393c52 2025-07-17T08:34:16.0696977Z * [new tag] trunk/9a2c669425379eb264f896390b8fcd8d3f2ce959 -> trunk/9a2c669425379eb264f896390b8fcd8d3f2ce959 2025-07-17T08:34:16.0699155Z * [new tag] trunk/9a42f01586267b52fe303c0c1b7a71d50cc4178e -> trunk/9a42f01586267b52fe303c0c1b7a71d50cc4178e 2025-07-17T08:34:16.0701275Z * [new tag] trunk/9a4c08ddfc9b43c07cd16355277d359dfcef50d6 -> trunk/9a4c08ddfc9b43c07cd16355277d359dfcef50d6 2025-07-17T08:34:16.0703441Z * [new tag] trunk/9a5278225fc5e7b46d54a65ae1a3f049ee49824f -> trunk/9a5278225fc5e7b46d54a65ae1a3f049ee49824f 2025-07-17T08:34:16.0705588Z * [new tag] trunk/9a5c59368d46d3793e09b72a2782ef4d12e74392 -> trunk/9a5c59368d46d3793e09b72a2782ef4d12e74392 2025-07-17T08:34:16.0707802Z * [new tag] trunk/9aaa184105b2f436b5834187c4c004c02e438491 -> trunk/9aaa184105b2f436b5834187c4c004c02e438491 2025-07-17T08:34:16.0709983Z * [new tag] trunk/9afee0fa969fd4ebab86a2cbad3217c8bade156e -> trunk/9afee0fa969fd4ebab86a2cbad3217c8bade156e 2025-07-17T08:34:16.0712082Z * [new tag] trunk/9b0013c6bb98d7161e921d03be76c81bbc0eebef -> trunk/9b0013c6bb98d7161e921d03be76c81bbc0eebef 2025-07-17T08:34:16.0714291Z * [new tag] trunk/9b122aab5dcd419b96d4a1137c45e99bde94f14e -> trunk/9b122aab5dcd419b96d4a1137c45e99bde94f14e 2025-07-17T08:34:16.0716466Z * [new tag] trunk/9b498d3bb28b8e3411ce464dd2755c5b96d92c8f -> trunk/9b498d3bb28b8e3411ce464dd2755c5b96d92c8f 2025-07-17T08:34:16.0718669Z * [new tag] trunk/9b4a748e29a720d0fade7e1298a68cc36cfd5f5e -> trunk/9b4a748e29a720d0fade7e1298a68cc36cfd5f5e 2025-07-17T08:34:16.0720812Z * [new tag] trunk/9b4db093cb63555654c868ba9e3ab68ef4c722e5 -> trunk/9b4db093cb63555654c868ba9e3ab68ef4c722e5 2025-07-17T08:34:16.0723160Z * [new tag] trunk/9bae2fcf990cd51891218895ac1b3cfe70ffc2c3 -> trunk/9bae2fcf990cd51891218895ac1b3cfe70ffc2c3 2025-07-17T08:34:16.0725357Z * [new tag] trunk/9bd0830ed8d3e753afc3e9fff2553f4c5871f6f9 -> trunk/9bd0830ed8d3e753afc3e9fff2553f4c5871f6f9 2025-07-17T08:34:16.0727800Z * [new tag] trunk/9bd42c15707a4b410ee005d5916e882a7db432bb -> trunk/9bd42c15707a4b410ee005d5916e882a7db432bb 2025-07-17T08:34:16.0730444Z * [new tag] trunk/9bdf87e8918b9a3f78d7bcb8a770c19f7c82ac15 -> trunk/9bdf87e8918b9a3f78d7bcb8a770c19f7c82ac15 2025-07-17T08:34:16.0732798Z * [new tag] trunk/9be5860bc34b643abd02ac5b5a89d820efcdfbc9 -> trunk/9be5860bc34b643abd02ac5b5a89d820efcdfbc9 2025-07-17T08:34:16.0735327Z * [new tag] trunk/9bf41633d76f7816fdcc0b7ae22a42dc91821004 -> trunk/9bf41633d76f7816fdcc0b7ae22a42dc91821004 2025-07-17T08:34:16.0737650Z * [new tag] trunk/9bf6593e96b711641606e6008a4936173fd3b458 -> trunk/9bf6593e96b711641606e6008a4936173fd3b458 2025-07-17T08:34:16.0740173Z * [new tag] trunk/9bfefda296d2df31ce9bb90410d91d8f18127545 -> trunk/9bfefda296d2df31ce9bb90410d91d8f18127545 2025-07-17T08:34:16.0742452Z * [new tag] trunk/9c189ed29a2cfa08ff357cb5042ff8c34f07d668 -> trunk/9c189ed29a2cfa08ff357cb5042ff8c34f07d668 2025-07-17T08:34:16.0744911Z * [new tag] trunk/9c39bc24807a5843f8affdf56bd71836760dc554 -> trunk/9c39bc24807a5843f8affdf56bd71836760dc554 2025-07-17T08:34:16.0747247Z * [new tag] trunk/9ca080db87f66becb2bb2c61598c0beabae73d5f -> trunk/9ca080db87f66becb2bb2c61598c0beabae73d5f 2025-07-17T08:34:16.0749582Z * [new tag] trunk/9cced33c7c9cdfc68e9ed85c4b45bdfd05dac92d -> trunk/9cced33c7c9cdfc68e9ed85c4b45bdfd05dac92d 2025-07-17T08:34:16.0751901Z * [new tag] trunk/9cd521de4dad5fc6bca94e253a9334b9a521acb0 -> trunk/9cd521de4dad5fc6bca94e253a9334b9a521acb0 2025-07-17T08:34:16.0754190Z * [new tag] trunk/9d175bc7e615b062d8ea57df6ca17edb8a6b951f -> trunk/9d175bc7e615b062d8ea57df6ca17edb8a6b951f 2025-07-17T08:34:16.0756492Z * [new tag] trunk/9d184bda2f190a3ba72a4a0d95e1fde26d6bfc08 -> trunk/9d184bda2f190a3ba72a4a0d95e1fde26d6bfc08 2025-07-17T08:34:16.0759030Z * [new tag] trunk/9d2d2270037c5a014767cdfa531863da8062bf9d -> trunk/9d2d2270037c5a014767cdfa531863da8062bf9d 2025-07-17T08:34:16.0761636Z * [new tag] trunk/9d59b516e9b3026948918e3ff8c2ef55a33d13ad -> trunk/9d59b516e9b3026948918e3ff8c2ef55a33d13ad 2025-07-17T08:34:16.0763853Z * [new tag] trunk/9d677389cb5eb75d423860c55519b522961a9195 -> trunk/9d677389cb5eb75d423860c55519b522961a9195 2025-07-17T08:34:16.0766135Z * [new tag] trunk/9de23d0c29dfac8dc0f6f234bdbcd85a6375fa81 -> trunk/9de23d0c29dfac8dc0f6f234bdbcd85a6375fa81 2025-07-17T08:34:16.0768341Z * [new tag] trunk/9df0176408518b30ac172837bd697c9d19b19a98 -> trunk/9df0176408518b30ac172837bd697c9d19b19a98 2025-07-17T08:34:16.0770719Z * [new tag] trunk/9df2e8020f190bdad02258aff2b2f88f7db3dcab -> trunk/9df2e8020f190bdad02258aff2b2f88f7db3dcab 2025-07-17T08:34:16.0773141Z * [new tag] trunk/9e132b770e06c2399f827d3d1963fe036e9744e6 -> trunk/9e132b770e06c2399f827d3d1963fe036e9744e6 2025-07-17T08:34:16.0775476Z * [new tag] trunk/9e5f4a844c0aebf964a435094005c92713fbe99a -> trunk/9e5f4a844c0aebf964a435094005c92713fbe99a 2025-07-17T08:34:16.0777779Z * [new tag] trunk/9e88d6c857fd2b276427f4527130e2ecb7ed731e -> trunk/9e88d6c857fd2b276427f4527130e2ecb7ed731e 2025-07-17T08:34:16.0780169Z * [new tag] trunk/9e9484d022f8d65e2066b5683b75c84ba0f36332 -> trunk/9e9484d022f8d65e2066b5683b75c84ba0f36332 2025-07-17T08:34:16.0782680Z * [new tag] trunk/9ed0060225a7b78c60c42f29be94444b537edd4a -> trunk/9ed0060225a7b78c60c42f29be94444b537edd4a 2025-07-17T08:34:16.0785087Z * [new tag] trunk/9f18482d41227df3cf2248dfa54bd6601e61e1ca -> trunk/9f18482d41227df3cf2248dfa54bd6601e61e1ca 2025-07-17T08:34:16.0787334Z * [new tag] trunk/9f37cce69334bccebf4b21503f0047d0c0bb320c -> trunk/9f37cce69334bccebf4b21503f0047d0c0bb320c 2025-07-17T08:34:16.0789505Z * [new tag] trunk/9f5153b1a495b3b2b7ad9f22a064fe4074d742aa -> trunk/9f5153b1a495b3b2b7ad9f22a064fe4074d742aa 2025-07-17T08:34:16.0791614Z * [new tag] trunk/9f5276dc07c788533af8945b1605df47a33313e7 -> trunk/9f5276dc07c788533af8945b1605df47a33313e7 2025-07-17T08:34:16.0793738Z * [new tag] trunk/9fe2d156a9f81d67e248c0edaf7feee1a8d6c4d5 -> trunk/9fe2d156a9f81d67e248c0edaf7feee1a8d6c4d5 2025-07-17T08:34:16.0795810Z * [new tag] trunk/9fed2addedb42da86b657165fe14eadc911232cf -> trunk/9fed2addedb42da86b657165fe14eadc911232cf 2025-07-17T08:34:16.0797952Z * [new tag] trunk/a00a697c1748937b0c6d13508ff76b8af4957d8a -> trunk/a00a697c1748937b0c6d13508ff76b8af4957d8a 2025-07-17T08:34:16.0800108Z * [new tag] trunk/a0308edb6cdfd8983e80a499890d9f320556e844 -> trunk/a0308edb6cdfd8983e80a499890d9f320556e844 2025-07-17T08:34:16.0802200Z * [new tag] trunk/a04a13c44908fe0ace4f76a228d045dbf5c015bc -> trunk/a04a13c44908fe0ace4f76a228d045dbf5c015bc 2025-07-17T08:34:16.0804470Z * [new tag] trunk/a0e0abd0379ed403adc926d3cc5459fa35aff0a0 -> trunk/a0e0abd0379ed403adc926d3cc5459fa35aff0a0 2025-07-17T08:34:16.0806889Z * [new tag] trunk/a10024d7dea47c52469059a47efe376eb20adca0 -> trunk/a10024d7dea47c52469059a47efe376eb20adca0 2025-07-17T08:34:16.0808724Z * [new tag] trunk/a1057cda31fe890d237fe9a2e6b3314cd4be3439 -> trunk/a1057cda31fe890d237fe9a2e6b3314cd4be3439 2025-07-17T08:34:16.0810856Z * [new tag] trunk/a1257446f8eaf81eb758e89b4e784bed47b9c733 -> trunk/a1257446f8eaf81eb758e89b4e784bed47b9c733 2025-07-17T08:34:16.0812879Z * [new tag] trunk/a1282b18239204b0344884ebd232b33b2d8b748f -> trunk/a1282b18239204b0344884ebd232b33b2d8b748f 2025-07-17T08:34:16.0815144Z * [new tag] trunk/a14f427db68e54500ef4cd9ed34cb9537263bb74 -> trunk/a14f427db68e54500ef4cd9ed34cb9537263bb74 2025-07-17T08:34:16.0817228Z * [new tag] trunk/a1dad2f2d2c082e2a3784c3d585ef0204b7ccf75 -> trunk/a1dad2f2d2c082e2a3784c3d585ef0204b7ccf75 2025-07-17T08:34:16.0819360Z * [new tag] trunk/a1e4f1f98a0b9596fe52aaf2f85b0778498d5f49 -> trunk/a1e4f1f98a0b9596fe52aaf2f85b0778498d5f49 2025-07-17T08:34:16.0821485Z * [new tag] trunk/a205e8fd7357cf5c2b95eb4bec7c8ff7b5c41651 -> trunk/a205e8fd7357cf5c2b95eb4bec7c8ff7b5c41651 2025-07-17T08:34:16.0823637Z * [new tag] trunk/a21806f0386c2671fc11ae37fe2fe96c4aeb37d9 -> trunk/a21806f0386c2671fc11ae37fe2fe96c4aeb37d9 2025-07-17T08:34:16.0825808Z * [new tag] trunk/a23f4471b952d8cd630b860639e0aaa9be957d60 -> trunk/a23f4471b952d8cd630b860639e0aaa9be957d60 2025-07-17T08:34:16.0828037Z * [new tag] trunk/a24afbff3f44399ac9513addce6bd50cacab22d2 -> trunk/a24afbff3f44399ac9513addce6bd50cacab22d2 2025-07-17T08:34:16.0830054Z * [new tag] trunk/a24ce67deedab025531660ddb44c148bb066edf8 -> trunk/a24ce67deedab025531660ddb44c148bb066edf8 2025-07-17T08:34:16.0832342Z * [new tag] trunk/a25d1443fae79a9984eaa44e973b2f5645c85da8 -> trunk/a25d1443fae79a9984eaa44e973b2f5645c85da8 2025-07-17T08:34:16.0834557Z * [new tag] trunk/a26bf3892778ca7cc457c772a1f5194c11b6f33c -> trunk/a26bf3892778ca7cc457c772a1f5194c11b6f33c 2025-07-17T08:34:16.0836949Z * [new tag] trunk/a28e6ae38f1e1de33ecf48b679ff1eda71fc2efc -> trunk/a28e6ae38f1e1de33ecf48b679ff1eda71fc2efc 2025-07-17T08:34:16.0838878Z * [new tag] trunk/a2a75be0f8d680cf1dc36c50f34f225b0c7c4d6a -> trunk/a2a75be0f8d680cf1dc36c50f34f225b0c7c4d6a 2025-07-17T08:34:16.0852621Z * [new tag] trunk/a2ad16be72bf989c01b96c4c56b1c108a71c087f -> trunk/a2ad16be72bf989c01b96c4c56b1c108a71c087f 2025-07-17T08:34:16.0853319Z * [new tag] trunk/a2b0b2698d5b953861b2e4f3cdee11136f07bd3b -> trunk/a2b0b2698d5b953861b2e4f3cdee11136f07bd3b 2025-07-17T08:34:16.0853860Z * [new tag] trunk/a3098a74d494020dbb906c05ef047013e1921662 -> trunk/a3098a74d494020dbb906c05ef047013e1921662 2025-07-17T08:34:16.0854389Z * [new tag] trunk/a317c63d1b70a7989a4cab09394691fb7a3d8323 -> trunk/a317c63d1b70a7989a4cab09394691fb7a3d8323 2025-07-17T08:34:16.0854888Z * [new tag] trunk/a355158fcba807fda1e47e5ee42babdbcf447947 -> trunk/a355158fcba807fda1e47e5ee42babdbcf447947 2025-07-17T08:34:16.0855393Z * [new tag] trunk/a35b3a9b95bbe0703de33802162a83cb7970ddc0 -> trunk/a35b3a9b95bbe0703de33802162a83cb7970ddc0 2025-07-17T08:34:16.0855878Z * [new tag] trunk/a369350065493109d1abfbb994695777ab11bcf4 -> trunk/a369350065493109d1abfbb994695777ab11bcf4 2025-07-17T08:34:16.0856556Z * [new tag] trunk/a38f433be2e94a64b095a44ba39879d02d0c2316 -> trunk/a38f433be2e94a64b095a44ba39879d02d0c2316 2025-07-17T08:34:16.0858938Z * [new tag] trunk/a3ec6d64b24fdbd61164240f3bcd8530434812b5 -> trunk/a3ec6d64b24fdbd61164240f3bcd8530434812b5 2025-07-17T08:34:16.0861161Z * [new tag] trunk/a46ea8a364e528828c4369d58d53802291f0b49b -> trunk/a46ea8a364e528828c4369d58d53802291f0b49b 2025-07-17T08:34:16.0863677Z * [new tag] trunk/a47ca4fc746a663c0e97d55a87815d0965d0a7e9 -> trunk/a47ca4fc746a663c0e97d55a87815d0965d0a7e9 2025-07-17T08:34:16.0867236Z * [new tag] trunk/a4b59498c5222a02a706fa1e1308a6035d65feab -> trunk/a4b59498c5222a02a706fa1e1308a6035d65feab 2025-07-17T08:34:16.0869683Z * [new tag] trunk/a4c7e7f98373ad8f309e419c6f98b0134933dcda -> trunk/a4c7e7f98373ad8f309e419c6f98b0134933dcda 2025-07-17T08:34:16.0872021Z * [new tag] trunk/a4d753295ee5662056bdfd1b00fa242071ac7125 -> trunk/a4d753295ee5662056bdfd1b00fa242071ac7125 2025-07-17T08:34:16.0874476Z * [new tag] trunk/a4ea242edc8626250e54a62aa073cba0e5322af2 -> trunk/a4ea242edc8626250e54a62aa073cba0e5322af2 2025-07-17T08:34:16.0876987Z * [new tag] trunk/a529a5daf5d348f4f8dac4fcd85521153b6a3d4d -> trunk/a529a5daf5d348f4f8dac4fcd85521153b6a3d4d 2025-07-17T08:34:16.0879598Z * [new tag] trunk/a5938ff431202864c92fec7d9042574348b1c15b -> trunk/a5938ff431202864c92fec7d9042574348b1c15b 2025-07-17T08:34:16.0881905Z * [new tag] trunk/a5b4463d60e5beaec546843b24876ce573890784 -> trunk/a5b4463d60e5beaec546843b24876ce573890784 2025-07-17T08:34:16.0884467Z * [new tag] trunk/a5c61eb78dda6b7b4c4beb8bc616f9545ea4807b -> trunk/a5c61eb78dda6b7b4c4beb8bc616f9545ea4807b 2025-07-17T08:34:16.0886769Z * [new tag] trunk/a5cbb2bcb3c005909a3c10042605e861e6390927 -> trunk/a5cbb2bcb3c005909a3c10042605e861e6390927 2025-07-17T08:34:16.0889295Z * [new tag] trunk/a5df6ffbc2cf8d61fc98bca1c10f22ff64b2318e -> trunk/a5df6ffbc2cf8d61fc98bca1c10f22ff64b2318e 2025-07-17T08:34:16.0891924Z * [new tag] trunk/a5e68814d556cf67c6511876410970dd08c3dd6d -> trunk/a5e68814d556cf67c6511876410970dd08c3dd6d 2025-07-17T08:34:16.0894160Z * [new tag] trunk/a5f59cc2eab3a5201712c52fe48c268357ba4f3c -> trunk/a5f59cc2eab3a5201712c52fe48c268357ba4f3c 2025-07-17T08:34:16.0896591Z * [new tag] trunk/a6084b71edb8d2856356724b5e71c4e2a861867f -> trunk/a6084b71edb8d2856356724b5e71c4e2a861867f 2025-07-17T08:34:16.0899390Z * [new tag] trunk/a6210fd07b8fe1924f24229bb30562608af4f41a -> trunk/a6210fd07b8fe1924f24229bb30562608af4f41a 2025-07-17T08:34:16.0901847Z * [new tag] trunk/a666cf3b38f5e7739f94b175f152b127a6d51344 -> trunk/a666cf3b38f5e7739f94b175f152b127a6d51344 2025-07-17T08:34:16.0904457Z * [new tag] trunk/a67eb1a0d627d59d95a99722d37cab3bf054261f -> trunk/a67eb1a0d627d59d95a99722d37cab3bf054261f 2025-07-17T08:34:16.0907272Z * [new tag] trunk/a69e27ca5ad4287add73972ef1b34b469e3c7d23 -> trunk/a69e27ca5ad4287add73972ef1b34b469e3c7d23 2025-07-17T08:34:16.0910015Z * [new tag] trunk/a6a3a441442a96f38d0771c985f753223cea2ba0 -> trunk/a6a3a441442a96f38d0771c985f753223cea2ba0 2025-07-17T08:34:16.0912512Z * [new tag] trunk/a6a8641c8aee5797324c3d41749bcae62b4e5de8 -> trunk/a6a8641c8aee5797324c3d41749bcae62b4e5de8 2025-07-17T08:34:16.0915291Z * [new tag] trunk/a6fab82b16011213cb010c8c50461b9a680748a2 -> trunk/a6fab82b16011213cb010c8c50461b9a680748a2 2025-07-17T08:34:16.0917708Z * [new tag] trunk/a73d9e0aec9319e56ba0c9b0ccc25db69c739faf -> trunk/a73d9e0aec9319e56ba0c9b0ccc25db69c739faf 2025-07-17T08:34:16.0920443Z * [new tag] trunk/a767e50adca9e08dd638be60c9e5604de15b9f15 -> trunk/a767e50adca9e08dd638be60c9e5604de15b9f15 2025-07-17T08:34:16.0923602Z * [new tag] trunk/a7b29c88b1f55bac02f38bf1faba68b6c4be4c9c -> trunk/a7b29c88b1f55bac02f38bf1faba68b6c4be4c9c 2025-07-17T08:34:16.0928027Z * [new tag] trunk/a7eb153bba0534daaa1b652d44bb9fb5bd1ac7c7 -> trunk/a7eb153bba0534daaa1b652d44bb9fb5bd1ac7c7 2025-07-17T08:34:16.0930693Z * [new tag] trunk/a82c171bb26949dda5f5932051a905a9bab38761 -> trunk/a82c171bb26949dda5f5932051a905a9bab38761 2025-07-17T08:34:16.0933631Z * [new tag] trunk/a85ad5552532926b78d489d1b60671f74bf6dc8e -> trunk/a85ad5552532926b78d489d1b60671f74bf6dc8e 2025-07-17T08:34:16.0936135Z * [new tag] trunk/a87dfc7480bc98d33dab274d7e95723aa9cf7840 -> trunk/a87dfc7480bc98d33dab274d7e95723aa9cf7840 2025-07-17T08:34:16.0938599Z * [new tag] trunk/a8b973673798ca79dfe616c9080415d09f9e990d -> trunk/a8b973673798ca79dfe616c9080415d09f9e990d 2025-07-17T08:34:16.0941172Z * [new tag] trunk/a8ec7babcf27584cf068c3a5a04bb3ea0102ab99 -> trunk/a8ec7babcf27584cf068c3a5a04bb3ea0102ab99 2025-07-17T08:34:16.0943442Z * [new tag] trunk/a8fe982993221048ee1665ce28add1b02888784d -> trunk/a8fe982993221048ee1665ce28add1b02888784d 2025-07-17T08:34:16.0946393Z * [new tag] trunk/a92b24cd839aa6807f3c9ea8b8787da99c4490c0 -> trunk/a92b24cd839aa6807f3c9ea8b8787da99c4490c0 2025-07-17T08:34:16.0949025Z * [new tag] trunk/a9352bd25e9f070e8f846b8fdbcd7c90ad5c66b3 -> trunk/a9352bd25e9f070e8f846b8fdbcd7c90ad5c66b3 2025-07-17T08:34:16.0951531Z * [new tag] trunk/a952956d05dd617355007ae31d8e936474a35f14 -> trunk/a952956d05dd617355007ae31d8e936474a35f14 2025-07-17T08:34:16.0954138Z * [new tag] trunk/a9537b626c91ce617139ade60b9107a2805a4248 -> trunk/a9537b626c91ce617139ade60b9107a2805a4248 2025-07-17T08:34:16.0956791Z * [new tag] trunk/a95504b10fff38b5308660e0b535961beed6c9f1 -> trunk/a95504b10fff38b5308660e0b535961beed6c9f1 2025-07-17T08:34:16.0959739Z * [new tag] trunk/a9a0501ec40d8fab07b0f8d2fcbf8eb7147c1ae8 -> trunk/a9a0501ec40d8fab07b0f8d2fcbf8eb7147c1ae8 2025-07-17T08:34:16.0962122Z * [new tag] trunk/a9ac9f2635a9be28eaf267ed41070081a4ce05d8 -> trunk/a9ac9f2635a9be28eaf267ed41070081a4ce05d8 2025-07-17T08:34:16.0964387Z * [new tag] trunk/a9d5157e2596e1d830158e83fab618aab1d4ee39 -> trunk/a9d5157e2596e1d830158e83fab618aab1d4ee39 2025-07-17T08:34:16.0966605Z * [new tag] trunk/a9ee4250d55c6342b80e2d57a8ad9a1992ddcdce -> trunk/a9ee4250d55c6342b80e2d57a8ad9a1992ddcdce 2025-07-17T08:34:16.0969002Z * [new tag] trunk/a9ef7c4d044d386993a6e82b5ad37b7db759b502 -> trunk/a9ef7c4d044d386993a6e82b5ad37b7db759b502 2025-07-17T08:34:16.0971697Z * [new tag] trunk/a9f902add02383ca1b0386eb865767641975fede -> trunk/a9f902add02383ca1b0386eb865767641975fede 2025-07-17T08:34:16.0974271Z * [new tag] trunk/aa116285768ba6a8cd34bf8d245e38d04c88810a -> trunk/aa116285768ba6a8cd34bf8d245e38d04c88810a 2025-07-17T08:34:16.0977022Z * [new tag] trunk/aa280ea19fb20923d048909fa98af092e18ca2fb -> trunk/aa280ea19fb20923d048909fa98af092e18ca2fb 2025-07-17T08:34:16.0979319Z * [new tag] trunk/aa2d54148d476383986855af3fe53862da861dda -> trunk/aa2d54148d476383986855af3fe53862da861dda 2025-07-17T08:34:16.0981565Z * [new tag] trunk/aab949aa96a6b197b75ffa25608fa84049ff68ad -> trunk/aab949aa96a6b197b75ffa25608fa84049ff68ad 2025-07-17T08:34:16.0984017Z * [new tag] trunk/aac0e8f0e998a9381f0eb5dd303a853515109eb0 -> trunk/aac0e8f0e998a9381f0eb5dd303a853515109eb0 2025-07-17T08:34:16.0986467Z * [new tag] trunk/aacb9440791f4e9567e2123f19bb062e75c2d242 -> trunk/aacb9440791f4e9567e2123f19bb062e75c2d242 2025-07-17T08:34:16.0988914Z * [new tag] trunk/ab2294d8289a7757a2fc321cdefac88e2b378edf -> trunk/ab2294d8289a7757a2fc321cdefac88e2b378edf 2025-07-17T08:34:16.0991045Z * [new tag] trunk/ab3393e923514438c9bdfc1afcd0b2aac8be8de9 -> trunk/ab3393e923514438c9bdfc1afcd0b2aac8be8de9 2025-07-17T08:34:16.0993483Z * [new tag] trunk/ab51a937371e61f91cacae7d9fb4b1fe61f24f03 -> trunk/ab51a937371e61f91cacae7d9fb4b1fe61f24f03 2025-07-17T08:34:16.0995733Z * [new tag] trunk/ab56e5add9c36f611a4d9af2f43213926b65d7b4 -> trunk/ab56e5add9c36f611a4d9af2f43213926b65d7b4 2025-07-17T08:34:16.0998137Z * [new tag] trunk/ab655816b8f76f511fb2262d45276d8d1b13d59c -> trunk/ab655816b8f76f511fb2262d45276d8d1b13d59c 2025-07-17T08:34:16.1000528Z * [new tag] trunk/ab6cb34480a14f3cf2446519189ee5f0c5e7278d -> trunk/ab6cb34480a14f3cf2446519189ee5f0c5e7278d 2025-07-17T08:34:16.1002728Z * [new tag] trunk/ab81fb846cd0e52bbc6a2eb366706350639e209f -> trunk/ab81fb846cd0e52bbc6a2eb366706350639e209f 2025-07-17T08:34:16.1004868Z * [new tag] trunk/ab8874bd26894a8ab956408ceee2e89c9ad7f450 -> trunk/ab8874bd26894a8ab956408ceee2e89c9ad7f450 2025-07-17T08:34:16.1007053Z * [new tag] trunk/abbdf9f3632af505b3265b4258a6c185b257edde -> trunk/abbdf9f3632af505b3265b4258a6c185b257edde 2025-07-17T08:34:16.1009480Z * [new tag] trunk/abeae997a35b1920a45be9c26eff7474f2c6c5dd -> trunk/abeae997a35b1920a45be9c26eff7474f2c6c5dd 2025-07-17T08:34:16.1012014Z * [new tag] trunk/abf4da0d242274ba7ab4c0859d105cae74a2dbc5 -> trunk/abf4da0d242274ba7ab4c0859d105cae74a2dbc5 2025-07-17T08:34:16.1014354Z * [new tag] trunk/ac706bfc7f942b8a97401486a840dd8f6452f5cb -> trunk/ac706bfc7f942b8a97401486a840dd8f6452f5cb 2025-07-17T08:34:16.1016440Z * [new tag] trunk/ac86ec0e60370c037e018137f2048cafd47c5c28 -> trunk/ac86ec0e60370c037e018137f2048cafd47c5c28 2025-07-17T08:34:16.1018648Z * [new tag] trunk/acaf6ba3c6d0bdec88ab3f6c2ef82650050558d2 -> trunk/acaf6ba3c6d0bdec88ab3f6c2ef82650050558d2 2025-07-17T08:34:16.1020796Z * [new tag] trunk/ad86c05b78d343f3346d07a07e17639e421d2cf4 -> trunk/ad86c05b78d343f3346d07a07e17639e421d2cf4 2025-07-17T08:34:16.1023060Z * [new tag] trunk/ada44e5ba78be9377814678d1986556af2d6e570 -> trunk/ada44e5ba78be9377814678d1986556af2d6e570 2025-07-17T08:34:16.1025348Z * [new tag] trunk/add0b450bd9907ba9d089c79ca4af96c0590d8ff -> trunk/add0b450bd9907ba9d089c79ca4af96c0590d8ff 2025-07-17T08:34:16.1027706Z * [new tag] trunk/adf6dd1e44c1b006ea6078576b64d5616cecfa5a -> trunk/adf6dd1e44c1b006ea6078576b64d5616cecfa5a 2025-07-17T08:34:16.1030156Z * [new tag] trunk/adf9644440e0d007853e7fdfd67b6a59248f4fbb -> trunk/adf9644440e0d007853e7fdfd67b6a59248f4fbb 2025-07-17T08:34:16.1032466Z * [new tag] trunk/ae0f1f89847564b04bdc43395e664e48f08aaca2 -> trunk/ae0f1f89847564b04bdc43395e664e48f08aaca2 2025-07-17T08:34:16.1034573Z * [new tag] trunk/ae1094b72b7db368f292ed6033de5b495dcdc878 -> trunk/ae1094b72b7db368f292ed6033de5b495dcdc878 2025-07-17T08:34:16.1036726Z * [new tag] trunk/ae86e8f6c829a3cfa9204949156fce2d048c919b -> trunk/ae86e8f6c829a3cfa9204949156fce2d048c919b 2025-07-17T08:34:16.1038856Z * [new tag] trunk/aec569da2325c09487ab659a0f28c3b51e60d779 -> trunk/aec569da2325c09487ab659a0f28c3b51e60d779 2025-07-17T08:34:16.1040980Z * [new tag] trunk/aee2bfc5bae2974f5a3023d51dfa8e3e641144cc -> trunk/aee2bfc5bae2974f5a3023d51dfa8e3e641144cc 2025-07-17T08:34:16.1043012Z * [new tag] trunk/aeffb68d3466635e4e95c50bffd7dfebaba94da2 -> trunk/aeffb68d3466635e4e95c50bffd7dfebaba94da2 2025-07-17T08:34:16.1045095Z * [new tag] trunk/af284b45d5cbfd19d168bf1d118b3a40cd8b3a92 -> trunk/af284b45d5cbfd19d168bf1d118b3a40cd8b3a92 2025-07-17T08:34:16.1047221Z * [new tag] trunk/af3d06909400413801b726a96f3ff52b5ee9ebb5 -> trunk/af3d06909400413801b726a96f3ff52b5ee9ebb5 2025-07-17T08:34:16.1049291Z * [new tag] trunk/af9c92b4cb9f406129dfd8c64c082c0aaf7c723f -> trunk/af9c92b4cb9f406129dfd8c64c082c0aaf7c723f 2025-07-17T08:34:16.1051292Z * [new tag] trunk/aff9c1eec599b8fa97240d192534b0f650c16b1b -> trunk/aff9c1eec599b8fa97240d192534b0f650c16b1b 2025-07-17T08:34:16.1053366Z * [new tag] trunk/b00b641ff1fbfa8f4f6152ffc631e0d24145a7a8 -> trunk/b00b641ff1fbfa8f4f6152ffc631e0d24145a7a8 2025-07-17T08:34:16.1055282Z * [new tag] trunk/b020971e7806bba39aecf636e59e743911831ad8 -> trunk/b020971e7806bba39aecf636e59e743911831ad8 2025-07-17T08:34:16.1057664Z * [new tag] trunk/b0556110e58e3bcf2c872b933e4fd4a0d34398ad -> trunk/b0556110e58e3bcf2c872b933e4fd4a0d34398ad 2025-07-17T08:34:16.1059729Z * [new tag] trunk/b07725a9516028a485153c4b5356b3e33b990f81 -> trunk/b07725a9516028a485153c4b5356b3e33b990f81 2025-07-17T08:34:16.1061874Z * [new tag] trunk/b096341963c4af3b2b0d7f598eba1cdc1c74fb6e -> trunk/b096341963c4af3b2b0d7f598eba1cdc1c74fb6e 2025-07-17T08:34:16.1064029Z * [new tag] trunk/b09bd414a6ccba158c09f586a278051588d90936 -> trunk/b09bd414a6ccba158c09f586a278051588d90936 2025-07-17T08:34:16.1066235Z * [new tag] trunk/b0fbbef1361ccaab8a5aec8e7cd62150e7b361de -> trunk/b0fbbef1361ccaab8a5aec8e7cd62150e7b361de 2025-07-17T08:34:16.1068281Z * [new tag] trunk/b146ca74f01df3cf711fd0f855e05805e490156c -> trunk/b146ca74f01df3cf711fd0f855e05805e490156c 2025-07-17T08:34:16.1070414Z * [new tag] trunk/b146e1a264960e5fb79cf4621ebe4b1345db3e64 -> trunk/b146e1a264960e5fb79cf4621ebe4b1345db3e64 2025-07-17T08:34:16.1072414Z * [new tag] trunk/b147b6c0e31d43a405416743fde1b0b606aa3839 -> trunk/b147b6c0e31d43a405416743fde1b0b606aa3839 2025-07-17T08:34:16.1074473Z * [new tag] trunk/b1713c665516d8355f5491d10b31f98c74ce27f1 -> trunk/b1713c665516d8355f5491d10b31f98c74ce27f1 2025-07-17T08:34:16.1076576Z * [new tag] trunk/b1a54fab9bcb0cc167773f9a885d4170447e1c68 -> trunk/b1a54fab9bcb0cc167773f9a885d4170447e1c68 2025-07-17T08:34:16.1078610Z * [new tag] trunk/b1b8e57cda03bd016594a4fc4944fa5e7f9dd961 -> trunk/b1b8e57cda03bd016594a4fc4944fa5e7f9dd961 2025-07-17T08:34:16.1080905Z * [new tag] trunk/b1d62febd03ac421197d5516596f98d3c46e9b44 -> trunk/b1d62febd03ac421197d5516596f98d3c46e9b44 2025-07-17T08:34:16.1082960Z * [new tag] trunk/b221be9140689d180ec339db05c5e235a95949d3 -> trunk/b221be9140689d180ec339db05c5e235a95949d3 2025-07-17T08:34:16.1085117Z * [new tag] trunk/b22b93a6babf86e823e979528bc0aab88fb7a012 -> trunk/b22b93a6babf86e823e979528bc0aab88fb7a012 2025-07-17T08:34:16.1087190Z * [new tag] trunk/b26da7741be37693ab1cd21115f3fca15b1cdb6b -> trunk/b26da7741be37693ab1cd21115f3fca15b1cdb6b 2025-07-17T08:34:16.1089342Z * [new tag] trunk/b2d473c8f8a6d0677940c174b38511f5ab3c3b65 -> trunk/b2d473c8f8a6d0677940c174b38511f5ab3c3b65 2025-07-17T08:34:16.1091473Z * [new tag] trunk/b2fc9cfea16c8eb52c1ce79b2032793dd1a545fb -> trunk/b2fc9cfea16c8eb52c1ce79b2032793dd1a545fb 2025-07-17T08:34:16.1093505Z * [new tag] trunk/b30e04b3c8a83e19dc9123f2f0229d80e208fb50 -> trunk/b30e04b3c8a83e19dc9123f2f0229d80e208fb50 2025-07-17T08:34:16.1095580Z * [new tag] trunk/b354328ecd6a313eab905ab6f74aed5ea2d3a2ce -> trunk/b354328ecd6a313eab905ab6f74aed5ea2d3a2ce 2025-07-17T08:34:16.1097769Z * [new tag] trunk/b359571c6043b40c4ae4fbb07135fd0f04902e21 -> trunk/b359571c6043b40c4ae4fbb07135fd0f04902e21 2025-07-17T08:34:16.1099882Z * [new tag] trunk/b3b4d28f4cf149cb7448c556d359ca852d815ab1 -> trunk/b3b4d28f4cf149cb7448c556d359ca852d815ab1 2025-07-17T08:34:16.1101937Z * [new tag] trunk/b40981c63078d99bb07afbdcfec1ce8f7519f26b -> trunk/b40981c63078d99bb07afbdcfec1ce8f7519f26b 2025-07-17T08:34:16.1103959Z * [new tag] trunk/b40c0b61eb5f5b6104cb732e709819e84518faa7 -> trunk/b40c0b61eb5f5b6104cb732e709819e84518faa7 2025-07-17T08:34:16.1107431Z * [new tag] trunk/b4228a94d11b1ba6599f443267824d2d918644f2 -> trunk/b4228a94d11b1ba6599f443267824d2d918644f2 2025-07-17T08:34:16.1109718Z * [new tag] trunk/b44306d3681d5b248e6b439d293ea0d5a8903a61 -> trunk/b44306d3681d5b248e6b439d293ea0d5a8903a61 2025-07-17T08:34:16.1111753Z * [new tag] trunk/b4442f42a93390760bb923cbe13b80993f5e8e78 -> trunk/b4442f42a93390760bb923cbe13b80993f5e8e78 2025-07-17T08:34:16.1113735Z * [new tag] trunk/b4476ca378be50034bd5cdc1eaa95104337c998a -> trunk/b4476ca378be50034bd5cdc1eaa95104337c998a 2025-07-17T08:34:16.1116057Z * [new tag] trunk/b487003182b7f2d6697064f184515369bf6c8cce -> trunk/b487003182b7f2d6697064f184515369bf6c8cce 2025-07-17T08:34:16.1117978Z * [new tag] trunk/b49edc0d6c72f744d744a026b0ce0d689e34b83b -> trunk/b49edc0d6c72f744d744a026b0ce0d689e34b83b 2025-07-17T08:34:16.1120134Z * [new tag] trunk/b4e3c9ea34cb607324639cd0bb0129740c300721 -> trunk/b4e3c9ea34cb607324639cd0bb0129740c300721 2025-07-17T08:34:16.1122165Z * [new tag] trunk/b4fc42ca807efc1a1b78f52d5481711a4e8bbea3 -> trunk/b4fc42ca807efc1a1b78f52d5481711a4e8bbea3 2025-07-17T08:34:16.1124228Z * [new tag] trunk/b50075343aeb519d135e00c44ff3577eaa25c61b -> trunk/b50075343aeb519d135e00c44ff3577eaa25c61b 2025-07-17T08:34:16.1126410Z * [new tag] trunk/b54eac2a5ed31106393bbc338de8637817809a1f -> trunk/b54eac2a5ed31106393bbc338de8637817809a1f 2025-07-17T08:34:16.1128646Z * [new tag] trunk/b5bfbba1841da810305262c7f47ee2dae54f335e -> trunk/b5bfbba1841da810305262c7f47ee2dae54f335e 2025-07-17T08:34:16.1130705Z * [new tag] trunk/b5c8b8d09f006b1b2911858882a56dfe6e325f36 -> trunk/b5c8b8d09f006b1b2911858882a56dfe6e325f36 2025-07-17T08:34:16.1132887Z * [new tag] trunk/b5ce77c1f5964293299eb1366f341872a4e47fa6 -> trunk/b5ce77c1f5964293299eb1366f341872a4e47fa6 2025-07-17T08:34:16.1134946Z * [new tag] trunk/b60569ed946ddcc267150a675916d68d7cac9085 -> trunk/b60569ed946ddcc267150a675916d68d7cac9085 2025-07-17T08:34:16.1137020Z * [new tag] trunk/b6188174795956f959feae0cbc33cbdb7901c4b6 -> trunk/b6188174795956f959feae0cbc33cbdb7901c4b6 2025-07-17T08:34:16.1139160Z * [new tag] trunk/b642a5c118baf4cd47dd7fe0190b83e04cee960f -> trunk/b642a5c118baf4cd47dd7fe0190b83e04cee960f 2025-07-17T08:34:16.1141186Z * [new tag] trunk/b6454a9058f2e50be9a3c26c128fec843b09c154 -> trunk/b6454a9058f2e50be9a3c26c128fec843b09c154 2025-07-17T08:34:16.1143427Z * [new tag] trunk/b6add8c8ba927a9f1687134ec2734b02a6ccb3c0 -> trunk/b6add8c8ba927a9f1687134ec2734b02a6ccb3c0 2025-07-17T08:34:16.1145709Z * [new tag] trunk/b6c00dfe249a7bfc1d61a322d5bc30f164353abf -> trunk/b6c00dfe249a7bfc1d61a322d5bc30f164353abf 2025-07-17T08:34:16.1147884Z * [new tag] trunk/b6e625e34f358c71b62409f96dc1e22e4791beef -> trunk/b6e625e34f358c71b62409f96dc1e22e4791beef 2025-07-17T08:34:16.1150156Z * [new tag] trunk/b6f84b3b0fef781653911420253dcff6767197dc -> trunk/b6f84b3b0fef781653911420253dcff6767197dc 2025-07-17T08:34:16.1152316Z * [new tag] trunk/b754b1fa43d20f5b31e17c396487ab56991912da -> trunk/b754b1fa43d20f5b31e17c396487ab56991912da 2025-07-17T08:34:16.1154525Z * [new tag] trunk/b7860c7863df9139bceabf5b0c186e6e4287aa4c -> trunk/b7860c7863df9139bceabf5b0c186e6e4287aa4c 2025-07-17T08:34:16.1156537Z * [new tag] trunk/b7a73a2cdbbe46e04aa145fb8c1615e608dcbde9 -> trunk/b7a73a2cdbbe46e04aa145fb8c1615e608dcbde9 2025-07-17T08:34:16.1158598Z * [new tag] trunk/b7b1109f49f5d0bd6145ae47c5c7d7d18c5659b0 -> trunk/b7b1109f49f5d0bd6145ae47c5c7d7d18c5659b0 2025-07-17T08:34:16.1160877Z * [new tag] trunk/b7def5ff1ca72fbb06350ffc75117efc68e149fb -> trunk/b7def5ff1ca72fbb06350ffc75117efc68e149fb 2025-07-17T08:34:16.1162909Z * [new tag] trunk/b83d8827bcd63501d7298267d94d103bf591c6c2 -> trunk/b83d8827bcd63501d7298267d94d103bf591c6c2 2025-07-17T08:34:16.1164983Z * [new tag] trunk/b85f10ea5006e8ae8fc769f48659ab7ad5eafb69 -> trunk/b85f10ea5006e8ae8fc769f48659ab7ad5eafb69 2025-07-17T08:34:16.1167107Z * [new tag] trunk/b86d5cef68d56f3924dc199424e65904a32d0743 -> trunk/b86d5cef68d56f3924dc199424e65904a32d0743 2025-07-17T08:34:16.1169264Z * [new tag] trunk/b878ca0c91bdccbdd907c15a01e5dcf249f0783c -> trunk/b878ca0c91bdccbdd907c15a01e5dcf249f0783c 2025-07-17T08:34:16.1171534Z * [new tag] trunk/b8ace6f95105751d0535cb0ce0d2c8f656c8b96c -> trunk/b8ace6f95105751d0535cb0ce0d2c8f656c8b96c 2025-07-17T08:34:16.1173463Z * [new tag] trunk/b8aee84fb9692f1a46c8c2a0ae6685df440f6e8f -> trunk/b8aee84fb9692f1a46c8c2a0ae6685df440f6e8f 2025-07-17T08:34:16.1175521Z * [new tag] trunk/b8bc2c2660e84034ff15232e2161e3ef9a6656d0 -> trunk/b8bc2c2660e84034ff15232e2161e3ef9a6656d0 2025-07-17T08:34:16.1177572Z * [new tag] trunk/b8c2d4c259b16f3dc044d049d964c44907ccecb6 -> trunk/b8c2d4c259b16f3dc044d049d964c44907ccecb6 2025-07-17T08:34:16.1179781Z * [new tag] trunk/b8d96c3f78a27e193f4fa9580f8d28298c8180e3 -> trunk/b8d96c3f78a27e193f4fa9580f8d28298c8180e3 2025-07-17T08:34:16.1181824Z * [new tag] trunk/b8fc5e0c0dda5da8315f9bc108a83a0f92252513 -> trunk/b8fc5e0c0dda5da8315f9bc108a83a0f92252513 2025-07-17T08:34:16.1183987Z * [new tag] trunk/b916d8a583cf4fb018c44eaf5efefa9ca76da366 -> trunk/b916d8a583cf4fb018c44eaf5efefa9ca76da366 2025-07-17T08:34:16.1186190Z * [new tag] trunk/b95dadd7170626273bb03b1264f04f3f051908da -> trunk/b95dadd7170626273bb03b1264f04f3f051908da 2025-07-17T08:34:16.1188272Z * [new tag] trunk/b981fb6744d034a0b2a0baf7e138cfb8dc83ca96 -> trunk/b981fb6744d034a0b2a0baf7e138cfb8dc83ca96 2025-07-17T08:34:16.1190321Z * [new tag] trunk/b9afdd9bcc738697c6eefc90899508ab783bf6ab -> trunk/b9afdd9bcc738697c6eefc90899508ab783bf6ab 2025-07-17T08:34:16.1192524Z * [new tag] trunk/b9b84d8011b08ac62cabf9100043c65863372fea -> trunk/b9b84d8011b08ac62cabf9100043c65863372fea 2025-07-17T08:34:16.1194614Z * [new tag] trunk/b9dc2fa4f7aa237a19248705abf82f5eae662182 -> trunk/b9dc2fa4f7aa237a19248705abf82f5eae662182 2025-07-17T08:34:16.1196862Z * [new tag] trunk/ba0d0de5e652650f8a59c85fc25a90f4ed9e2dc1 -> trunk/ba0d0de5e652650f8a59c85fc25a90f4ed9e2dc1 2025-07-17T08:34:16.1198938Z * [new tag] trunk/bb1f3d1a55e3f8eafb279995824c1ef87f24b341 -> trunk/bb1f3d1a55e3f8eafb279995824c1ef87f24b341 2025-07-17T08:34:16.1201174Z * [new tag] trunk/bb3c911c2d64ccad03349e50a0a220a2889ae85c -> trunk/bb3c911c2d64ccad03349e50a0a220a2889ae85c 2025-07-17T08:34:16.1203417Z * [new tag] trunk/bb462a6237c163774c99e01462703ebce55f4589 -> trunk/bb462a6237c163774c99e01462703ebce55f4589 2025-07-17T08:34:16.1205322Z * [new tag] trunk/bb476310a456b1fd418c79573ed34ad68b4871d4 -> trunk/bb476310a456b1fd418c79573ed34ad68b4871d4 2025-07-17T08:34:16.1207547Z * [new tag] trunk/bbb930aba2e769e3ed981f2a035133891de36dae -> trunk/bbb930aba2e769e3ed981f2a035133891de36dae 2025-07-17T08:34:16.1209660Z * [new tag] trunk/bbbced94a43cf764ddfe719e7d4c161a3992830c -> trunk/bbbced94a43cf764ddfe719e7d4c161a3992830c 2025-07-17T08:34:16.1211782Z * [new tag] trunk/bbe681ed510227ce05f99db2a36c1af5c2cc302b -> trunk/bbe681ed510227ce05f99db2a36c1af5c2cc302b 2025-07-17T08:34:16.1213875Z * [new tag] trunk/bbf1a6feac0e88772be103a1b159b871b5c00b4a -> trunk/bbf1a6feac0e88772be103a1b159b871b5c00b4a 2025-07-17T08:34:16.1216045Z * [new tag] trunk/bc3972b80a7abe85036f48b610532fce39ea5097 -> trunk/bc3972b80a7abe85036f48b610532fce39ea5097 2025-07-17T08:34:16.1218210Z * [new tag] trunk/bc5a11b58180d40157175f45c69d60a9b9961315 -> trunk/bc5a11b58180d40157175f45c69d60a9b9961315 2025-07-17T08:34:16.1220351Z * [new tag] trunk/bc65253369933160a2da3fc786d027a572faf6b7 -> trunk/bc65253369933160a2da3fc786d027a572faf6b7 2025-07-17T08:34:16.1222389Z * [new tag] trunk/bc6e0661a6ec7e536bee60b9c929f71643bb6c89 -> trunk/bc6e0661a6ec7e536bee60b9c929f71643bb6c89 2025-07-17T08:34:16.1224474Z * [new tag] trunk/bc9091a524a1ebe4de16af4dd8f442db7d1cb138 -> trunk/bc9091a524a1ebe4de16af4dd8f442db7d1cb138 2025-07-17T08:34:16.1226755Z * [new tag] trunk/bc9bd2a766ae8cf2aba8f3d0153cd8ecd05b4465 -> trunk/bc9bd2a766ae8cf2aba8f3d0153cd8ecd05b4465 2025-07-17T08:34:16.1229171Z * [new tag] trunk/bcad962550f2bfd850684250a3c881c9f38ad601 -> trunk/bcad962550f2bfd850684250a3c881c9f38ad601 2025-07-17T08:34:16.1231075Z * [new tag] trunk/bcc98bb2a4dd11ac696082731f8980b72deb6750 -> trunk/bcc98bb2a4dd11ac696082731f8980b72deb6750 2025-07-17T08:34:16.1233176Z * [new tag] trunk/bccb8473fed94dbc6f1392d0c5b4a51150ee4a9a -> trunk/bccb8473fed94dbc6f1392d0c5b4a51150ee4a9a 2025-07-17T08:34:16.1235366Z * [new tag] trunk/bcf50636ba1b93a833267c645d887888df06e9ea -> trunk/bcf50636ba1b93a833267c645d887888df06e9ea 2025-07-17T08:34:16.1237434Z * [new tag] trunk/bd364c901d5b20500ec5cbe275e93c955809d900 -> trunk/bd364c901d5b20500ec5cbe275e93c955809d900 2025-07-17T08:34:16.1239489Z * [new tag] trunk/bd3c32916cf5f85ddb9dd3c36e7311adfa8808af -> trunk/bd3c32916cf5f85ddb9dd3c36e7311adfa8808af 2025-07-17T08:34:16.1241592Z * [new tag] trunk/bd6b5fddbf5fb0b603ab8a7428079d9a86cf532a -> trunk/bd6b5fddbf5fb0b603ab8a7428079d9a86cf532a 2025-07-17T08:34:16.1243624Z * [new tag] trunk/bdacf08b8682b9fbe3a8656a53b1f8b1cb007fd8 -> trunk/bdacf08b8682b9fbe3a8656a53b1f8b1cb007fd8 2025-07-17T08:34:16.1245878Z * [new tag] trunk/bdb1553b77bb28df580ea41d726417ac91028ec6 -> trunk/bdb1553b77bb28df580ea41d726417ac91028ec6 2025-07-17T08:34:16.1247907Z * [new tag] trunk/bdb78191662c01ab1263108febac29a8560337d0 -> trunk/bdb78191662c01ab1263108febac29a8560337d0 2025-07-17T08:34:16.1250024Z * [new tag] trunk/bdbf2792a8f15907f6bbf231f73dab0e8efe1c50 -> trunk/bdbf2792a8f15907f6bbf231f73dab0e8efe1c50 2025-07-17T08:34:16.1252189Z * [new tag] trunk/be124a61a4933603795644d068d2c0e5f444e766 -> trunk/be124a61a4933603795644d068d2c0e5f444e766 2025-07-17T08:34:16.1254303Z * [new tag] trunk/be2ab96347a1e7206a57f69b58263c4455ff8f76 -> trunk/be2ab96347a1e7206a57f69b58263c4455ff8f76 2025-07-17T08:34:16.1256509Z * [new tag] trunk/be2ad70cfa1360da5c23a04ff6ca3480fa02f278 -> trunk/be2ad70cfa1360da5c23a04ff6ca3480fa02f278 2025-07-17T08:34:16.1258579Z * [new tag] trunk/be2e43264d0bce254efabcc60c368d1418bde57f -> trunk/be2e43264d0bce254efabcc60c368d1418bde57f 2025-07-17T08:34:16.1260669Z * [new tag] trunk/be56a8d7ac0dc72f6946354d7ac5be9ece0f1c35 -> trunk/be56a8d7ac0dc72f6946354d7ac5be9ece0f1c35 2025-07-17T08:34:16.1262740Z * [new tag] trunk/beb52f5c0ac8efddeafa862f274bc247db989695 -> trunk/beb52f5c0ac8efddeafa862f274bc247db989695 2025-07-17T08:34:16.1265031Z * [new tag] trunk/bee93f9f0d16f4f563812bb5c16e862de15724c1 -> trunk/bee93f9f0d16f4f563812bb5c16e862de15724c1 2025-07-17T08:34:16.1267171Z * [new tag] trunk/beed033b6e6ac57c0b4a1f47eb436e115a52e41b -> trunk/beed033b6e6ac57c0b4a1f47eb436e115a52e41b 2025-07-17T08:34:16.1269447Z * [new tag] trunk/bf06190e21fdf539c13e1ec01271653d0729733a -> trunk/bf06190e21fdf539c13e1ec01271653d0729733a 2025-07-17T08:34:16.1271596Z * [new tag] trunk/bf1ebe0531e1b0390ea09d66ac78500d3c6d3a15 -> trunk/bf1ebe0531e1b0390ea09d66ac78500d3c6d3a15 2025-07-17T08:34:16.1273640Z * [new tag] trunk/bf50d715539acedcb31a6d8f787149eff6213fdb -> trunk/bf50d715539acedcb31a6d8f787149eff6213fdb 2025-07-17T08:34:16.1275771Z * [new tag] trunk/bf798a2f016ca0001750436150e7a2bdb2676d1a -> trunk/bf798a2f016ca0001750436150e7a2bdb2676d1a 2025-07-17T08:34:16.1277695Z * [new tag] trunk/bf7e290854b7f0ab3fb89251d0100821f1b3a70e -> trunk/bf7e290854b7f0ab3fb89251d0100821f1b3a70e 2025-07-17T08:34:16.1280199Z * [new tag] trunk/bf897b4cea2b7481499a81afa30bfc69a8e685c4 -> trunk/bf897b4cea2b7481499a81afa30bfc69a8e685c4 2025-07-17T08:34:16.1282264Z * [new tag] trunk/bfcababbcb95ec42046737dbcf61f8e258075ace -> trunk/bfcababbcb95ec42046737dbcf61f8e258075ace 2025-07-17T08:34:16.1284528Z * [new tag] trunk/bfccfa0b31221d5df0b263de5a41fb9f7c84b97d -> trunk/bfccfa0b31221d5df0b263de5a41fb9f7c84b97d 2025-07-17T08:34:16.1286415Z * [new tag] trunk/c038719731abdcd415a5e82c5f3826f2358229fc -> trunk/c038719731abdcd415a5e82c5f3826f2358229fc 2025-07-17T08:34:16.1288450Z * [new tag] trunk/c04a4e709410936c12cdc9db5aaf47164f74a367 -> trunk/c04a4e709410936c12cdc9db5aaf47164f74a367 2025-07-17T08:34:16.1290691Z * [new tag] trunk/c062550a3598d27c2d6572db7c0f4ff90a84cc84 -> trunk/c062550a3598d27c2d6572db7c0f4ff90a84cc84 2025-07-17T08:34:16.1292962Z * [new tag] trunk/c06c2569ee53c169c96f42b45bdb64f390d2f23b -> trunk/c06c2569ee53c169c96f42b45bdb64f390d2f23b 2025-07-17T08:34:16.1295005Z * [new tag] trunk/c09b05487877a3f8b239523b3806f5dd9fc82051 -> trunk/c09b05487877a3f8b239523b3806f5dd9fc82051 2025-07-17T08:34:16.1297210Z * [new tag] trunk/c09cf29d7d010c0547f2f6771921a0ee2ec4904b -> trunk/c09cf29d7d010c0547f2f6771921a0ee2ec4904b 2025-07-17T08:34:16.1299247Z * [new tag] trunk/c09eba877f9c16908b3a925ef694604c1c761b85 -> trunk/c09eba877f9c16908b3a925ef694604c1c761b85 2025-07-17T08:34:16.1301342Z * [new tag] trunk/c0e155a8d230b04f186d1b7540b01213be2b9fc6 -> trunk/c0e155a8d230b04f186d1b7540b01213be2b9fc6 2025-07-17T08:34:16.1304036Z * [new tag] trunk/c0ee01c2fb54e2ae924270f7fd53069915a61a50 -> trunk/c0ee01c2fb54e2ae924270f7fd53069915a61a50 2025-07-17T08:34:16.1306200Z * [new tag] trunk/c10339559de1464c2c5aeb16649c382eefcfb572 -> trunk/c10339559de1464c2c5aeb16649c382eefcfb572 2025-07-17T08:34:16.1308410Z * [new tag] trunk/c11888e7a6557ff64e24ca06e22946c116a7c1e4 -> trunk/c11888e7a6557ff64e24ca06e22946c116a7c1e4 2025-07-17T08:34:16.1310507Z * [new tag] trunk/c13e725edd8dd21406c629bf625f2d6c59ceedd1 -> trunk/c13e725edd8dd21406c629bf625f2d6c59ceedd1 2025-07-17T08:34:16.1312624Z * [new tag] trunk/c14110056f1b6e989850f20ae56d47fbc775e890 -> trunk/c14110056f1b6e989850f20ae56d47fbc775e890 2025-07-17T08:34:16.1314744Z * [new tag] trunk/c1446e1e9d2f5a1bae5fb3d58e58f16c15bb15c3 -> trunk/c1446e1e9d2f5a1bae5fb3d58e58f16c15bb15c3 2025-07-17T08:34:16.1316893Z * [new tag] trunk/c165b36a31585d4e061a84e1977428afd931b82d -> trunk/c165b36a31585d4e061a84e1977428afd931b82d 2025-07-17T08:34:16.1318848Z * [new tag] trunk/c174f3a6a55864cedb8f6d9014e9b8cadf91186b -> trunk/c174f3a6a55864cedb8f6d9014e9b8cadf91186b 2025-07-17T08:34:16.1321107Z * [new tag] trunk/c177abd217ecef5cc096d50adbf5990525714dd3 -> trunk/c177abd217ecef5cc096d50adbf5990525714dd3 2025-07-17T08:34:16.1323153Z * [new tag] trunk/c199a4d0fd03ab3f7e36c6133839cfaf34ba59da -> trunk/c199a4d0fd03ab3f7e36c6133839cfaf34ba59da 2025-07-17T08:34:16.1325241Z * [new tag] trunk/c1a629f76d0afc2ddb4a5c7c1925caa1d12b5a5e -> trunk/c1a629f76d0afc2ddb4a5c7c1925caa1d12b5a5e 2025-07-17T08:34:16.1327291Z * [new tag] trunk/c1ad4b8e7a16f54c35a3908b56ed7d9f95eef586 -> trunk/c1ad4b8e7a16f54c35a3908b56ed7d9f95eef586 2025-07-17T08:34:16.1329388Z * [new tag] trunk/c1ae768baa9598ea97f301999a227b34a2efdecf -> trunk/c1ae768baa9598ea97f301999a227b34a2efdecf 2025-07-17T08:34:16.1331491Z * [new tag] trunk/c1cbaca7fd9937c0a089d98aa69066daf35c898f -> trunk/c1cbaca7fd9937c0a089d98aa69066daf35c898f 2025-07-17T08:34:16.1333676Z * [new tag] trunk/c1f531f0b0e6faf443d90f8de2936e866c8c27c2 -> trunk/c1f531f0b0e6faf443d90f8de2936e866c8c27c2 2025-07-17T08:34:16.1335754Z * [new tag] trunk/c202a7329ad798da676762a8af2aa588f882d288 -> trunk/c202a7329ad798da676762a8af2aa588f882d288 2025-07-17T08:34:16.1337987Z * [new tag] trunk/c2185dc4a5626848df37cad214b73d5ae7dd4f17 -> trunk/c2185dc4a5626848df37cad214b73d5ae7dd4f17 2025-07-17T08:34:16.1340202Z * [new tag] trunk/c219dbd2fc70227ba543c98e9740a84723ba36da -> trunk/c219dbd2fc70227ba543c98e9740a84723ba36da 2025-07-17T08:34:16.1342103Z * [new tag] trunk/c2510fcd86152028c3e6cf483740b177a10ac9b9 -> trunk/c2510fcd86152028c3e6cf483740b177a10ac9b9 2025-07-17T08:34:16.1344245Z * [new tag] trunk/c26ce593d8737cab4b55483b62956a6fea9e4375 -> trunk/c26ce593d8737cab4b55483b62956a6fea9e4375 2025-07-17T08:34:16.1348528Z * [new tag] trunk/c27f83dd91ff3599b5157866e6be014c20b967a0 -> trunk/c27f83dd91ff3599b5157866e6be014c20b967a0 2025-07-17T08:34:16.1350201Z * [new tag] trunk/c28e74e45743feac10559c30dbf71cc35bc12ccb -> trunk/c28e74e45743feac10559c30dbf71cc35bc12ccb 2025-07-17T08:34:16.1352196Z * [new tag] trunk/c2beeadeb40e927c51bc5bcd409b3f28374a5190 -> trunk/c2beeadeb40e927c51bc5bcd409b3f28374a5190 2025-07-17T08:34:16.1354347Z * [new tag] trunk/c2d1b225e62bdf3adbba91891f53bc60315adaac -> trunk/c2d1b225e62bdf3adbba91891f53bc60315adaac 2025-07-17T08:34:16.1356519Z * [new tag] trunk/c2f4cc59a70bfd7d7e46e9b5596bae8e4ae2cf9c -> trunk/c2f4cc59a70bfd7d7e46e9b5596bae8e4ae2cf9c 2025-07-17T08:34:16.1358563Z * [new tag] trunk/c329a8f19cc94cb7f9d3fc814484cded138ee3ca -> trunk/c329a8f19cc94cb7f9d3fc814484cded138ee3ca 2025-07-17T08:34:16.1360618Z * [new tag] trunk/c37ddcaefbe9b877e1816ce97dedb8ad26d09450 -> trunk/c37ddcaefbe9b877e1816ce97dedb8ad26d09450 2025-07-17T08:34:16.1362755Z * [new tag] trunk/c3ecabf0593066750156989fc75acbeeaddf0791 -> trunk/c3ecabf0593066750156989fc75acbeeaddf0791 2025-07-17T08:34:16.1364881Z * [new tag] trunk/c48d0f4643b7a69ebe24069e932ce1465a31cdbe -> trunk/c48d0f4643b7a69ebe24069e932ce1465a31cdbe 2025-07-17T08:34:16.1366983Z * [new tag] trunk/c4b93e6579c8e2f1252067c4923cbb859c476fb4 -> trunk/c4b93e6579c8e2f1252067c4923cbb859c476fb4 2025-07-17T08:34:16.1369033Z * [new tag] trunk/c4cdcda754e2b73b030902dd06fb651c7a026510 -> trunk/c4cdcda754e2b73b030902dd06fb651c7a026510 2025-07-17T08:34:16.1371149Z * [new tag] trunk/c515385b0ac4a94deef652159e71fe0912615d14 -> trunk/c515385b0ac4a94deef652159e71fe0912615d14 2025-07-17T08:34:16.1373433Z * [new tag] trunk/c54778625ec45b94eb97f52da3ad6c2eb3d3c3c9 -> trunk/c54778625ec45b94eb97f52da3ad6c2eb3d3c3c9 2025-07-17T08:34:16.1375445Z * [new tag] trunk/c553c55be76daaf70d977a4ac7664b46cbcfa7c4 -> trunk/c553c55be76daaf70d977a4ac7664b46cbcfa7c4 2025-07-17T08:34:16.1377486Z * [new tag] trunk/c5589074e64b28afd737174ae1e9c2ba0b925e56 -> trunk/c5589074e64b28afd737174ae1e9c2ba0b925e56 2025-07-17T08:34:16.1379578Z * [new tag] trunk/c55eef79f8880e4b610a0ca6f6131e690dc948dd -> trunk/c55eef79f8880e4b610a0ca6f6131e690dc948dd 2025-07-17T08:34:16.1381726Z * [new tag] trunk/c5a4fe9c17bc20eb46b15fda554d17030bb6a5b9 -> trunk/c5a4fe9c17bc20eb46b15fda554d17030bb6a5b9 2025-07-17T08:34:16.1383733Z * [new tag] trunk/c5b46b5408e136a5a3060b11a96afa51bd009fd5 -> trunk/c5b46b5408e136a5a3060b11a96afa51bd009fd5 2025-07-17T08:34:16.1385900Z * [new tag] trunk/c5d00e150ad503510bf28adf09f69201aeb94f5c -> trunk/c5d00e150ad503510bf28adf09f69201aeb94f5c 2025-07-17T08:34:16.1387946Z * [new tag] trunk/c5d3e7a4ff460eed70b8443485a7e3568e87aee9 -> trunk/c5d3e7a4ff460eed70b8443485a7e3568e87aee9 2025-07-17T08:34:16.1390140Z * [new tag] trunk/c60327ba74f4db232a2832f2c7ca4b2db43a3132 -> trunk/c60327ba74f4db232a2832f2c7ca4b2db43a3132 2025-07-17T08:34:16.1392296Z * [new tag] trunk/c60d8188d23801d44343c96746ec2e7d2971c5d7 -> trunk/c60d8188d23801d44343c96746ec2e7d2971c5d7 2025-07-17T08:34:16.1394340Z * [new tag] trunk/c620d0b5c7e8679413d620624725471223ce8359 -> trunk/c620d0b5c7e8679413d620624725471223ce8359 2025-07-17T08:34:16.1396308Z * [new tag] trunk/c68af9af1b3652a8e25bd6d0ff8dae89f206a81a -> trunk/c68af9af1b3652a8e25bd6d0ff8dae89f206a81a 2025-07-17T08:34:16.1398527Z * [new tag] trunk/c6a27bae3656021720074520ac6dad9cebe83ff5 -> trunk/c6a27bae3656021720074520ac6dad9cebe83ff5 2025-07-17T08:34:16.1401320Z * [new tag] trunk/c6b4f98625bb6b22bb9a60112a6d58e684a97e1b -> trunk/c6b4f98625bb6b22bb9a60112a6d58e684a97e1b 2025-07-17T08:34:16.1403677Z * [new tag] trunk/c74fd35050a7241f0c439501ef735aa6cdde751f -> trunk/c74fd35050a7241f0c439501ef735aa6cdde751f 2025-07-17T08:34:16.1405737Z * [new tag] trunk/c75c732481915c25295a0ae6321d39aa314eaa93 -> trunk/c75c732481915c25295a0ae6321d39aa314eaa93 2025-07-17T08:34:16.1407927Z * [new tag] trunk/c78bbdf4102d2c13bf6aa1abe4352aa7bca401ca -> trunk/c78bbdf4102d2c13bf6aa1abe4352aa7bca401ca 2025-07-17T08:34:16.1410050Z * [new tag] trunk/c78fce9e79b79686b87f4007cbaec819bdd0223f -> trunk/c78fce9e79b79686b87f4007cbaec819bdd0223f 2025-07-17T08:34:16.1412082Z * [new tag] trunk/c79c7bbe615265b6b3d7df39d6d5a68afd7d6b2a -> trunk/c79c7bbe615265b6b3d7df39d6d5a68afd7d6b2a 2025-07-17T08:34:16.1413999Z * [new tag] trunk/c7b6c98d1097bec9dc92bde2fe324aa126a5daa2 -> trunk/c7b6c98d1097bec9dc92bde2fe324aa126a5daa2 2025-07-17T08:34:16.1416135Z * [new tag] trunk/c808af514d59e25ea4a880c1e1e07d3232984e5d -> trunk/c808af514d59e25ea4a880c1e1e07d3232984e5d 2025-07-17T08:34:16.1418207Z * [new tag] trunk/c811f41cf58238b49e9c2f0a9fa00dcc471b2e10 -> trunk/c811f41cf58238b49e9c2f0a9fa00dcc471b2e10 2025-07-17T08:34:16.1420320Z * [new tag] trunk/c82a174ceab79f77ba18405dd263eb03692608fd -> trunk/c82a174ceab79f77ba18405dd263eb03692608fd 2025-07-17T08:34:16.1422490Z * [new tag] trunk/c83041cac2e9ca537b210b74876dbcee5e08ae9a -> trunk/c83041cac2e9ca537b210b74876dbcee5e08ae9a 2025-07-17T08:34:16.1424991Z * [new tag] trunk/c843909d9e32f92b2e31cf9b8f066daf311a6f18 -> trunk/c843909d9e32f92b2e31cf9b8f066daf311a6f18 2025-07-17T08:34:16.1427237Z * [new tag] trunk/c88e87f3553b7ef0b743fb030317fabb6fe2dc29 -> trunk/c88e87f3553b7ef0b743fb030317fabb6fe2dc29 2025-07-17T08:34:16.1429401Z * [new tag] trunk/c8c221c0b3abbb8b5e20138080644dd5f5cd0aa1 -> trunk/c8c221c0b3abbb8b5e20138080644dd5f5cd0aa1 2025-07-17T08:34:16.1431452Z * [new tag] trunk/c8c892b4a50fdad624753db5b8d2bbb0fc5bb110 -> trunk/c8c892b4a50fdad624753db5b8d2bbb0fc5bb110 2025-07-17T08:34:16.1433600Z * [new tag] trunk/c8d39a10457ea5d65184c6e8f037f46c5525d869 -> trunk/c8d39a10457ea5d65184c6e8f037f46c5525d869 2025-07-17T08:34:16.1435798Z * [new tag] trunk/c9174a20f75ba007b0f520326ced5c65d8a2b269 -> trunk/c9174a20f75ba007b0f520326ced5c65d8a2b269 2025-07-17T08:34:16.1437769Z * [new tag] trunk/c92f1075aaf3649f6368af2a3df9b5167f941b3f -> trunk/c92f1075aaf3649f6368af2a3df9b5167f941b3f 2025-07-17T08:34:16.1439901Z * [new tag] trunk/c9404faacb7c18af17086be26587b46e141afbcc -> trunk/c9404faacb7c18af17086be26587b46e141afbcc 2025-07-17T08:34:16.1442080Z * [new tag] trunk/c95705dac2da8134c946cdd573944632ef22f230 -> trunk/c95705dac2da8134c946cdd573944632ef22f230 2025-07-17T08:34:16.1444096Z * [new tag] trunk/c95f7fa874a3116f1067f9092456ee7281003614 -> trunk/c95f7fa874a3116f1067f9092456ee7281003614 2025-07-17T08:34:16.1446253Z * [new tag] trunk/c9a5bf09baa1a6776555e901331b1baefe6691bc -> trunk/c9a5bf09baa1a6776555e901331b1baefe6691bc 2025-07-17T08:34:16.1448338Z * [new tag] trunk/c9afcffed0870c211d2e8be422812d6a70aef3e2 -> trunk/c9afcffed0870c211d2e8be422812d6a70aef3e2 2025-07-17T08:34:16.1450482Z * [new tag] trunk/c9e9a0c82316ed59f6ef989a6000f0d5231b9b16 -> trunk/c9e9a0c82316ed59f6ef989a6000f0d5231b9b16 2025-07-17T08:34:16.1452536Z * [new tag] trunk/ca3cabd24ace1899ca4669431f5b0556c5ab9ebf -> trunk/ca3cabd24ace1899ca4669431f5b0556c5ab9ebf 2025-07-17T08:34:16.1454862Z * [new tag] trunk/ca5a40395d8b212dce0a57df01ca056eae55446f -> trunk/ca5a40395d8b212dce0a57df01ca056eae55446f 2025-07-17T08:34:16.1456705Z * [new tag] trunk/cadcb5d36802579441da3dbe3c2eecc74a0eae78 -> trunk/cadcb5d36802579441da3dbe3c2eecc74a0eae78 2025-07-17T08:34:16.1458980Z * [new tag] trunk/cb711c8fa04673d3490306e2b14539ab7dca3c23 -> trunk/cb711c8fa04673d3490306e2b14539ab7dca3c23 2025-07-17T08:34:16.1461107Z * [new tag] trunk/cb853945a77af54d9989ae7ac36d0007c0215b10 -> trunk/cb853945a77af54d9989ae7ac36d0007c0215b10 2025-07-17T08:34:16.1463053Z * [new tag] trunk/cb9b479f4f799eea058de9be3a0e09f977fc82da -> trunk/cb9b479f4f799eea058de9be3a0e09f977fc82da 2025-07-17T08:34:16.1465154Z * [new tag] trunk/cbafba57941c1d5f78640845320ac89157570b44 -> trunk/cbafba57941c1d5f78640845320ac89157570b44 2025-07-17T08:34:16.1467299Z * [new tag] trunk/cbcffce48a3422a5e9a2103888248c82d52d199c -> trunk/cbcffce48a3422a5e9a2103888248c82d52d199c 2025-07-17T08:34:16.1469406Z * [new tag] trunk/cc09d3a5ba2a5ae032078570e6123d523952ed22 -> trunk/cc09d3a5ba2a5ae032078570e6123d523952ed22 2025-07-17T08:34:16.1471574Z * [new tag] trunk/cc0faeb80fff17b3d170aa70041865aafb1790a9 -> trunk/cc0faeb80fff17b3d170aa70041865aafb1790a9 2025-07-17T08:34:16.1474512Z * [new tag] trunk/cc3ea2d84004b6348c77a285ba2639932b87c101 -> trunk/cc3ea2d84004b6348c77a285ba2639932b87c101 2025-07-17T08:34:16.1477440Z * [new tag] trunk/cc410d3761001499611bca6cf835239b86093791 -> trunk/cc410d3761001499611bca6cf835239b86093791 2025-07-17T08:34:16.1477916Z * [new tag] trunk/ccb1f687d63f0215ebcf3c1340c6682edeaccd28 -> trunk/ccb1f687d63f0215ebcf3c1340c6682edeaccd28 2025-07-17T08:34:16.1479941Z * [new tag] trunk/ccb67f39b4d75943cdc531d9032b3ba9235cfd56 -> trunk/ccb67f39b4d75943cdc531d9032b3ba9235cfd56 2025-07-17T08:34:16.1482112Z * [new tag] trunk/ccc6279b4086d55cd1f7e2d699473478610d8a7b -> trunk/ccc6279b4086d55cd1f7e2d699473478610d8a7b 2025-07-17T08:34:16.1484272Z * [new tag] trunk/cce4d213a67b7536dce431fd29c92f9791c9f81d -> trunk/cce4d213a67b7536dce431fd29c92f9791c9f81d 2025-07-17T08:34:16.1486342Z * [new tag] trunk/cd1a924dba145334aacb7c1b7276d6cef7ca41cc -> trunk/cd1a924dba145334aacb7c1b7276d6cef7ca41cc 2025-07-17T08:34:16.1488337Z * [new tag] trunk/cd361fc247a9abdbe9851867e31ac3cefcff299e -> trunk/cd361fc247a9abdbe9851867e31ac3cefcff299e 2025-07-17T08:34:16.1490465Z * [new tag] trunk/cd66ff80307862ef8e75520054ecd19a5eff9f7e -> trunk/cd66ff80307862ef8e75520054ecd19a5eff9f7e 2025-07-17T08:34:16.1492742Z * [new tag] trunk/cd75cf3cabedeac2ae4bbb266c05ca9b64181abe -> trunk/cd75cf3cabedeac2ae4bbb266c05ca9b64181abe 2025-07-17T08:34:16.1494889Z * [new tag] trunk/cd80f9a4c35a63ef415dbf6a361e4520a9cabf7b -> trunk/cd80f9a4c35a63ef415dbf6a361e4520a9cabf7b 2025-07-17T08:34:16.1496995Z * [new tag] trunk/cd82096973985042ed2eaa84e69fbf0f067f9301 -> trunk/cd82096973985042ed2eaa84e69fbf0f067f9301 2025-07-17T08:34:16.1499057Z * [new tag] trunk/cd995bfb2aac8891465809be3ce29543bd524287 -> trunk/cd995bfb2aac8891465809be3ce29543bd524287 2025-07-17T08:34:16.1501132Z * [new tag] trunk/cdb144fcf0aa4300c6de7158cb18ff7503d48492 -> trunk/cdb144fcf0aa4300c6de7158cb18ff7503d48492 2025-07-17T08:34:16.1503176Z * [new tag] trunk/cdfa33a328f2d3fdaeb645fde186194ddc2e66c0 -> trunk/cdfa33a328f2d3fdaeb645fde186194ddc2e66c0 2025-07-17T08:34:16.1505337Z * [new tag] trunk/ce1a07570d95cdd1543b9b2d45b3899c59e6802f -> trunk/ce1a07570d95cdd1543b9b2d45b3899c59e6802f 2025-07-17T08:34:16.1507506Z * [new tag] trunk/ce3406817d50b3357fa644784cc84ff167ce40ce -> trunk/ce3406817d50b3357fa644784cc84ff167ce40ce 2025-07-17T08:34:16.1509835Z * [new tag] trunk/ce44877961af7c8ec618f525853ce7edf3efa4eb -> trunk/ce44877961af7c8ec618f525853ce7edf3efa4eb 2025-07-17T08:34:16.1512144Z * [new tag] trunk/ce6e0523f9911c605860c4f5f278c2dad893dde9 -> trunk/ce6e0523f9911c605860c4f5f278c2dad893dde9 2025-07-17T08:34:16.1513922Z * [new tag] trunk/ce73b0c53f1f215345dcfe3953871a4aaf170a0c -> trunk/ce73b0c53f1f215345dcfe3953871a4aaf170a0c 2025-07-17T08:34:16.1515964Z * [new tag] trunk/ce79056471737557dcc64378985cd2b036e7322c -> trunk/ce79056471737557dcc64378985cd2b036e7322c 2025-07-17T08:34:16.1518090Z * [new tag] trunk/ce8180a61de405c519e00ac48eab8ff765ea71e6 -> trunk/ce8180a61de405c519e00ac48eab8ff765ea71e6 2025-07-17T08:34:16.1520247Z * [new tag] trunk/ce97a5dcfa3cb10c7805ff5cb44abd6a16b4ae8b -> trunk/ce97a5dcfa3cb10c7805ff5cb44abd6a16b4ae8b 2025-07-17T08:34:16.1522343Z * [new tag] trunk/ce9ba071fd29013e72100dd97728d01c860720d9 -> trunk/ce9ba071fd29013e72100dd97728d01c860720d9 2025-07-17T08:34:16.1524326Z * [new tag] trunk/cec264c8c6d74d4fbf5ee31983737b8b5e316dd0 -> trunk/cec264c8c6d74d4fbf5ee31983737b8b5e316dd0 2025-07-17T08:34:16.1526456Z * [new tag] trunk/cec59b76ca606c3e5d34ac0d0f9e0e22b8cfe5bb -> trunk/cec59b76ca606c3e5d34ac0d0f9e0e22b8cfe5bb 2025-07-17T08:34:16.1528561Z * [new tag] trunk/cf0749c92fdfcb8a583ea15f351add88c4d33618 -> trunk/cf0749c92fdfcb8a583ea15f351add88c4d33618 2025-07-17T08:34:16.1530943Z * [new tag] trunk/cf3247b74aaeb956b3c2b31d05e965a0aca5a8b4 -> trunk/cf3247b74aaeb956b3c2b31d05e965a0aca5a8b4 2025-07-17T08:34:16.1533044Z * [new tag] trunk/cf90c9f8d1632777ec5f4b6ccaa14bc5bf259e9c -> trunk/cf90c9f8d1632777ec5f4b6ccaa14bc5bf259e9c 2025-07-17T08:34:16.1535129Z * [new tag] trunk/cf9878d7a20b11d761934ae7732b01644ae137d6 -> trunk/cf9878d7a20b11d761934ae7732b01644ae137d6 2025-07-17T08:34:16.1537256Z * [new tag] trunk/d061a02e6ecf3b62f409578c7d05a564264d1288 -> trunk/d061a02e6ecf3b62f409578c7d05a564264d1288 2025-07-17T08:34:16.1539492Z * [new tag] trunk/d06a406656bf0b4bc9af2d056558dc1630a6e5b9 -> trunk/d06a406656bf0b4bc9af2d056558dc1630a6e5b9 2025-07-17T08:34:16.1541546Z * [new tag] trunk/d083841c0e0d879e460aebb45daac086a240a7dd -> trunk/d083841c0e0d879e460aebb45daac086a240a7dd 2025-07-17T08:34:16.1543764Z * [new tag] trunk/d0a9629435aaceb5acbf31aad70f2109cb8a3ea2 -> trunk/d0a9629435aaceb5acbf31aad70f2109cb8a3ea2 2025-07-17T08:34:16.1545940Z * [new tag] trunk/d0cfa3e5bf662b72068e26e06a1a5c2e640ce8d9 -> trunk/d0cfa3e5bf662b72068e26e06a1a5c2e640ce8d9 2025-07-17T08:34:16.1548059Z * [new tag] trunk/d158e9ea82ce3440e2e9a1bbbecae53a79869404 -> trunk/d158e9ea82ce3440e2e9a1bbbecae53a79869404 2025-07-17T08:34:16.1550084Z * [new tag] trunk/d1947a87074c5db2568038878b1948ea3a33cc23 -> trunk/d1947a87074c5db2568038878b1948ea3a33cc23 2025-07-17T08:34:16.1552303Z * [new tag] trunk/d1b4e0fa9a5feb22fc6de1d36dc4c9dac685caed -> trunk/d1b4e0fa9a5feb22fc6de1d36dc4c9dac685caed 2025-07-17T08:34:16.1554378Z * [new tag] trunk/d1c924c68aaf650439c2193f74cfefc9f16076f1 -> trunk/d1c924c68aaf650439c2193f74cfefc9f16076f1 2025-07-17T08:34:16.1556546Z * [new tag] trunk/d26ca5de058dbcf56ac52bb43e84dd98df2ace97 -> trunk/d26ca5de058dbcf56ac52bb43e84dd98df2ace97 2025-07-17T08:34:16.1558672Z * [new tag] trunk/d27d36136ce35d5d6dc3faa818ba840ba61d4357 -> trunk/d27d36136ce35d5d6dc3faa818ba840ba61d4357 2025-07-17T08:34:16.1560799Z * [new tag] trunk/d283fc79b15844ac470b3429b4dac5661621ae5c -> trunk/d283fc79b15844ac470b3429b4dac5661621ae5c 2025-07-17T08:34:16.1562994Z * [new tag] trunk/d2a2bfcb5867d4920e0a45113861cde6f74857b9 -> trunk/d2a2bfcb5867d4920e0a45113861cde6f74857b9 2025-07-17T08:34:16.1565066Z * [new tag] trunk/d2f06d2b062c9d868a898044bc324547f34c4760 -> trunk/d2f06d2b062c9d868a898044bc324547f34c4760 2025-07-17T08:34:16.1567246Z * [new tag] trunk/d309cd1d502eab8e9cb536876bf5eed88634eb41 -> trunk/d309cd1d502eab8e9cb536876bf5eed88634eb41 2025-07-17T08:34:16.1569139Z * [new tag] trunk/d32deb664a1dfc0e36f0a8d670695046632ebf2a -> trunk/d32deb664a1dfc0e36f0a8d670695046632ebf2a 2025-07-17T08:34:16.1571406Z * [new tag] trunk/d3d64c6db090ee9be051202e6ac6fd4acd5d3e97 -> trunk/d3d64c6db090ee9be051202e6ac6fd4acd5d3e97 2025-07-17T08:34:16.1573508Z * [new tag] trunk/d3d655ad14ee4cd1c135ac57bbf75d5623fc9fa6 -> trunk/d3d655ad14ee4cd1c135ac57bbf75d5623fc9fa6 2025-07-17T08:34:16.1575693Z * [new tag] trunk/d3da03d6fa02aecd01ff41246def793b8fc630bf -> trunk/d3da03d6fa02aecd01ff41246def793b8fc630bf 2025-07-17T08:34:16.1577860Z * [new tag] trunk/d3efd732348f8a455ca23e7fcae043651e41f65e -> trunk/d3efd732348f8a455ca23e7fcae043651e41f65e 2025-07-17T08:34:16.1579945Z * [new tag] trunk/d40aaa42ee8d7283d8de02c68c5625d67acf668a -> trunk/d40aaa42ee8d7283d8de02c68c5625d67acf668a 2025-07-17T08:34:16.1581966Z * [new tag] trunk/d41f62b7a06c51e4a57df4d58e7a2d86d2faa875 -> trunk/d41f62b7a06c51e4a57df4d58e7a2d86d2faa875 2025-07-17T08:34:16.1583948Z * [new tag] trunk/d42c11819f003a848c782d6090f44bbca11c7d60 -> trunk/d42c11819f003a848c782d6090f44bbca11c7d60 2025-07-17T08:34:16.1587508Z * [new tag] trunk/d43c0bdf46d82806cff195a6d2bfa103ab46dc14 -> trunk/d43c0bdf46d82806cff195a6d2bfa103ab46dc14 2025-07-17T08:34:16.1589596Z * [new tag] trunk/d4ad28042993dd09c80775d7fc5756fb4d13fdd4 -> trunk/d4ad28042993dd09c80775d7fc5756fb4d13fdd4 2025-07-17T08:34:16.1591769Z * [new tag] trunk/d4d0ede6bacb4b3b33c0e4aa4cb0e79d34e697ec -> trunk/d4d0ede6bacb4b3b33c0e4aa4cb0e79d34e697ec 2025-07-17T08:34:16.1593841Z * [new tag] trunk/d512584718838876ae14b32bb53cd836a1ceccae -> trunk/d512584718838876ae14b32bb53cd836a1ceccae 2025-07-17T08:34:16.1595871Z * [new tag] trunk/d55dc00f8479699f454e91c779b53e2a16cffc21 -> trunk/d55dc00f8479699f454e91c779b53e2a16cffc21 2025-07-17T08:34:16.1597957Z * [new tag] trunk/d56f11a1f275f2dc037cd5099b70240745990693 -> trunk/d56f11a1f275f2dc037cd5099b70240745990693 2025-07-17T08:34:16.1599987Z * [new tag] trunk/d5781c8d21b3dca35715a093ba52c5698551ad9b -> trunk/d5781c8d21b3dca35715a093ba52c5698551ad9b 2025-07-17T08:34:16.1602030Z * [new tag] trunk/d58ed04d89c34c6930d0f28be351c53db407078f -> trunk/d58ed04d89c34c6930d0f28be351c53db407078f 2025-07-17T08:34:16.1604112Z * [new tag] trunk/d59ed21d0fdcbb34b5c4b121a3b49ae64d8da367 -> trunk/d59ed21d0fdcbb34b5c4b121a3b49ae64d8da367 2025-07-17T08:34:16.1606204Z * [new tag] trunk/d5a89178b05db3124f16c733e9b00f8c5c4ab038 -> trunk/d5a89178b05db3124f16c733e9b00f8c5c4ab038 2025-07-17T08:34:16.1608230Z * [new tag] trunk/d5b4a329607514b30944c7682efcf79cf8950ed3 -> trunk/d5b4a329607514b30944c7682efcf79cf8950ed3 2025-07-17T08:34:16.1610291Z * [new tag] trunk/d5d14ee823e70224a154884a5c2912643d648094 -> trunk/d5d14ee823e70224a154884a5c2912643d648094 2025-07-17T08:34:16.1612481Z * [new tag] trunk/d5e6f4209424b96237ea5468dd47899e1124dfcb -> trunk/d5e6f4209424b96237ea5468dd47899e1124dfcb 2025-07-17T08:34:16.1614815Z * [new tag] trunk/d6237721c074484ea5e72fc05614587886e57fd6 -> trunk/d6237721c074484ea5e72fc05614587886e57fd6 2025-07-17T08:34:16.1616922Z * [new tag] trunk/d632cf2cc9aac8ab0e03d1537982265e42be95e5 -> trunk/d632cf2cc9aac8ab0e03d1537982265e42be95e5 2025-07-17T08:34:16.1618930Z * [new tag] trunk/d66b4bcc3f89ac6b9d2ccfd4cb01b0aa85d4e3fd -> trunk/d66b4bcc3f89ac6b9d2ccfd4cb01b0aa85d4e3fd 2025-07-17T08:34:16.1620903Z * [new tag] trunk/d6ee5144cab20141c8e0cf48856a90a6517753f1 -> trunk/d6ee5144cab20141c8e0cf48856a90a6517753f1 2025-07-17T08:34:16.1623141Z * [new tag] trunk/d75d30eeb610b164e69d0678a2e2b2dea81eec0f -> trunk/d75d30eeb610b164e69d0678a2e2b2dea81eec0f 2025-07-17T08:34:16.1625428Z * [new tag] trunk/d76323d41742cbc05ec6857319b267d2c7ea8fd9 -> trunk/d76323d41742cbc05ec6857319b267d2c7ea8fd9 2025-07-17T08:34:16.1627346Z * [new tag] trunk/d79651571fb082a7a20631059563ecdf9d11b7e6 -> trunk/d79651571fb082a7a20631059563ecdf9d11b7e6 2025-07-17T08:34:16.1629601Z * [new tag] trunk/d797038ea9f3951c7602e8710f4866f73d7e7acd -> trunk/d797038ea9f3951c7602e8710f4866f73d7e7acd 2025-07-17T08:34:16.1631686Z * [new tag] trunk/d7e1b8b11d7430c7633dcad6f6596b5df8fa02f7 -> trunk/d7e1b8b11d7430c7633dcad6f6596b5df8fa02f7 2025-07-17T08:34:16.1633799Z * [new tag] trunk/d7e3c9ce8289f26704b140e68dac2849df750040 -> trunk/d7e3c9ce8289f26704b140e68dac2849df750040 2025-07-17T08:34:16.1635805Z * [new tag] trunk/d7e657da358d6d393e2105f94f1312fb969654a2 -> trunk/d7e657da358d6d393e2105f94f1312fb969654a2 2025-07-17T08:34:16.1637929Z * [new tag] trunk/d83ff89d3bf54ecc95d441411ecbf07831cc0e5a -> trunk/d83ff89d3bf54ecc95d441411ecbf07831cc0e5a 2025-07-17T08:34:16.1639972Z * [new tag] trunk/d846e213553621cb4581f1f36ac0023e528ddb65 -> trunk/d846e213553621cb4581f1f36ac0023e528ddb65 2025-07-17T08:34:16.1642100Z * [new tag] trunk/d84efde3f02bdc4780e5c9ec43f9c515c744e9be -> trunk/d84efde3f02bdc4780e5c9ec43f9c515c744e9be 2025-07-17T08:34:16.1644155Z * [new tag] trunk/d859e65826d511f8379db4a8c4109417e26e0772 -> trunk/d859e65826d511f8379db4a8c4109417e26e0772 2025-07-17T08:34:16.1646353Z * [new tag] trunk/d89f30ad45b9d4bfe5cf5ab441b53e849e55df7b -> trunk/d89f30ad45b9d4bfe5cf5ab441b53e849e55df7b 2025-07-17T08:34:16.1648392Z * [new tag] trunk/d8bb5ac260dd81f75c8add6eadd8ce11e62be1a3 -> trunk/d8bb5ac260dd81f75c8add6eadd8ce11e62be1a3 2025-07-17T08:34:16.1650357Z * [new tag] trunk/d9426a81d2ab54f809a3b32a6ab2e606073fe66f -> trunk/d9426a81d2ab54f809a3b32a6ab2e606073fe66f 2025-07-17T08:34:16.1652307Z * [new tag] trunk/d9577df312d477e8fa5b9d7bc61fb1f2c07b8e48 -> trunk/d9577df312d477e8fa5b9d7bc61fb1f2c07b8e48 2025-07-17T08:34:16.1654383Z * [new tag] trunk/d96dec84151dfdea1aa2bf568deebdc182214f45 -> trunk/d96dec84151dfdea1aa2bf568deebdc182214f45 2025-07-17T08:34:16.1656416Z * [new tag] trunk/d9799a2ee7adb9d15f7a2d47ee17fc39c588af60 -> trunk/d9799a2ee7adb9d15f7a2d47ee17fc39c588af60 2025-07-17T08:34:16.1658315Z * [new tag] trunk/d98fa4a1033079ae19591aff5c3c354617bff482 -> trunk/d98fa4a1033079ae19591aff5c3c354617bff482 2025-07-17T08:34:16.1660410Z * [new tag] trunk/d99cac28160428a8b006890abffaaa0754bd28e1 -> trunk/d99cac28160428a8b006890abffaaa0754bd28e1 2025-07-17T08:34:16.1662541Z * [new tag] trunk/d9b8369f3976e89eef96c903dfede746fb438f63 -> trunk/d9b8369f3976e89eef96c903dfede746fb438f63 2025-07-17T08:34:16.1664502Z * [new tag] trunk/da05b7fb94fa6382c43e165a525a76d8ae62cadd -> trunk/da05b7fb94fa6382c43e165a525a76d8ae62cadd 2025-07-17T08:34:16.1666694Z * [new tag] trunk/da1f337bc43ce2d720aca1349f9c09712e037382 -> trunk/da1f337bc43ce2d720aca1349f9c09712e037382 2025-07-17T08:34:16.1668764Z * [new tag] trunk/da1f8980dfd646f14dc33bf0bc70fcb4d09096b3 -> trunk/da1f8980dfd646f14dc33bf0bc70fcb4d09096b3 2025-07-17T08:34:16.1670821Z * [new tag] trunk/da4e7c77a12746b0806a63af58d7c913ed241f55 -> trunk/da4e7c77a12746b0806a63af58d7c913ed241f55 2025-07-17T08:34:16.1672950Z * [new tag] trunk/da50835bdebd43275e0be4b1362e1bb92b568e72 -> trunk/da50835bdebd43275e0be4b1362e1bb92b568e72 2025-07-17T08:34:16.1675051Z * [new tag] trunk/da910e603a764c1f935dd0409b4c18bd84b1f045 -> trunk/da910e603a764c1f935dd0409b4c18bd84b1f045 2025-07-17T08:34:16.1677208Z * [new tag] trunk/dabb55baff2292ddd7728882b4e2113b8cb9e23b -> trunk/dabb55baff2292ddd7728882b4e2113b8cb9e23b 2025-07-17T08:34:16.1679645Z * [new tag] trunk/db01f1032ff133dc7cc3097b76bfb1b73ff533ce -> trunk/db01f1032ff133dc7cc3097b76bfb1b73ff533ce 2025-07-17T08:34:16.1681628Z * [new tag] trunk/db188503cb705230664c94d8b3a85165a00d632d -> trunk/db188503cb705230664c94d8b3a85165a00d632d 2025-07-17T08:34:16.1683725Z * [new tag] trunk/db491825e05e8bed9c7ed5d9fdd6ce206ab3167c -> trunk/db491825e05e8bed9c7ed5d9fdd6ce206ab3167c 2025-07-17T08:34:16.1685825Z * [new tag] trunk/db5970c1a67968f3b76d204d75789021d4304337 -> trunk/db5970c1a67968f3b76d204d75789021d4304337 2025-07-17T08:34:16.1688323Z * [new tag] trunk/dbec08bc1cc9113c13652128a4ddcfcf2426a8c0 -> trunk/dbec08bc1cc9113c13652128a4ddcfcf2426a8c0 2025-07-17T08:34:16.1690501Z * [new tag] trunk/dbf7d421dabced2335d17c7d7e57c1770f2f12c0 -> trunk/dbf7d421dabced2335d17c7d7e57c1770f2f12c0 2025-07-17T08:34:16.1692601Z * [new tag] trunk/dc5e8f7999cccb51efcf0f5fe197a740a918c73d -> trunk/dc5e8f7999cccb51efcf0f5fe197a740a918c73d 2025-07-17T08:34:16.1694736Z * [new tag] trunk/dcb97cd519e4e4974bc3bd4960512b557216f3e6 -> trunk/dcb97cd519e4e4974bc3bd4960512b557216f3e6 2025-07-17T08:34:16.1696938Z * [new tag] trunk/dd1b6621bc04a37b60ac304d913a3c838b0b1e4d -> trunk/dd1b6621bc04a37b60ac304d913a3c838b0b1e4d 2025-07-17T08:34:16.1699020Z * [new tag] trunk/dd3e7170c20cade9a308900aa4df46ab7656d253 -> trunk/dd3e7170c20cade9a308900aa4df46ab7656d253 2025-07-17T08:34:16.1701104Z * [new tag] trunk/dd41a3907cde3ce19fb69bd79c4055233a1e28d3 -> trunk/dd41a3907cde3ce19fb69bd79c4055233a1e28d3 2025-07-17T08:34:16.1703239Z * [new tag] trunk/dd78d6e7eaecf0aa5317076a721bc719c96b41f8 -> trunk/dd78d6e7eaecf0aa5317076a721bc719c96b41f8 2025-07-17T08:34:16.1705188Z * [new tag] trunk/dd93883231226576220a8ec0fa266c9e22eae82c -> trunk/dd93883231226576220a8ec0fa266c9e22eae82c 2025-07-17T08:34:16.1708626Z * [new tag] trunk/ddf502c988133835a89959bef945bf9c5f06b428 -> trunk/ddf502c988133835a89959bef945bf9c5f06b428 2025-07-17T08:34:16.1710646Z * [new tag] trunk/de1930a4290a27f3f03dd1d3454fb6ef9d12f3ea -> trunk/de1930a4290a27f3f03dd1d3454fb6ef9d12f3ea 2025-07-17T08:34:16.1712784Z * [new tag] trunk/de45c5f673ce261e9a82c54280beeda36cff640e -> trunk/de45c5f673ce261e9a82c54280beeda36cff640e 2025-07-17T08:34:16.1715035Z * [new tag] trunk/dea4864ce08115a0cdc871b84dd3080567d8b5e4 -> trunk/dea4864ce08115a0cdc871b84dd3080567d8b5e4 2025-07-17T08:34:16.1717136Z * [new tag] trunk/df72078fe1339751e702c7511c23b4597d022dcc -> trunk/df72078fe1339751e702c7511c23b4597d022dcc 2025-07-17T08:34:16.1719168Z * [new tag] trunk/dfa2649434f539d7580d38f08890176e73d45158 -> trunk/dfa2649434f539d7580d38f08890176e73d45158 2025-07-17T08:34:16.1721242Z * [new tag] trunk/dfc31b3345d78b0a49d446dcc1957404606a3aa2 -> trunk/dfc31b3345d78b0a49d446dcc1957404606a3aa2 2025-07-17T08:34:16.1723417Z * [new tag] trunk/dfcda613b65499e4b593b5bbb64812d57194858e -> trunk/dfcda613b65499e4b593b5bbb64812d57194858e 2025-07-17T08:34:16.1725484Z * [new tag] trunk/dfdd636cfa06ac70c4aa7ad5ee5e854c46b25a8e -> trunk/dfdd636cfa06ac70c4aa7ad5ee5e854c46b25a8e 2025-07-17T08:34:16.1727522Z * [new tag] trunk/dfef1e44085bb156abc4aff0f34a0b82a4a337b8 -> trunk/dfef1e44085bb156abc4aff0f34a0b82a4a337b8 2025-07-17T08:34:16.1729618Z * [new tag] trunk/e01fde82131c7f0b4c122222694911ee6fab36ca -> trunk/e01fde82131c7f0b4c122222694911ee6fab36ca 2025-07-17T08:34:16.1731791Z * [new tag] trunk/e0447bb5f84dca38e7515d1b1fdea42c647e5acd -> trunk/e0447bb5f84dca38e7515d1b1fdea42c647e5acd 2025-07-17T08:34:16.1733858Z * [new tag] trunk/e071837594f26a3bbaeee11eaa74f3bfd9c998ce -> trunk/e071837594f26a3bbaeee11eaa74f3bfd9c998ce 2025-07-17T08:34:16.1736147Z * [new tag] trunk/e0850123355a919427be3cb02f40c9a9e144fc3c -> trunk/e0850123355a919427be3cb02f40c9a9e144fc3c 2025-07-17T08:34:16.1738131Z * [new tag] trunk/e0ab1b538a646bff0739830d74a8e6ea6bf1eddd -> trunk/e0ab1b538a646bff0739830d74a8e6ea6bf1eddd 2025-07-17T08:34:16.1740228Z * [new tag] trunk/e0ae4ecca87928a96c97fb60cbb36aca0fc182f2 -> trunk/e0ae4ecca87928a96c97fb60cbb36aca0fc182f2 2025-07-17T08:34:16.1742334Z * [new tag] trunk/e0fd48be7d526625cacfd093f3db13506ca9cc17 -> trunk/e0fd48be7d526625cacfd093f3db13506ca9cc17 2025-07-17T08:34:16.1745004Z * [new tag] trunk/e1180c7228ba8c8b16cabf78706d4a67ca189a6b -> trunk/e1180c7228ba8c8b16cabf78706d4a67ca189a6b 2025-07-17T08:34:16.1747781Z * [new tag] trunk/e124a0d88ca2aa04bfaca2dcabf5de6244048e45 -> trunk/e124a0d88ca2aa04bfaca2dcabf5de6244048e45 2025-07-17T08:34:16.1749938Z * [new tag] trunk/e12597090c482ebf1eff7739794667a92cbff657 -> trunk/e12597090c482ebf1eff7739794667a92cbff657 2025-07-17T08:34:16.1752291Z * [new tag] trunk/e15848669f84d3767bfca724a29a6a6dde3308b9 -> trunk/e15848669f84d3767bfca724a29a6a6dde3308b9 2025-07-17T08:34:16.1754329Z * [new tag] trunk/e15ea965a1e84029eb900b93f5776767a39fd91e -> trunk/e15ea965a1e84029eb900b93f5776767a39fd91e 2025-07-17T08:34:16.1756400Z * [new tag] trunk/e15f4248ad2797539f1bf965bd0a3500b8ed15ed -> trunk/e15f4248ad2797539f1bf965bd0a3500b8ed15ed 2025-07-17T08:34:16.1758404Z * [new tag] trunk/e1723098805fcbf3ab7f3d20750fb0665bf65004 -> trunk/e1723098805fcbf3ab7f3d20750fb0665bf65004 2025-07-17T08:34:16.1760605Z * [new tag] trunk/e1a20988f3724317a7ee79c1777d574a8282a122 -> trunk/e1a20988f3724317a7ee79c1777d574a8282a122 2025-07-17T08:34:16.1763078Z * [new tag] trunk/e1aee86646aa6d1b9cb9d34351e43936401c5efc -> trunk/e1aee86646aa6d1b9cb9d34351e43936401c5efc 2025-07-17T08:34:16.1765174Z * [new tag] trunk/e1db10e05aa720aef1989773adcf48f311bcf920 -> trunk/e1db10e05aa720aef1989773adcf48f311bcf920 2025-07-17T08:34:16.1767203Z * [new tag] trunk/e1f28fe17bc55389fc5a31bad588d22e5ecca722 -> trunk/e1f28fe17bc55389fc5a31bad588d22e5ecca722 2025-07-17T08:34:16.1769280Z * [new tag] trunk/e20784f228abca24ab263cf0f69534667365ecba -> trunk/e20784f228abca24ab263cf0f69534667365ecba 2025-07-17T08:34:16.1771346Z * [new tag] trunk/e2351f2dcf7d300460edb9d0c2eb0ea11cc547c5 -> trunk/e2351f2dcf7d300460edb9d0c2eb0ea11cc547c5 2025-07-17T08:34:16.1773368Z * [new tag] trunk/e25ce0f928bfdd545323fcc2f88209233309bbbc -> trunk/e25ce0f928bfdd545323fcc2f88209233309bbbc 2025-07-17T08:34:16.1775396Z * [new tag] trunk/e265b719bd67f7c0a2b9001daef442a70232dcc8 -> trunk/e265b719bd67f7c0a2b9001daef442a70232dcc8 2025-07-17T08:34:16.1777433Z * [new tag] trunk/e28925aa7566da3ffe4f13dd8d59a6767bf97b2e -> trunk/e28925aa7566da3ffe4f13dd8d59a6767bf97b2e 2025-07-17T08:34:16.1779499Z * [new tag] trunk/e290a4c645e00926f0bfa44488f9f7dbd7cb2d0b -> trunk/e290a4c645e00926f0bfa44488f9f7dbd7cb2d0b 2025-07-17T08:34:16.1781547Z * [new tag] trunk/e2c9d8d6414927ce754bbc40b767edf103cf16da -> trunk/e2c9d8d6414927ce754bbc40b767edf103cf16da 2025-07-17T08:34:16.1783667Z * [new tag] trunk/e2f64eedafc6f0dcad9256ac1f38c0359477679a -> trunk/e2f64eedafc6f0dcad9256ac1f38c0359477679a 2025-07-17T08:34:16.1785785Z * [new tag] trunk/e311886e3d57c83a88b97a084dd0b95d6d1537a8 -> trunk/e311886e3d57c83a88b97a084dd0b95d6d1537a8 2025-07-17T08:34:16.1788105Z * [new tag] trunk/e31f20529276356092b5c63c2920d5b17ca9f4ba -> trunk/e31f20529276356092b5c63c2920d5b17ca9f4ba 2025-07-17T08:34:16.1790216Z * [new tag] trunk/e323d46b617c1552cc0f017ad8ec80189eacf187 -> trunk/e323d46b617c1552cc0f017ad8ec80189eacf187 2025-07-17T08:34:16.1792303Z * [new tag] trunk/e3320965b48d87b7257997c35db722302d923ead -> trunk/e3320965b48d87b7257997c35db722302d923ead 2025-07-17T08:34:16.1794570Z * [new tag] trunk/e375d21bb9b0ef6fefe7a8af5a054a17de8c63c9 -> trunk/e375d21bb9b0ef6fefe7a8af5a054a17de8c63c9 2025-07-17T08:34:16.1796445Z * [new tag] trunk/e3977e843de6c9c43be00ee8c67c533debfc0dc9 -> trunk/e3977e843de6c9c43be00ee8c67c533debfc0dc9 2025-07-17T08:34:16.1798517Z * [new tag] trunk/e3afbb03623559fa3fa7ba607c07c86ebed889f0 -> trunk/e3afbb03623559fa3fa7ba607c07c86ebed889f0 2025-07-17T08:34:16.1800811Z * [new tag] trunk/e3b44edfd837199880aed3ec802383617da731f3 -> trunk/e3b44edfd837199880aed3ec802383617da731f3 2025-07-17T08:34:16.1803026Z * [new tag] trunk/e3f2597b4529927b812b443cf8da19b44ed8dea4 -> trunk/e3f2597b4529927b812b443cf8da19b44ed8dea4 2025-07-17T08:34:16.1805071Z * [new tag] trunk/e3f8141c25b7213885f0b7b64f2b0ccc7858be95 -> trunk/e3f8141c25b7213885f0b7b64f2b0ccc7858be95 2025-07-17T08:34:16.1807114Z * [new tag] trunk/e3fe001d9e38153a7797ad25377fdfa34e461113 -> trunk/e3fe001d9e38153a7797ad25377fdfa34e461113 2025-07-17T08:34:16.1809215Z * [new tag] trunk/e40ade5182233f548b25f2732effe3719d16e9ad -> trunk/e40ade5182233f548b25f2732effe3719d16e9ad 2025-07-17T08:34:16.1811304Z * [new tag] trunk/e466dab164d9236bfe5817ec8e4d24c7b9d3e392 -> trunk/e466dab164d9236bfe5817ec8e4d24c7b9d3e392 2025-07-17T08:34:16.1813539Z * [new tag] trunk/e472daa80963aae389089f9dc324b04261e2a5ef -> trunk/e472daa80963aae389089f9dc324b04261e2a5ef 2025-07-17T08:34:16.1815708Z * [new tag] trunk/e4ae60a413b99688d308794baaa1c685b5c19025 -> trunk/e4ae60a413b99688d308794baaa1c685b5c19025 2025-07-17T08:34:16.1817851Z * [new tag] trunk/e4c17d5e1ccd0e730caef484af291243bc1d9cde -> trunk/e4c17d5e1ccd0e730caef484af291243bc1d9cde 2025-07-17T08:34:16.1820102Z * [new tag] trunk/e4c9f6d9a286bd367ce3a157428f588e267d2630 -> trunk/e4c9f6d9a286bd367ce3a157428f588e267d2630 2025-07-17T08:34:16.1822158Z * [new tag] trunk/e517066f41342f0f01043121dcb8ac6b235ee40c -> trunk/e517066f41342f0f01043121dcb8ac6b235ee40c 2025-07-17T08:34:16.1824336Z * [new tag] trunk/e53ddaf1f6ef3f1d22075ad33a5922fb8b277fe0 -> trunk/e53ddaf1f6ef3f1d22075ad33a5922fb8b277fe0 2025-07-17T08:34:16.1826509Z * [new tag] trunk/e581f015eebf483eb6f19447caf2d2452a602c2f -> trunk/e581f015eebf483eb6f19447caf2d2452a602c2f 2025-07-17T08:34:16.1828647Z * [new tag] trunk/e583b888194c8c74ebbd332c09c394acebbbbcff -> trunk/e583b888194c8c74ebbd332c09c394acebbbbcff 2025-07-17T08:34:16.1830670Z * [new tag] trunk/e5a0b73ce9e7bc489150932b60f673155d6ddc30 -> trunk/e5a0b73ce9e7bc489150932b60f673155d6ddc30 2025-07-17T08:34:16.1832716Z * [new tag] trunk/e5a11971919009b5e5bd050b3ed52e324f1ce894 -> trunk/e5a11971919009b5e5bd050b3ed52e324f1ce894 2025-07-17T08:34:16.1834910Z * [new tag] trunk/e5ea24fb27e0ef5dbb0c4039d88c0f4faa221fef -> trunk/e5ea24fb27e0ef5dbb0c4039d88c0f4faa221fef 2025-07-17T08:34:16.1837089Z * [new tag] trunk/e5ed267f830ce9fc6579f928082e563fbed37e3f -> trunk/e5ed267f830ce9fc6579f928082e563fbed37e3f 2025-07-17T08:34:16.1839034Z * [new tag] trunk/e5edd013ab418b8b3609cb3cb1df3804b69d8eef -> trunk/e5edd013ab418b8b3609cb3cb1df3804b69d8eef 2025-07-17T08:34:16.1841092Z * [new tag] trunk/e5f6ffd8109bee99416d9b127f961ab0e253a1c2 -> trunk/e5f6ffd8109bee99416d9b127f961ab0e253a1c2 2025-07-17T08:34:16.1843187Z * [new tag] trunk/e600e044a770d29d1fe5d9638b274a7d4f22f969 -> trunk/e600e044a770d29d1fe5d9638b274a7d4f22f969 2025-07-17T08:34:16.1845260Z * [new tag] trunk/e6252f62efa5e6e4ab0d56967d7461f1e8a7eb7c -> trunk/e6252f62efa5e6e4ab0d56967d7461f1e8a7eb7c 2025-07-17T08:34:16.1847362Z * [new tag] trunk/e694280d1215caf70f41575f2611bfa26c69ebdb -> trunk/e694280d1215caf70f41575f2611bfa26c69ebdb 2025-07-17T08:34:16.1849665Z * [new tag] trunk/e6d71f37896e99ced7daf9ee43a30c0ec03d60f2 -> trunk/e6d71f37896e99ced7daf9ee43a30c0ec03d60f2 2025-07-17T08:34:16.1851737Z * [new tag] trunk/e6d8ed02cbeabe961d1a5303973a6d89ee851736 -> trunk/e6d8ed02cbeabe961d1a5303973a6d89ee851736 2025-07-17T08:34:16.1853866Z * [new tag] trunk/e6ed4074e8411000eea2f0de0c0829d909e4701f -> trunk/e6ed4074e8411000eea2f0de0c0829d909e4701f 2025-07-17T08:34:16.1855921Z * [new tag] trunk/e71bb021b9553ddc2db6cb8ea7bf8643552f09fc -> trunk/e71bb021b9553ddc2db6cb8ea7bf8643552f09fc 2025-07-17T08:34:16.1857985Z * [new tag] trunk/e7698ff5cf40729d11df6c32c6df0a163e5d0ce0 -> trunk/e7698ff5cf40729d11df6c32c6df0a163e5d0ce0 2025-07-17T08:34:16.1860191Z * [new tag] trunk/e78f2ac92b709a060aa323d6e527ec2ecc36fb93 -> trunk/e78f2ac92b709a060aa323d6e527ec2ecc36fb93 2025-07-17T08:34:16.1862437Z * [new tag] trunk/e7a66166ce5294a52bf3966aef49562f94343475 -> trunk/e7a66166ce5294a52bf3966aef49562f94343475 2025-07-17T08:34:16.1864548Z * [new tag] trunk/e7da21806fbc96d47ac03ea8f6507deb0ab48481 -> trunk/e7da21806fbc96d47ac03ea8f6507deb0ab48481 2025-07-17T08:34:16.1866787Z * [new tag] trunk/e8217ad8becd2b297682c685a9179997cb0a98cc -> trunk/e8217ad8becd2b297682c685a9179997cb0a98cc 2025-07-17T08:34:16.1869118Z * [new tag] trunk/e895e9689c625cbcd8f46880115e0d093713fa37 -> trunk/e895e9689c625cbcd8f46880115e0d093713fa37 2025-07-17T08:34:16.1871202Z * [new tag] trunk/e8b3dfa7c0b6d74fc84714dbb79cb6a8bad43ae7 -> trunk/e8b3dfa7c0b6d74fc84714dbb79cb6a8bad43ae7 2025-07-17T08:34:16.1873267Z * [new tag] trunk/e8bfce9a43960f417a36636a0e7f6a58b7dc56d2 -> trunk/e8bfce9a43960f417a36636a0e7f6a58b7dc56d2 2025-07-17T08:34:16.1875485Z * [new tag] trunk/e8cf5ff5641c1ce4c3046a85df55b15eaaea38b2 -> trunk/e8cf5ff5641c1ce4c3046a85df55b15eaaea38b2 2025-07-17T08:34:16.1877532Z * [new tag] trunk/e8d29c45e02402bcb4ae7726abd415bebd441852 -> trunk/e8d29c45e02402bcb4ae7726abd415bebd441852 2025-07-17T08:34:16.1879580Z * [new tag] trunk/e90148c91d3b8a759db558374c8539e47338926b -> trunk/e90148c91d3b8a759db558374c8539e47338926b 2025-07-17T08:34:16.1881963Z * [new tag] trunk/e92e3eaf4eb815ea28db9a5af9d9ee48c3f7be3f -> trunk/e92e3eaf4eb815ea28db9a5af9d9ee48c3f7be3f 2025-07-17T08:34:16.1884033Z * [new tag] trunk/e9367a7a4288e626f01fada3912d68756f1ca6d3 -> trunk/e9367a7a4288e626f01fada3912d68756f1ca6d3 2025-07-17T08:34:16.1886250Z * [new tag] trunk/e959dd017d7dcd7c6351c9ef303f3667aebe11f5 -> trunk/e959dd017d7dcd7c6351c9ef303f3667aebe11f5 2025-07-17T08:34:16.1888324Z * [new tag] trunk/e95e8eed0a96f024dc84012494f465b34eae9d22 -> trunk/e95e8eed0a96f024dc84012494f465b34eae9d22 2025-07-17T08:34:16.1890352Z * [new tag] trunk/e96f530af5bf8ceaeaa87ec7f4651490a25bce07 -> trunk/e96f530af5bf8ceaeaa87ec7f4651490a25bce07 2025-07-17T08:34:16.1892448Z * [new tag] trunk/e98dd95446e009ace1722498effbf32250d623e4 -> trunk/e98dd95446e009ace1722498effbf32250d623e4 2025-07-17T08:34:16.1894544Z * [new tag] trunk/e99a2a2dba90c9b6276219104bc99aff2f67e2f7 -> trunk/e99a2a2dba90c9b6276219104bc99aff2f67e2f7 2025-07-17T08:34:16.1896660Z * [new tag] trunk/e99cc126a4f4bd5233ff363f27d14a0fff4b4392 -> trunk/e99cc126a4f4bd5233ff363f27d14a0fff4b4392 2025-07-17T08:34:16.1898735Z * [new tag] trunk/e9fdaf8701b599fd943bb899639b5e8a4966b3c3 -> trunk/e9fdaf8701b599fd943bb899639b5e8a4966b3c3 2025-07-17T08:34:16.1900912Z * [new tag] trunk/ea23eb4b98ae8af40f0c38538a9e63f1b0dc40e2 -> trunk/ea23eb4b98ae8af40f0c38538a9e63f1b0dc40e2 2025-07-17T08:34:16.1902973Z * [new tag] trunk/ea37f720995616d8570045c63486198a021f8285 -> trunk/ea37f720995616d8570045c63486198a021f8285 2025-07-17T08:34:16.1905065Z * [new tag] trunk/ea74fdd24aa7d98433231f4a3d75cfd241d8720e -> trunk/ea74fdd24aa7d98433231f4a3d75cfd241d8720e 2025-07-17T08:34:16.1907420Z * [new tag] trunk/eab45643f22e58ee12d95d8b0162d51ca0a50801 -> trunk/eab45643f22e58ee12d95d8b0162d51ca0a50801 2025-07-17T08:34:16.1909327Z * [new tag] trunk/eabf7cd3c552e6c17321a63d7e5a19da92909910 -> trunk/eabf7cd3c552e6c17321a63d7e5a19da92909910 2025-07-17T08:34:16.1911343Z * [new tag] trunk/eaceb243df6fc17a37a2c1d9f8775d1561a0f67c -> trunk/eaceb243df6fc17a37a2c1d9f8775d1561a0f67c 2025-07-17T08:34:16.1913375Z * [new tag] trunk/eaf32fffb7e83b5331951b254ad213ceb2ae72f8 -> trunk/eaf32fffb7e83b5331951b254ad213ceb2ae72f8 2025-07-17T08:34:16.1915548Z * [new tag] trunk/eaf704914eb63efffafeb402633f76f2cd521ef4 -> trunk/eaf704914eb63efffafeb402633f76f2cd521ef4 2025-07-17T08:34:16.1917713Z * [new tag] trunk/eb152ab1dd9eafc777b6642bb141ab1b8376359a -> trunk/eb152ab1dd9eafc777b6642bb141ab1b8376359a 2025-07-17T08:34:16.1919910Z * [new tag] trunk/eb2af14f8e75c22c0a2fcf407005d2e8173d0df5 -> trunk/eb2af14f8e75c22c0a2fcf407005d2e8173d0df5 2025-07-17T08:34:16.1921990Z * [new tag] trunk/eb331b59fedb7f70229ca491029f2f7f27bbff54 -> trunk/eb331b59fedb7f70229ca491029f2f7f27bbff54 2025-07-17T08:34:16.1924053Z * [new tag] trunk/eb4cf59ecdf69dd53805a37525a7f750c8f1e0dd -> trunk/eb4cf59ecdf69dd53805a37525a7f750c8f1e0dd 2025-07-17T08:34:16.1926058Z * [new tag] trunk/eb9efb37c8f315f1d30e86d5797490c6a8666889 -> trunk/eb9efb37c8f315f1d30e86d5797490c6a8666889 2025-07-17T08:34:16.1928207Z * [new tag] trunk/eba5fc91ac924a7a05569c33b0f90f0faf22696d -> trunk/eba5fc91ac924a7a05569c33b0f90f0faf22696d 2025-07-17T08:34:16.1930268Z * [new tag] trunk/ebab2799423987a16975ab07ee49dd944e5315e6 -> trunk/ebab2799423987a16975ab07ee49dd944e5315e6 2025-07-17T08:34:16.1932307Z * [new tag] trunk/ebf83b8b7772632c0558db9a88281ee10ff2df38 -> trunk/ebf83b8b7772632c0558db9a88281ee10ff2df38 2025-07-17T08:34:16.1934440Z * [new tag] trunk/ec0276103fc023b93c6eb17639d4605656ea4f10 -> trunk/ec0276103fc023b93c6eb17639d4605656ea4f10 2025-07-17T08:34:16.1936613Z * [new tag] trunk/ec08eb8ba22e66b113e4f2aba1f6afb738f9d861 -> trunk/ec08eb8ba22e66b113e4f2aba1f6afb738f9d861 2025-07-17T08:34:16.1938573Z * [new tag] trunk/ec816d73b4c8f74a14e275891939de6cfd6891b6 -> trunk/ec816d73b4c8f74a14e275891939de6cfd6891b6 2025-07-17T08:34:16.1940624Z * [new tag] trunk/ecd73c58eeaf7e919316f9b9596f8c677af96c66 -> trunk/ecd73c58eeaf7e919316f9b9596f8c677af96c66 2025-07-17T08:34:16.1942785Z * [new tag] trunk/ed03492238c9ef76f5c1df6cfa54da83a2a384e1 -> trunk/ed03492238c9ef76f5c1df6cfa54da83a2a384e1 2025-07-17T08:34:16.1944930Z * [new tag] trunk/ed051c308464904e07d33c39524b3931dc8c947a -> trunk/ed051c308464904e07d33c39524b3931dc8c947a 2025-07-17T08:34:16.1948868Z * [new tag] trunk/ed508cc0182b923789555d4e1ec1f8b23707b7db -> trunk/ed508cc0182b923789555d4e1ec1f8b23707b7db 2025-07-17T08:34:16.1951055Z * [new tag] trunk/ed5d6d2a200a0f03b164053322341bb10acc051d -> trunk/ed5d6d2a200a0f03b164053322341bb10acc051d 2025-07-17T08:34:16.1953164Z * [new tag] trunk/ed661a5f11de116539f70882f4f59a61d5e3e209 -> trunk/ed661a5f11de116539f70882f4f59a61d5e3e209 2025-07-17T08:34:16.1955339Z * [new tag] trunk/ed6ae20cf0e31d49d54177251293267205e24021 -> trunk/ed6ae20cf0e31d49d54177251293267205e24021 2025-07-17T08:34:16.1957494Z * [new tag] trunk/ed6df0e324233fda094e3c1cb4de4009d1ba2413 -> trunk/ed6df0e324233fda094e3c1cb4de4009d1ba2413 2025-07-17T08:34:16.1959673Z * [new tag] trunk/ed911747c2a56cf84d648d677403d5bf6b2ebd0b -> trunk/ed911747c2a56cf84d648d677403d5bf6b2ebd0b 2025-07-17T08:34:16.1961938Z * [new tag] trunk/eda0a9cc90b9a63127a49d617329f98b6404e90d -> trunk/eda0a9cc90b9a63127a49d617329f98b6404e90d 2025-07-17T08:34:16.1964233Z * [new tag] trunk/edb92e16ba9ad12bd51c4f73389556e163e9cc08 -> trunk/edb92e16ba9ad12bd51c4f73389556e163e9cc08 2025-07-17T08:34:16.1966161Z * [new tag] trunk/edd45f3a020f892c17672cc2d08f64cb960006ad -> trunk/edd45f3a020f892c17672cc2d08f64cb960006ad 2025-07-17T08:34:16.1968314Z * [new tag] trunk/edd9c09e73aaa8d1a1667fa7ea5a337aa31fa6ec -> trunk/edd9c09e73aaa8d1a1667fa7ea5a337aa31fa6ec 2025-07-17T08:34:16.1970387Z * [new tag] trunk/eddddea9087a8840ce5ae1bbb3c6f59556d7c0f0 -> trunk/eddddea9087a8840ce5ae1bbb3c6f59556d7c0f0 2025-07-17T08:34:16.1972537Z * [new tag] trunk/ede6ead8cd8e925cb093f2b3016342e645bd728d -> trunk/ede6ead8cd8e925cb093f2b3016342e645bd728d 2025-07-17T08:34:16.1974498Z * [new tag] trunk/edf7bb4f514220f96ddfa646ae6e9e930a305ec1 -> trunk/edf7bb4f514220f96ddfa646ae6e9e930a305ec1 2025-07-17T08:34:16.1976437Z * [new tag] trunk/ee0992871c99fc6a1e19eb839ab65391a168d2f8 -> trunk/ee0992871c99fc6a1e19eb839ab65391a168d2f8 2025-07-17T08:34:16.1978527Z * [new tag] trunk/ee4d343499c80be16a58d5ac604da6e2130cd94d -> trunk/ee4d343499c80be16a58d5ac604da6e2130cd94d 2025-07-17T08:34:16.1980714Z * [new tag] trunk/ee56e9f8a8202bfa02c2d9ae3cfa07d4a41ab567 -> trunk/ee56e9f8a8202bfa02c2d9ae3cfa07d4a41ab567 2025-07-17T08:34:16.1982859Z * [new tag] trunk/ee5c2908cbfcbda6e8a5737d9939d36ba417d627 -> trunk/ee5c2908cbfcbda6e8a5737d9939d36ba417d627 2025-07-17T08:34:16.1984843Z * [new tag] trunk/ee9ac36c2316f430c4de2a0982bde3cb8087cbe7 -> trunk/ee9ac36c2316f430c4de2a0982bde3cb8087cbe7 2025-07-17T08:34:16.1987096Z * [new tag] trunk/eea3bcb3d146f16a8f8c91a9e2a8f1b76c5917f4 -> trunk/eea3bcb3d146f16a8f8c91a9e2a8f1b76c5917f4 2025-07-17T08:34:16.1989480Z * [new tag] trunk/eeaefa133694811aac1fd215bcae2e7f1e098d1d -> trunk/eeaefa133694811aac1fd215bcae2e7f1e098d1d 2025-07-17T08:34:16.1991579Z * [new tag] trunk/eecaa0bbc6a39c89366f9ec97797479c27f6d760 -> trunk/eecaa0bbc6a39c89366f9ec97797479c27f6d760 2025-07-17T08:34:16.1993811Z * [new tag] trunk/eef253d9f6fc8b9774d199889a817f50e89572c2 -> trunk/eef253d9f6fc8b9774d199889a817f50e89572c2 2025-07-17T08:34:16.1995954Z * [new tag] trunk/ef4cca2d79eba61441da46906b30f8f6165cc455 -> trunk/ef4cca2d79eba61441da46906b30f8f6165cc455 2025-07-17T08:34:16.1998026Z * [new tag] trunk/ef6d2cee7a93c1e8f52a7595b6f630d0fcd83692 -> trunk/ef6d2cee7a93c1e8f52a7595b6f630d0fcd83692 2025-07-17T08:34:16.2000135Z * [new tag] trunk/ef6dfa06a9ff84bf04b0d83cfab4cb396a508a0e -> trunk/ef6dfa06a9ff84bf04b0d83cfab4cb396a508a0e 2025-07-17T08:34:16.2002210Z * [new tag] trunk/ef97bd47131423e0819b293dc227b62d0c376023 -> trunk/ef97bd47131423e0819b293dc227b62d0c376023 2025-07-17T08:34:16.2004341Z * [new tag] trunk/efbf07e7ea46c33c936cac882eb1c8b782d1d070 -> trunk/efbf07e7ea46c33c936cac882eb1c8b782d1d070 2025-07-17T08:34:16.2006489Z * [new tag] trunk/effe376db07c7071fa4d704dd0787a394a5eda50 -> trunk/effe376db07c7071fa4d704dd0787a394a5eda50 2025-07-17T08:34:16.2008562Z * [new tag] trunk/f04fd4dc4eaf16557f9212240bdaa8377d51bad1 -> trunk/f04fd4dc4eaf16557f9212240bdaa8377d51bad1 2025-07-17T08:34:16.2010626Z * [new tag] trunk/f096820d0f845f36529fe774cf761d5fc4ad02a8 -> trunk/f096820d0f845f36529fe774cf761d5fc4ad02a8 2025-07-17T08:34:16.2012648Z * [new tag] trunk/f0b388665efd20c1ec35fc48afb115a0f15116ce -> trunk/f0b388665efd20c1ec35fc48afb115a0f15116ce 2025-07-17T08:34:16.2014804Z * [new tag] trunk/f0bee87eea03f6ded538590e2a073cc62ecd2818 -> trunk/f0bee87eea03f6ded538590e2a073cc62ecd2818 2025-07-17T08:34:16.2016932Z * [new tag] trunk/f1331f3f1b43d1848341a0f0da66a13cb05570d0 -> trunk/f1331f3f1b43d1848341a0f0da66a13cb05570d0 2025-07-17T08:34:16.2019233Z * [new tag] trunk/f140fac8dcfa6ec20d594bc4637417a83bc90036 -> trunk/f140fac8dcfa6ec20d594bc4637417a83bc90036 2025-07-17T08:34:16.2021501Z * [new tag] trunk/f151b201236f959e3874b73dde9bfae5e10dae78 -> trunk/f151b201236f959e3874b73dde9bfae5e10dae78 2025-07-17T08:34:16.2023498Z * [new tag] trunk/f154f9b3040369a7979d5de7acb6fe21433eda83 -> trunk/f154f9b3040369a7979d5de7acb6fe21433eda83 2025-07-17T08:34:16.2026147Z * [new tag] trunk/f16053f0c9a09fa337fbf85aaf64f88712b8dcdb -> trunk/f16053f0c9a09fa337fbf85aaf64f88712b8dcdb 2025-07-17T08:34:16.2028762Z * [new tag] trunk/f179b7198522e6d93bd103efba1a1ebd5a2cf891 -> trunk/f179b7198522e6d93bd103efba1a1ebd5a2cf891 2025-07-17T08:34:16.2030253Z * [new tag] trunk/f17f6581252ff07e75f33fdce0b4248acdb5da0b -> trunk/f17f6581252ff07e75f33fdce0b4248acdb5da0b 2025-07-17T08:34:16.2032258Z * [new tag] trunk/f1f49e56b079bfc73b1b2fb061d150ec787ee716 -> trunk/f1f49e56b079bfc73b1b2fb061d150ec787ee716 2025-07-17T08:34:16.2034347Z * [new tag] trunk/f2b44424a1f3481ddc066e8dd62c0cafa1298e92 -> trunk/f2b44424a1f3481ddc066e8dd62c0cafa1298e92 2025-07-17T08:34:16.2036500Z * [new tag] trunk/f2d70898c6fad6c6e867d9bf60eced6aacfa9782 -> trunk/f2d70898c6fad6c6e867d9bf60eced6aacfa9782 2025-07-17T08:34:16.2038556Z * [new tag] trunk/f2e712ca14dbbb8435427fd536b0dd63994a9265 -> trunk/f2e712ca14dbbb8435427fd536b0dd63994a9265 2025-07-17T08:34:16.2040696Z * [new tag] trunk/f2ecf6145fde55baa8a91e27b6b3489172f0e639 -> trunk/f2ecf6145fde55baa8a91e27b6b3489172f0e639 2025-07-17T08:34:16.2042779Z * [new tag] trunk/f34335bf3373301ba3af3ff8268cd0cd1715bd2e -> trunk/f34335bf3373301ba3af3ff8268cd0cd1715bd2e 2025-07-17T08:34:16.2044825Z * [new tag] trunk/f34ab1628b6798cc62f55260dfad872f5df8fd94 -> trunk/f34ab1628b6798cc62f55260dfad872f5df8fd94 2025-07-17T08:34:16.2046955Z * [new tag] trunk/f3e6c8e834a49f738e329b802b4b10912e33915f -> trunk/f3e6c8e834a49f738e329b802b4b10912e33915f 2025-07-17T08:34:16.2049126Z * [new tag] trunk/f3ec16c26a24bcbdcb96c692e321be1a4464067c -> trunk/f3ec16c26a24bcbdcb96c692e321be1a4464067c 2025-07-17T08:34:16.2051210Z * [new tag] trunk/f402eed4d9515dee5bb9b11a2b157de57a5988c1 -> trunk/f402eed4d9515dee5bb9b11a2b157de57a5988c1 2025-07-17T08:34:16.2053336Z * [new tag] trunk/f40efde2a474ffd77c021686da2f14864d6aeb6d -> trunk/f40efde2a474ffd77c021686da2f14864d6aeb6d 2025-07-17T08:34:16.2055587Z * [new tag] trunk/f41d017aa6ca1bd121cee6e428875b7fd31a7ad0 -> trunk/f41d017aa6ca1bd121cee6e428875b7fd31a7ad0 2025-07-17T08:34:16.2057711Z * [new tag] trunk/f4376cac54978b9d7c859c1e3495a57d72ab9601 -> trunk/f4376cac54978b9d7c859c1e3495a57d72ab9601 2025-07-17T08:34:16.2059790Z * [new tag] trunk/f4406689b8805ed23dfef34c6320bb99ea6d3767 -> trunk/f4406689b8805ed23dfef34c6320bb99ea6d3767 2025-07-17T08:34:16.2061891Z * [new tag] trunk/f44a9eee4778d2a2aafe3a0beeea7a37ab8d380e -> trunk/f44a9eee4778d2a2aafe3a0beeea7a37ab8d380e 2025-07-17T08:34:16.2064081Z * [new tag] trunk/f45f483884ef524f2d8260dcf2b2ba2c203eab2f -> trunk/f45f483884ef524f2d8260dcf2b2ba2c203eab2f 2025-07-17T08:34:16.2067006Z * [new tag] trunk/f45f6e86b9ef3d9c94b87c2240dbe9a07bb9fd3d -> trunk/f45f6e86b9ef3d9c94b87c2240dbe9a07bb9fd3d 2025-07-17T08:34:16.2069067Z * [new tag] trunk/f48a1576606186cf2104c82be367b617912322de -> trunk/f48a1576606186cf2104c82be367b617912322de 2025-07-17T08:34:16.2071182Z * [new tag] trunk/f4d60a68dd2fb9fda92af83df016f4cfe3af00ba -> trunk/f4d60a68dd2fb9fda92af83df016f4cfe3af00ba 2025-07-17T08:34:16.2073334Z * [new tag] trunk/f56bfb3030acff03e21d568089f3af9e09ec7cb2 -> trunk/f56bfb3030acff03e21d568089f3af9e09ec7cb2 2025-07-17T08:34:16.2075493Z * [new tag] trunk/f58a680d09e13658a52c6ba05c63c15759846bcc -> trunk/f58a680d09e13658a52c6ba05c63c15759846bcc 2025-07-17T08:34:16.2077757Z * [new tag] trunk/f59c76b5494f83abbb2ca169e13201439396c1aa -> trunk/f59c76b5494f83abbb2ca169e13201439396c1aa 2025-07-17T08:34:16.2079673Z * [new tag] trunk/f5bbaa22536437e9f689db8ee7a5ca472fdf4bd0 -> trunk/f5bbaa22536437e9f689db8ee7a5ca472fdf4bd0 2025-07-17T08:34:16.2081751Z * [new tag] trunk/f5e1b24945cf7852a1425923ca543e1f83be14b1 -> trunk/f5e1b24945cf7852a1425923ca543e1f83be14b1 2025-07-17T08:34:16.2083810Z * [new tag] trunk/f5e6e52f2504d0d2c553fd292c89d999b2033616 -> trunk/f5e6e52f2504d0d2c553fd292c89d999b2033616 2025-07-17T08:34:16.2085796Z * [new tag] trunk/f5eb42e4c0405141134e9c52919ebb36ec77ca5f -> trunk/f5eb42e4c0405141134e9c52919ebb36ec77ca5f 2025-07-17T08:34:16.2088079Z * [new tag] trunk/f5f4beaf562911b5cf1a9e8b9eae5e5946681f3c -> trunk/f5f4beaf562911b5cf1a9e8b9eae5e5946681f3c 2025-07-17T08:34:16.2090165Z * [new tag] trunk/f6d138807f138868de0397936e2bee482c1fb987 -> trunk/f6d138807f138868de0397936e2bee482c1fb987 2025-07-17T08:34:16.2092338Z * [new tag] trunk/f6e18bc1054624bb148632a85c10371d6cc62492 -> trunk/f6e18bc1054624bb148632a85c10371d6cc62492 2025-07-17T08:34:16.2094426Z * [new tag] trunk/f70c80105ebc2a118af848c80a18d6efff820f72 -> trunk/f70c80105ebc2a118af848c80a18d6efff820f72 2025-07-17T08:34:16.2096469Z * [new tag] trunk/f7127b9b940a98596599acda1f89fc5153635a5d -> trunk/f7127b9b940a98596599acda1f89fc5153635a5d 2025-07-17T08:34:16.2098572Z * [new tag] trunk/f7130c097efa826313df44f0dcfa7d4d2e4253ec -> trunk/f7130c097efa826313df44f0dcfa7d4d2e4253ec 2025-07-17T08:34:16.2100522Z * [new tag] trunk/f742b32a2ff6326f67512d2b426cdc2da8413a75 -> trunk/f742b32a2ff6326f67512d2b426cdc2da8413a75 2025-07-17T08:34:16.2102449Z * [new tag] trunk/f79689bd3d145e76746e4cf81e99c40df8272a72 -> trunk/f79689bd3d145e76746e4cf81e99c40df8272a72 2025-07-17T08:34:16.2104565Z * [new tag] trunk/f7a5ad6c2987ee5a83aa5d868cee3b8067d3de94 -> trunk/f7a5ad6c2987ee5a83aa5d868cee3b8067d3de94 2025-07-17T08:34:16.2106711Z * [new tag] trunk/f80a61adf57badf999387efe47b8b2ff3388bd96 -> trunk/f80a61adf57badf999387efe47b8b2ff3388bd96 2025-07-17T08:34:16.2108933Z * [new tag] trunk/f810480dbefabbff6cf0852c9f610f84dd440b8d -> trunk/f810480dbefabbff6cf0852c9f610f84dd440b8d 2025-07-17T08:34:16.2111034Z * [new tag] trunk/f810e98143b7b1fe3436d5315101b22aa8438775 -> trunk/f810e98143b7b1fe3436d5315101b22aa8438775 2025-07-17T08:34:16.2113149Z * [new tag] trunk/f8293116f55a9ad399e4938f0fe3e34c6faa47c4 -> trunk/f8293116f55a9ad399e4938f0fe3e34c6faa47c4 2025-07-17T08:34:16.2115314Z * [new tag] trunk/f85954e043a5b6affb589012e8cc5eff8fdb5358 -> trunk/f85954e043a5b6affb589012e8cc5eff8fdb5358 2025-07-17T08:34:16.2117411Z * [new tag] trunk/f860992db5601a78f73eefc0a56af1d7158d6953 -> trunk/f860992db5601a78f73eefc0a56af1d7158d6953 2025-07-17T08:34:16.2119487Z * [new tag] trunk/f87d1179391d66854e3c6ca20717803cfa22f878 -> trunk/f87d1179391d66854e3c6ca20717803cfa22f878 2025-07-17T08:34:16.2121622Z * [new tag] trunk/f88d7a7a34d5a54d58f0a7edc01ff69f46221b6c -> trunk/f88d7a7a34d5a54d58f0a7edc01ff69f46221b6c 2025-07-17T08:34:16.2123761Z * [new tag] trunk/f8baec8984ed90b526fdc03eec19d8039e7d373b -> trunk/f8baec8984ed90b526fdc03eec19d8039e7d373b 2025-07-17T08:34:16.2126023Z * [new tag] trunk/f8c0a4bd28087b02958b92d7b4f41ebc607292b7 -> trunk/f8c0a4bd28087b02958b92d7b4f41ebc607292b7 2025-07-17T08:34:16.2128024Z * [new tag] trunk/f8cc4c0af802269fbe16a418386a94b3b6547c74 -> trunk/f8cc4c0af802269fbe16a418386a94b3b6547c74 2025-07-17T08:34:16.2130070Z * [new tag] trunk/f97f03c7efcf2b7a45384b9094eb6be4cb419546 -> trunk/f97f03c7efcf2b7a45384b9094eb6be4cb419546 2025-07-17T08:34:16.2132273Z * [new tag] trunk/fa0ea57f5e083daab1eb9cda39ab53a5159b077d -> trunk/fa0ea57f5e083daab1eb9cda39ab53a5159b077d 2025-07-17T08:34:16.2134079Z * [new tag] trunk/fa1c20ae9285f7994a73d2d06025065f96b67a57 -> trunk/fa1c20ae9285f7994a73d2d06025065f96b67a57 2025-07-17T08:34:16.2136298Z * [new tag] trunk/fa3c38c7ae17b8d8fccd0958831f9f1ced9e46b3 -> trunk/fa3c38c7ae17b8d8fccd0958831f9f1ced9e46b3 2025-07-17T08:34:16.2138315Z * [new tag] trunk/fa4f07b5b80bdcf99a1c7452de41939d2ab5886f -> trunk/fa4f07b5b80bdcf99a1c7452de41939d2ab5886f 2025-07-17T08:34:16.2140461Z * [new tag] trunk/fa705f791249c5eee912096f1d7ac7b2d5b60e8c -> trunk/fa705f791249c5eee912096f1d7ac7b2d5b60e8c 2025-07-17T08:34:16.2142613Z * [new tag] trunk/fab53dfdf1d89cecd5e82b12cced9b6dd217e87c -> trunk/fab53dfdf1d89cecd5e82b12cced9b6dd217e87c 2025-07-17T08:34:16.2144662Z * [new tag] trunk/fab85fc5f995ef79e67dc4b083f8cfabe7a90798 -> trunk/fab85fc5f995ef79e67dc4b083f8cfabe7a90798 2025-07-17T08:34:16.2146769Z * [new tag] trunk/fac0cc16efd342637a3d1953caf2ecfa8a89947c -> trunk/fac0cc16efd342637a3d1953caf2ecfa8a89947c 2025-07-17T08:34:16.2148927Z * [new tag] trunk/fadc936fad0793e931ed2eb89577e1d10d212f71 -> trunk/fadc936fad0793e931ed2eb89577e1d10d212f71 2025-07-17T08:34:16.2150902Z * [new tag] trunk/fb45649df7267e97efffec8305cddcb23e97d53f -> trunk/fb45649df7267e97efffec8305cddcb23e97d53f 2025-07-17T08:34:16.2153007Z * [new tag] trunk/fb462cec8d8674ad547c55dbe90710bde1dc2019 -> trunk/fb462cec8d8674ad547c55dbe90710bde1dc2019 2025-07-17T08:34:16.2155114Z * [new tag] trunk/fb731fe371cb1b5bf95de84b19c213590526acb2 -> trunk/fb731fe371cb1b5bf95de84b19c213590526acb2 2025-07-17T08:34:16.2157290Z * [new tag] trunk/fb75dea2c1b93c78dccf08d5fd5e20b362ecd405 -> trunk/fb75dea2c1b93c78dccf08d5fd5e20b362ecd405 2025-07-17T08:34:16.2159496Z * [new tag] trunk/fb9a5d248f36ddce041025c8fc5be0d8bee454b0 -> trunk/fb9a5d248f36ddce041025c8fc5be0d8bee454b0 2025-07-17T08:34:16.2161619Z * [new tag] trunk/fbbab794ef6befbe7ffab3648e9b1f2042942fed -> trunk/fbbab794ef6befbe7ffab3648e9b1f2042942fed 2025-07-17T08:34:16.2163660Z * [new tag] trunk/fbd88ae2b5f444cb4b419c415258caeaef4b7325 -> trunk/fbd88ae2b5f444cb4b419c415258caeaef4b7325 2025-07-17T08:34:16.2165799Z * [new tag] trunk/fc0376e8b162d8fcf4375ae30566139eae7d48ed -> trunk/fc0376e8b162d8fcf4375ae30566139eae7d48ed 2025-07-17T08:34:16.2167985Z * [new tag] trunk/fc10d4b1d649f1460b587f3ab45ad4403d84518c -> trunk/fc10d4b1d649f1460b587f3ab45ad4403d84518c 2025-07-17T08:34:16.2170286Z * [new tag] trunk/fc177801afcbf12543b5e193e736b155f79d4ad3 -> trunk/fc177801afcbf12543b5e193e736b155f79d4ad3 2025-07-17T08:34:16.2172367Z * [new tag] trunk/fc5ae1229341ca2841a977197c644733c6a5c789 -> trunk/fc5ae1229341ca2841a977197c644733c6a5c789 2025-07-17T08:34:16.2174429Z * [new tag] trunk/fc772692622e89335dc28638c512bb33dbed1729 -> trunk/fc772692622e89335dc28638c512bb33dbed1729 2025-07-17T08:34:16.2176804Z * [new tag] trunk/fca7013f85d4e7ac07bdd461f490aa159feac6a0 -> trunk/fca7013f85d4e7ac07bdd461f490aa159feac6a0 2025-07-17T08:34:16.2178896Z * [new tag] trunk/fcbf7c749a839cc817927ceba8ea2887cc28dbf5 -> trunk/fcbf7c749a839cc817927ceba8ea2887cc28dbf5 2025-07-17T08:34:16.2180859Z * [new tag] trunk/fcc682be4bda58894a15fee1d9041c6043fea66f -> trunk/fcc682be4bda58894a15fee1d9041c6043fea66f 2025-07-17T08:34:16.2182916Z * [new tag] trunk/fd4bb29410c035b31ca55262c3012cadb1194aae -> trunk/fd4bb29410c035b31ca55262c3012cadb1194aae 2025-07-17T08:34:16.2185037Z * [new tag] trunk/fd4f704905f95b46c4c8fb4108461ff56ce750c5 -> trunk/fd4f704905f95b46c4c8fb4108461ff56ce750c5 2025-07-17T08:34:16.2192821Z * [new tag] trunk/fd8ea3c8a35e5b4aea3ebd712f2c7ee28c5a3655 -> trunk/fd8ea3c8a35e5b4aea3ebd712f2c7ee28c5a3655 2025-07-17T08:34:16.2194558Z * [new tag] trunk/fdc5b42a8fa2b15526c81f2c83f614e086056320 -> trunk/fdc5b42a8fa2b15526c81f2c83f614e086056320 2025-07-17T08:34:16.2196334Z * [new tag] trunk/fdf5d97fa8393f56aea2779877efd8a264ad5811 -> trunk/fdf5d97fa8393f56aea2779877efd8a264ad5811 2025-07-17T08:34:16.2198913Z * [new tag] trunk/fe1f1a38dff7c59ec0ec5b05fef058629845d061 -> trunk/fe1f1a38dff7c59ec0ec5b05fef058629845d061 2025-07-17T08:34:16.2201035Z * [new tag] trunk/fec571cfd458c4644a2f483dbf9f5480feca7939 -> trunk/fec571cfd458c4644a2f483dbf9f5480feca7939 2025-07-17T08:34:16.2203371Z * [new tag] trunk/fec8af8b98f5e17740ff947e9f8a1c447a497413 -> trunk/fec8af8b98f5e17740ff947e9f8a1c447a497413 2025-07-17T08:34:16.2205325Z * [new tag] trunk/fedbd1a48e1e474cf9da5637aae89b5bc4c20626 -> trunk/fedbd1a48e1e474cf9da5637aae89b5bc4c20626 2025-07-17T08:34:16.2207414Z * [new tag] trunk/fee2377f9ea62223f69ea9904c5e25ccb2af5961 -> trunk/fee2377f9ea62223f69ea9904c5e25ccb2af5961 2025-07-17T08:34:16.2209362Z * [new tag] trunk/feea575082439a0496dd404a4925b7d551039065 -> trunk/feea575082439a0496dd404a4925b7d551039065 2025-07-17T08:34:16.2211560Z * [new tag] trunk/ff611d971fe5362a71c15109cf020d30e6c4b2b9 -> trunk/ff611d971fe5362a71c15109cf020d30e6c4b2b9 2025-07-17T08:34:16.2213753Z * [new tag] trunk/ff7dd1776f9cb8448336338b19b9b53073f2fbda -> trunk/ff7dd1776f9cb8448336338b19b9b53073f2fbda 2025-07-17T08:34:16.2216344Z * [new tag] trunk/ff8b53c056e6556187690a37c944c92feb964d2d -> trunk/ff8b53c056e6556187690a37c944c92feb964d2d 2025-07-17T08:34:16.2218488Z * [new tag] trunk/ffac0de07e0173a073c92c157f43c515009c0de1 -> trunk/ffac0de07e0173a073c92c157f43c515009c0de1 2025-07-17T08:34:16.2220970Z * [new tag] trunk/ffaed8c569406839335bf46dafc4c3e8871e4b8a -> trunk/ffaed8c569406839335bf46dafc4c3e8871e4b8a 2025-07-17T08:34:16.2223068Z * [new tag] trunk/ffc6cbfaf78ca219092ce64dcf113377ae698300 -> trunk/ffc6cbfaf78ca219092ce64dcf113377ae698300 2025-07-17T08:34:16.2225330Z * [new tag] v0.1.1 -> v0.1.1 2025-07-17T08:34:16.2227552Z * [new tag] v0.1.10 -> v0.1.10 2025-07-17T08:34:16.2229716Z * [new tag] v0.1.11 -> v0.1.11 2025-07-17T08:34:16.2231894Z * [new tag] v0.1.12 -> v0.1.12 2025-07-17T08:34:16.2233944Z * [new tag] v0.1.2 -> v0.1.2 2025-07-17T08:34:16.2236197Z * [new tag] v0.1.3 -> v0.1.3 2025-07-17T08:34:16.2238230Z * [new tag] v0.1.4 -> v0.1.4 2025-07-17T08:34:16.2240377Z * [new tag] v0.1.5 -> v0.1.5 2025-07-17T08:34:16.2242744Z * [new tag] v0.1.6 -> v0.1.6 2025-07-17T08:34:16.2244844Z * [new tag] v0.1.7 -> v0.1.7 2025-07-17T08:34:16.2246898Z * [new tag] v0.1.8 -> v0.1.8 2025-07-17T08:34:16.2248957Z * [new tag] v0.1.9 -> v0.1.9 2025-07-17T08:34:16.2251192Z * [new tag] v0.2.0 -> v0.2.0 2025-07-17T08:34:16.2253350Z * [new tag] v0.3.0 -> v0.3.0 2025-07-17T08:34:16.2255582Z * [new tag] v0.3.1 -> v0.3.1 2025-07-17T08:34:16.2257762Z * [new tag] v0.4.0 -> v0.4.0 2025-07-17T08:34:16.2259943Z * [new tag] v0.4.1 -> v0.4.1 2025-07-17T08:34:16.2262153Z * [new tag] v1.0.0 -> v1.0.0 2025-07-17T08:34:16.2264383Z * [new tag] v1.0.0a0 -> v1.0.0a0 2025-07-17T08:34:16.2266625Z * [new tag] v1.0.1 -> v1.0.1 2025-07-17T08:34:16.2269018Z * [new tag] v1.0rc0 -> v1.0rc0 2025-07-17T08:34:16.2270827Z * [new tag] v1.0rc1 -> v1.0rc1 2025-07-17T08:34:16.2273055Z * [new tag] v1.1.0 -> v1.1.0 2025-07-17T08:34:16.2275305Z * [new tag] v1.1.0a0 -> v1.1.0a0 2025-07-17T08:34:16.2277670Z * [new tag] v1.10.0 -> v1.10.0 2025-07-17T08:34:16.2279949Z * [new tag] v1.10.0-rc1 -> v1.10.0-rc1 2025-07-17T08:34:16.2282144Z * [new tag] v1.10.0-rc2 -> v1.10.0-rc2 2025-07-17T08:34:16.2284080Z * [new tag] v1.10.0-rc3 -> v1.10.0-rc3 2025-07-17T08:34:16.2286364Z * [new tag] v1.10.1 -> v1.10.1 2025-07-17T08:34:16.2288291Z * [new tag] v1.10.1-rc1 -> v1.10.1-rc1 2025-07-17T08:34:16.2290248Z * [new tag] v1.10.2 -> v1.10.2 2025-07-17T08:34:16.2292195Z * [new tag] v1.10.2-rc1 -> v1.10.2-rc1 2025-07-17T08:34:16.2294374Z * [new tag] v1.11.0 -> v1.11.0 2025-07-17T08:34:16.2296555Z * [new tag] v1.11.0-rc1 -> v1.11.0-rc1 2025-07-17T08:34:16.2298764Z * [new tag] v1.11.0-rc2 -> v1.11.0-rc2 2025-07-17T08:34:16.2301017Z * [new tag] v1.11.0-rc3 -> v1.11.0-rc3 2025-07-17T08:34:16.2303246Z * [new tag] v1.11.0-rc4 -> v1.11.0-rc4 2025-07-17T08:34:16.2305572Z * [new tag] v1.11.0-rc5 -> v1.11.0-rc5 2025-07-17T08:34:16.2307955Z * [new tag] v1.11.0-rc6 -> v1.11.0-rc6 2025-07-17T08:34:16.2309594Z * [new tag] v1.11.0-rc7 -> v1.11.0-rc7 2025-07-17T08:34:16.2311982Z * [new tag] v1.12.0 -> v1.12.0 2025-07-17T08:34:16.2314078Z * [new tag] v1.12.0-rc1 -> v1.12.0-rc1 2025-07-17T08:34:16.2316193Z * [new tag] v1.12.0-rc2 -> v1.12.0-rc2 2025-07-17T08:34:16.2318395Z * [new tag] v1.12.0-rc3 -> v1.12.0-rc3 2025-07-17T08:34:16.2320623Z * [new tag] v1.12.0-rc4 -> v1.12.0-rc4 2025-07-17T08:34:16.2322779Z * [new tag] v1.12.0-rc5 -> v1.12.0-rc5 2025-07-17T08:34:16.2324998Z * [new tag] v1.12.0-rc6 -> v1.12.0-rc6 2025-07-17T08:34:16.2326984Z * [new tag] v1.12.0-rc7 -> v1.12.0-rc7 2025-07-17T08:34:16.2328942Z * [new tag] v1.12.0-rc8 -> v1.12.0-rc8 2025-07-17T08:34:16.2330994Z * [new tag] v1.12.1 -> v1.12.1 2025-07-17T08:34:16.2333323Z * [new tag] v1.12.1-rc1 -> v1.12.1-rc1 2025-07-17T08:34:16.2335585Z * [new tag] v1.12.1-rc2 -> v1.12.1-rc2 2025-07-17T08:34:16.2337814Z * [new tag] v1.12.1-rc3 -> v1.12.1-rc3 2025-07-17T08:34:16.2339940Z * [new tag] v1.12.1-rc4 -> v1.12.1-rc4 2025-07-17T08:34:16.2341917Z * [new tag] v1.12.1-rc5 -> v1.12.1-rc5 2025-07-17T08:34:16.2344191Z * [new tag] v1.13.0 -> v1.13.0 2025-07-17T08:34:16.2346519Z * [new tag] v1.13.0-rc1 -> v1.13.0-rc1 2025-07-17T08:34:16.2348566Z * [new tag] v1.13.0-rc2 -> v1.13.0-rc2 2025-07-17T08:34:16.2350649Z * [new tag] v1.13.0-rc3 -> v1.13.0-rc3 2025-07-17T08:34:16.2352938Z * [new tag] v1.13.0-rc4 -> v1.13.0-rc4 2025-07-17T08:34:16.2354867Z * [new tag] v1.13.0-rc5 -> v1.13.0-rc5 2025-07-17T08:34:16.2357152Z * [new tag] v1.13.0-rc6 -> v1.13.0-rc6 2025-07-17T08:34:16.2359000Z * [new tag] v1.13.1 -> v1.13.1 2025-07-17T08:34:16.2360963Z * [new tag] v1.13.1-rc1 -> v1.13.1-rc1 2025-07-17T08:34:16.2363139Z * [new tag] v1.2.0 -> v1.2.0 2025-07-17T08:34:16.2365367Z * [new tag] v1.2.0a0 -> v1.2.0a0 2025-07-17T08:34:16.2367529Z * [new tag] v1.3.0 -> v1.3.0 2025-07-17T08:34:16.2369705Z * [new tag] v1.3.0a0 -> v1.3.0a0 2025-07-17T08:34:16.2371659Z * [new tag] v1.3.1 -> v1.3.1 2025-07-17T08:34:16.2373771Z * [new tag] v1.4.0 -> v1.4.0 2025-07-17T08:34:16.2375962Z * [new tag] v1.4.0a0 -> v1.4.0a0 2025-07-17T08:34:16.2377866Z * [new tag] v1.4.1 -> v1.4.1 2025-07-17T08:34:16.2380190Z * [new tag] v1.5.0 -> v1.5.0 2025-07-17T08:34:16.2382398Z * [new tag] v1.5.0-rc1 -> v1.5.0-rc1 2025-07-17T08:34:16.2384638Z * [new tag] v1.5.0-rc2 -> v1.5.0-rc2 2025-07-17T08:34:16.2386925Z * [new tag] v1.5.0-rc3 -> v1.5.0-rc3 2025-07-17T08:34:16.2388977Z * [new tag] v1.5.0-rc4 -> v1.5.0-rc4 2025-07-17T08:34:16.2390924Z * [new tag] v1.5.0-rc5 -> v1.5.0-rc5 2025-07-17T08:34:16.2393253Z * [new tag] v1.5.1 -> v1.5.1 2025-07-17T08:34:16.2395192Z * [new tag] v1.5.1-rc1 -> v1.5.1-rc1 2025-07-17T08:34:16.2397133Z * [new tag] v1.6.0 -> v1.6.0 2025-07-17T08:34:16.2399414Z * [new tag] v1.6.0-rc1 -> v1.6.0-rc1 2025-07-17T08:34:16.2401565Z * [new tag] v1.6.0-rc2 -> v1.6.0-rc2 2025-07-17T08:34:16.2403876Z * [new tag] v1.6.0-rc3 -> v1.6.0-rc3 2025-07-17T08:34:16.2406079Z * [new tag] v1.6.0-rc4 -> v1.6.0-rc4 2025-07-17T08:34:16.2408184Z * [new tag] v1.6.0-rc5 -> v1.6.0-rc5 2025-07-17T08:34:16.2410304Z * [new tag] v1.6.0-rc6 -> v1.6.0-rc6 2025-07-17T08:34:16.2412253Z * [new tag] v1.6.0-rc7 -> v1.6.0-rc7 2025-07-17T08:34:16.2414426Z * [new tag] v1.7.0 -> v1.7.0 2025-07-17T08:34:16.2416648Z * [new tag] v1.7.0-rc1 -> v1.7.0-rc1 2025-07-17T08:34:16.2418882Z * [new tag] v1.7.0-rc2 -> v1.7.0-rc2 2025-07-17T08:34:16.2420955Z * [new tag] v1.7.0-rc3 -> v1.7.0-rc3 2025-07-17T08:34:16.2422925Z * [new tag] v1.7.0-rc4 -> v1.7.0-rc4 2025-07-17T08:34:16.2425153Z * [new tag] v1.7.1 -> v1.7.1 2025-07-17T08:34:16.2427521Z * [new tag] v1.7.1-rc1 -> v1.7.1-rc1 2025-07-17T08:34:16.2429743Z * [new tag] v1.7.1-rc2 -> v1.7.1-rc2 2025-07-17T08:34:16.2431660Z * [new tag] v1.7.1-rc3 -> v1.7.1-rc3 2025-07-17T08:34:16.2433847Z * [new tag] v1.8.0 -> v1.8.0 2025-07-17T08:34:16.2435801Z * [new tag] v1.8.0-rc1 -> v1.8.0-rc1 2025-07-17T08:34:16.2437961Z * [new tag] v1.8.0-rc2 -> v1.8.0-rc2 2025-07-17T08:34:16.2440166Z * [new tag] v1.8.0-rc3 -> v1.8.0-rc3 2025-07-17T08:34:16.2442260Z * [new tag] v1.8.0-rc4 -> v1.8.0-rc4 2025-07-17T08:34:16.2444394Z * [new tag] v1.8.0-rc5 -> v1.8.0-rc5 2025-07-17T08:34:16.2446097Z * [new tag] v1.8.1 -> v1.8.1 2025-07-17T08:34:16.2448352Z * [new tag] v1.8.1-rc1 -> v1.8.1-rc1 2025-07-17T08:34:16.2450437Z * [new tag] v1.8.1-rc2 -> v1.8.1-rc2 2025-07-17T08:34:16.2452270Z * [new tag] v1.8.1-rc3 -> v1.8.1-rc3 2025-07-17T08:34:16.2454853Z * [new tag] v1.8.2 -> v1.8.2 2025-07-17T08:34:16.2456893Z * [new tag] v1.8.2-rc1 -> v1.8.2-rc1 2025-07-17T08:34:16.2459402Z * [new tag] v1.9.0 -> v1.9.0 2025-07-17T08:34:16.2461408Z * [new tag] v1.9.0-rc1 -> v1.9.0-rc1 2025-07-17T08:34:16.2463593Z * [new tag] v1.9.0-rc2 -> v1.9.0-rc2 2025-07-17T08:34:16.2466115Z * [new tag] v1.9.0-rc3 -> v1.9.0-rc3 2025-07-17T08:34:16.2468122Z * [new tag] v1.9.0-rc4 -> v1.9.0-rc4 2025-07-17T08:34:16.2470307Z * [new tag] v1.9.1 -> v1.9.1 2025-07-17T08:34:16.2472625Z * [new tag] v1.9.1-rc1 -> v1.9.1-rc1 2025-07-17T08:34:16.2474611Z * [new tag] v1.9.1-rc2 -> v1.9.1-rc2 2025-07-17T08:34:16.2476832Z * [new tag] v2.0.0 -> v2.0.0 2025-07-17T08:34:16.2478971Z * [new tag] v2.0.0-rc1 -> v2.0.0-rc1 2025-07-17T08:34:16.2481282Z * [new tag] v2.0.0-rc2 -> v2.0.0-rc2 2025-07-17T08:34:16.2483617Z * [new tag] v2.0.0-rc3 -> v2.0.0-rc3 2025-07-17T08:34:16.2485877Z * [new tag] v2.0.0-rc4 -> v2.0.0-rc4 2025-07-17T08:34:16.2488101Z * [new tag] v2.0.0-rc5 -> v2.0.0-rc5 2025-07-17T08:34:16.2490151Z * [new tag] v2.0.0-rc6 -> v2.0.0-rc6 2025-07-17T08:34:16.2492504Z * [new tag] v2.0.1 -> v2.0.1 2025-07-17T08:34:16.2494844Z * [new tag] v2.0.1-rc1 -> v2.0.1-rc1 2025-07-17T08:34:16.2496915Z * [new tag] v2.0.1-rc2 -> v2.0.1-rc2 2025-07-17T08:34:16.2499015Z * [new tag] v2.0.1-rc3 -> v2.0.1-rc3 2025-07-17T08:34:16.2501027Z * [new tag] v2.0.1-rc4 -> v2.0.1-rc4 2025-07-17T08:34:16.2503702Z * [new tag] v2.1.0 -> v2.1.0 2025-07-17T08:34:16.2505940Z * [new tag] v2.1.0-rc1 -> v2.1.0-rc1 2025-07-17T08:34:16.2508119Z * [new tag] v2.1.0-rc2 -> v2.1.0-rc2 2025-07-17T08:34:16.2510536Z * [new tag] v2.1.0-rc3 -> v2.1.0-rc3 2025-07-17T08:34:16.2512854Z * [new tag] v2.1.0-rc4 -> v2.1.0-rc4 2025-07-17T08:34:16.2515106Z * [new tag] v2.1.0-rc5 -> v2.1.0-rc5 2025-07-17T08:34:16.2517242Z * [new tag] v2.1.0-rc6 -> v2.1.0-rc6 2025-07-17T08:34:16.2519604Z * [new tag] v2.1.1 -> v2.1.1 2025-07-17T08:34:16.2521917Z * [new tag] v2.1.1-rc1 -> v2.1.1-rc1 2025-07-17T08:34:16.2524192Z * [new tag] v2.1.1-rc2 -> v2.1.1-rc2 2025-07-17T08:34:16.2526445Z * [new tag] v2.1.1-rc3 -> v2.1.1-rc3 2025-07-17T08:34:16.2528708Z * [new tag] v2.1.1-rc4 -> v2.1.1-rc4 2025-07-17T08:34:16.2531294Z * [new tag] v2.1.1-rc5 -> v2.1.1-rc5 2025-07-17T08:34:16.2533164Z * [new tag] v2.1.1-rc6 -> v2.1.1-rc6 2025-07-17T08:34:16.2535460Z * [new tag] v2.1.2 -> v2.1.2 2025-07-17T08:34:16.2538071Z * [new tag] v2.1.2-rc1 -> v2.1.2-rc1 2025-07-17T08:34:16.2540130Z * [new tag] v2.1.2-rc2 -> v2.1.2-rc2 2025-07-17T08:34:16.2542164Z * [new tag] v2.1.2-rc3 -> v2.1.2-rc3 2025-07-17T08:34:16.2544573Z * [new tag] v2.2.0 -> v2.2.0 2025-07-17T08:34:16.2546961Z * [new tag] v2.2.0-rc1 -> v2.2.0-rc1 2025-07-17T08:34:16.2549191Z * [new tag] v2.2.0-rc2 -> v2.2.0-rc2 2025-07-17T08:34:16.2551350Z * [new tag] v2.2.0-rc3 -> v2.2.0-rc3 2025-07-17T08:34:16.2553651Z * [new tag] v2.2.0-rc4 -> v2.2.0-rc4 2025-07-17T08:34:16.2555990Z * [new tag] v2.2.0-rc5 -> v2.2.0-rc5 2025-07-17T08:34:16.2558496Z * [new tag] v2.2.0-rc6 -> v2.2.0-rc6 2025-07-17T08:34:16.2560498Z * [new tag] v2.2.0-rc7 -> v2.2.0-rc7 2025-07-17T08:34:16.2562500Z * [new tag] v2.2.0-rc8 -> v2.2.0-rc8 2025-07-17T08:34:16.2564655Z * [new tag] v2.2.1 -> v2.2.1 2025-07-17T08:34:16.2566882Z * [new tag] v2.2.1-rc1 -> v2.2.1-rc1 2025-07-17T08:34:16.2569002Z * [new tag] v2.2.1-rc2 -> v2.2.1-rc2 2025-07-17T08:34:16.2571024Z * [new tag] v2.2.1-rc3 -> v2.2.1-rc3 2025-07-17T08:34:16.2573012Z * [new tag] v2.2.2 -> v2.2.2 2025-07-17T08:34:16.2575250Z * [new tag] v2.2.2-rc1 -> v2.2.2-rc1 2025-07-17T08:34:16.2577232Z * [new tag] v2.2.2-rc2 -> v2.2.2-rc2 2025-07-17T08:34:16.2579176Z * [new tag] v2.2.2-rc3 -> v2.2.2-rc3 2025-07-17T08:34:16.2581393Z * [new tag] v2.3.0 -> v2.3.0 2025-07-17T08:34:16.2583508Z * [new tag] v2.3.0-rc1 -> v2.3.0-rc1 2025-07-17T08:34:16.2585719Z * [new tag] v2.3.0-rc10 -> v2.3.0-rc10 2025-07-17T08:34:16.2588051Z * [new tag] v2.3.0-rc11 -> v2.3.0-rc11 2025-07-17T08:34:16.2590275Z * [new tag] v2.3.0-rc12 -> v2.3.0-rc12 2025-07-17T08:34:16.2592356Z * [new tag] v2.3.0-rc2 -> v2.3.0-rc2 2025-07-17T08:34:16.2594492Z * [new tag] v2.3.0-rc3 -> v2.3.0-rc3 2025-07-17T08:34:16.2596641Z * [new tag] v2.3.0-rc4 -> v2.3.0-rc4 2025-07-17T08:34:16.2598722Z * [new tag] v2.3.0-rc5 -> v2.3.0-rc5 2025-07-17T08:34:16.2600670Z * [new tag] v2.3.0-rc6 -> v2.3.0-rc6 2025-07-17T08:34:16.2602803Z * [new tag] v2.3.0-rc7 -> v2.3.0-rc7 2025-07-17T08:34:16.2604966Z * [new tag] v2.3.0-rc8 -> v2.3.0-rc8 2025-07-17T08:34:16.2606837Z * [new tag] v2.3.0-rc9 -> v2.3.0-rc9 2025-07-17T08:34:16.2608804Z * [new tag] v2.3.1 -> v2.3.1 2025-07-17T08:34:16.2611060Z * [new tag] v2.3.1-rc1 -> v2.3.1-rc1 2025-07-17T08:34:16.2613129Z * [new tag] v2.3.1-rc2 -> v2.3.1-rc2 2025-07-17T08:34:16.2615981Z * [new tag] v2.3.1-rc3 -> v2.3.1-rc3 2025-07-17T08:34:16.2618192Z * [new tag] v2.4.0 -> v2.4.0 2025-07-17T08:34:16.2620307Z * [new tag] v2.4.0-rc1 -> v2.4.0-rc1 2025-07-17T08:34:16.2622515Z * [new tag] v2.4.0-rc2 -> v2.4.0-rc2 2025-07-17T08:34:16.2624611Z * [new tag] v2.4.0-rc3 -> v2.4.0-rc3 2025-07-17T08:34:16.2627049Z * [new tag] v2.4.0-rc4 -> v2.4.0-rc4 2025-07-17T08:34:16.2629219Z * [new tag] v2.4.0-rc5 -> v2.4.0-rc5 2025-07-17T08:34:16.2631536Z * [new tag] v2.4.0-rc6 -> v2.4.0-rc6 2025-07-17T08:34:16.2633834Z * [new tag] v2.4.0-rc7 -> v2.4.0-rc7 2025-07-17T08:34:16.2636189Z * [new tag] v2.4.0-rc8 -> v2.4.0-rc8 2025-07-17T08:34:16.2638604Z * [new tag] v2.4.0-rc9 -> v2.4.0-rc9 2025-07-17T08:34:16.2640799Z * [new tag] v2.4.1 -> v2.4.1 2025-07-17T08:34:16.2643324Z * [new tag] v2.4.1-rc1 -> v2.4.1-rc1 2025-07-17T08:34:16.2645607Z * [new tag] v2.4.1-rc2 -> v2.4.1-rc2 2025-07-17T08:34:16.2648025Z * [new tag] v2.4.1-rc3 -> v2.4.1-rc3 2025-07-17T08:34:16.2650397Z * [new tag] v2.5.0 -> v2.5.0 2025-07-17T08:34:16.2652746Z * [new tag] v2.5.0-rc1 -> v2.5.0-rc1 2025-07-17T08:34:16.2654957Z * [new tag] v2.5.0-rc10 -> v2.5.0-rc10 2025-07-17T08:34:16.2657226Z * [new tag] v2.5.0-rc2 -> v2.5.0-rc2 2025-07-17T08:34:16.2659504Z * [new tag] v2.5.0-rc3 -> v2.5.0-rc3 2025-07-17T08:34:16.2661804Z * [new tag] v2.5.0-rc4 -> v2.5.0-rc4 2025-07-17T08:34:16.2664067Z * [new tag] v2.5.0-rc5 -> v2.5.0-rc5 2025-07-17T08:34:16.2666446Z * [new tag] v2.5.0-rc6 -> v2.5.0-rc6 2025-07-17T08:34:16.2668628Z * [new tag] v2.5.0-rc7 -> v2.5.0-rc7 2025-07-17T08:34:16.2670865Z * [new tag] v2.5.0-rc8 -> v2.5.0-rc8 2025-07-17T08:34:16.2673031Z * [new tag] v2.5.0-rc9 -> v2.5.0-rc9 2025-07-17T08:34:16.2675026Z * [new tag] v2.5.1 -> v2.5.1 2025-07-17T08:34:16.2676920Z * [new tag] v2.5.1-rc1 -> v2.5.1-rc1 2025-07-17T08:34:16.2678858Z * [new tag] v2.6.0 -> v2.6.0 2025-07-17T08:34:16.2681193Z * [new tag] v2.6.0-rc1 -> v2.6.0-rc1 2025-07-17T08:34:16.2683286Z * [new tag] v2.6.0-rc2 -> v2.6.0-rc2 2025-07-17T08:34:16.2685488Z * [new tag] v2.6.0-rc3 -> v2.6.0-rc3 2025-07-17T08:34:16.2687558Z * [new tag] v2.6.0-rc4 -> v2.6.0-rc4 2025-07-17T08:34:16.2689875Z * [new tag] v2.6.0-rc5 -> v2.6.0-rc5 2025-07-17T08:34:16.2692154Z * [new tag] v2.6.0-rc6 -> v2.6.0-rc6 2025-07-17T08:34:16.2694298Z * [new tag] v2.6.0-rc7 -> v2.6.0-rc7 2025-07-17T08:34:16.2696616Z * [new tag] v2.6.0-rc8 -> v2.6.0-rc8 2025-07-17T08:34:16.2698733Z * [new tag] v2.6.0-rc9 -> v2.6.0-rc9 2025-07-17T08:34:16.2701097Z * [new tag] v2.7.0 -> v2.7.0 2025-07-17T08:34:16.2703335Z * [new tag] v2.7.0-rc1 -> v2.7.0-rc1 2025-07-17T08:34:16.2705405Z * [new tag] v2.7.0-rc10 -> v2.7.0-rc10 2025-07-17T08:34:16.2707776Z * [new tag] v2.7.0-rc2 -> v2.7.0-rc2 2025-07-17T08:34:16.2709980Z * [new tag] v2.7.0-rc3 -> v2.7.0-rc3 2025-07-17T08:34:16.2712140Z * [new tag] v2.7.0-rc4 -> v2.7.0-rc4 2025-07-17T08:34:16.2714279Z * [new tag] v2.7.0-rc5 -> v2.7.0-rc5 2025-07-17T08:34:16.2716404Z * [new tag] v2.7.0-rc6 -> v2.7.0-rc6 2025-07-17T08:34:16.2718776Z * [new tag] v2.7.0-rc7 -> v2.7.0-rc7 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You can look around, make experimental 2025-07-17T08:34:18.7689626Z changes and commit them, and you can discard any commits you make in this 2025-07-17T08:34:18.7689986Z state without impacting any branches by switching back to a branch. 2025-07-17T08:34:18.7690173Z 2025-07-17T08:34:18.7690316Z If you want to create a new branch to retain commits you create, you may 2025-07-17T08:34:18.7690616Z do so (now or later) by using -c with the switch command. Example: 2025-07-17T08:34:18.7690796Z 2025-07-17T08:34:18.7690888Z git switch -c 2025-07-17T08:34:18.7691020Z 2025-07-17T08:34:18.7691103Z Or undo this operation with: 2025-07-17T08:34:18.7691216Z 2025-07-17T08:34:18.7691276Z git switch - 2025-07-17T08:34:18.7691378Z 2025-07-17T08:34:18.7691533Z Turn off this advice by setting config variable advice.detachedHead to false 2025-07-17T08:34:18.7691738Z 2025-07-17T08:34:18.7691905Z HEAD is now at a38f433be2e [Docker builds] Move from Miniconda to Miniforge (#158370) 2025-07-17T08:34:18.7839104Z ##[endgroup] 2025-07-17T08:34:18.7839431Z ##[group]Setting up auth for fetching submodules 2025-07-17T08:34:18.7844903Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-07-17T08:34:18.7890418Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf 2025-07-17T08:34:18.7924534Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com: 2025-07-17T08:34:18.7959648Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com: 2025-07-17T08:34:18.7986179Z ##[endgroup] 2025-07-17T08:34:18.7986571Z ##[group]Fetching submodules 2025-07-17T08:34:18.7989249Z [command]/usr/bin/git submodule sync --recursive 2025-07-17T08:34:18.8352701Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --recursive 2025-07-17T08:34:18.8657883Z Submodule 'android/libs/fbjni' (https://github.com/facebookincubator/fbjni.git) registered for path 'android/libs/fbjni' 2025-07-17T08:34:18.8662867Z Submodule 'third_party/NNPACK_deps/FP16' (https://github.com/Maratyszcza/FP16.git) registered for path 'third_party/FP16' 2025-07-17T08:34:18.8669096Z Submodule 'third_party/NNPACK_deps/FXdiv' (https://github.com/Maratyszcza/FXdiv.git) registered for path 'third_party/FXdiv' 2025-07-17T08:34:18.8675438Z Submodule 'third_party/NNPACK' (https://github.com/Maratyszcza/NNPACK.git) registered for path 'third_party/NNPACK' 2025-07-17T08:34:18.8681956Z Submodule 'third_party/NVTX' (https://github.com/NVIDIA/NVTX.git) registered for path 'third_party/NVTX' 2025-07-17T08:34:18.8688781Z Submodule 'third_party/VulkanMemoryAllocator' (https://github.com/GPUOpen-LibrariesAndSDKs/VulkanMemoryAllocator.git) registered for path 'third_party/VulkanMemoryAllocator' 2025-07-17T08:34:18.8695325Z Submodule 'third_party/XNNPACK' (https://github.com/google/XNNPACK.git) registered for path 'third_party/XNNPACK' 2025-07-17T08:34:18.8702115Z Submodule 'third_party/aiter' (https://github.com/ROCm/aiter.git) registered for path 'third_party/aiter' 2025-07-17T08:34:18.8709200Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/benchmark' 2025-07-17T08:34:18.8716105Z Submodule 'third_party/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/composable_kernel' 2025-07-17T08:34:18.8722965Z Submodule 'third_party/cpp-httplib' (https://github.com/yhirose/cpp-httplib.git) registered for path 'third_party/cpp-httplib' 2025-07-17T08:34:18.8730093Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo.git) registered for path 'third_party/cpuinfo' 2025-07-17T08:34:18.8737464Z Submodule 'third_party/cudnn_frontend' (https://github.com/NVIDIA/cudnn-frontend.git) registered for path 'third_party/cudnn_frontend' 2025-07-17T08:34:18.8744611Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/cutlass' 2025-07-17T08:34:18.8752030Z Submodule 'third_party/fbgemm' (https://github.com/pytorch/fbgemm) registered for path 'third_party/fbgemm' 2025-07-17T08:34:18.8759327Z Submodule 'third_party/flash-attention' (https://github.com/Dao-AILab/flash-attention.git) registered for path 'third_party/flash-attention' 2025-07-17T08:34:18.8766711Z Submodule 'third_party/flatbuffers' (https://github.com/google/flatbuffers.git) registered for path 'third_party/flatbuffers' 2025-07-17T08:34:18.8774193Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/fmt' 2025-07-17T08:34:18.8781823Z Submodule 'third_party/gemmlowp/gemmlowp' (https://github.com/google/gemmlowp.git) registered for path 'third_party/gemmlowp/gemmlowp' 2025-07-17T08:34:18.8789697Z Submodule 'third_party/gloo' (https://github.com/pytorch/gloo) registered for path 'third_party/gloo' 2025-07-17T08:34:18.8797285Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/googletest' 2025-07-17T08:34:18.8804909Z Submodule 'third_party/ideep' (https://github.com/intel/ideep) registered for path 'third_party/ideep' 2025-07-17T08:34:18.8813298Z Submodule 'third_party/ittapi' (https://github.com/intel/ittapi.git) registered for path 'third_party/ittapi' 2025-07-17T08:34:18.8820694Z Submodule 'third_party/kineto' (https://github.com/pytorch/kineto) registered for path 'third_party/kineto' 2025-07-17T08:34:18.8828731Z Submodule 'third_party/kleidiai' (https://github.com/ARM-software/kleidiai.git) registered for path 'third_party/kleidiai' 2025-07-17T08:34:18.8836679Z Submodule 'third_party/mimalloc' (https://github.com/microsoft/mimalloc.git) registered for path 'third_party/mimalloc' 2025-07-17T08:34:18.8844652Z Submodule 'third_party/nlohmann' (https://github.com/nlohmann/json.git) registered for path 'third_party/nlohmann' 2025-07-17T08:34:18.8852733Z Submodule 'third_party/onnx' (https://github.com/onnx/onnx.git) registered for path 'third_party/onnx' 2025-07-17T08:34:18.8861238Z Submodule 'third_party/opentelemetry-cpp' (https://github.com/open-telemetry/opentelemetry-cpp.git) registered for path 'third_party/opentelemetry-cpp' 2025-07-17T08:34:18.8869700Z Submodule 'third_party/pocketfft' (https://github.com/mreineck/pocketfft) registered for path 'third_party/pocketfft' 2025-07-17T08:34:18.8878044Z Submodule 'third_party/protobuf' (https://github.com/protocolbuffers/protobuf.git) registered for path 'third_party/protobuf' 2025-07-17T08:34:18.8886405Z Submodule 'third_party/NNPACK_deps/psimd' (https://github.com/Maratyszcza/psimd.git) registered for path 'third_party/psimd' 2025-07-17T08:34:18.8902096Z Submodule 'third_party/NNPACK_deps/pthreadpool' (https://github.com/Maratyszcza/pthreadpool.git) registered for path 'third_party/pthreadpool' 2025-07-17T08:34:18.8903652Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/pybind11' 2025-07-17T08:34:18.8912415Z Submodule 'third_party/python-peachpy' (https://github.com/malfet/PeachPy.git) registered for path 'third_party/python-peachpy' 2025-07-17T08:34:18.8921018Z Submodule 'third_party/sleef' (https://github.com/shibatch/sleef) registered for path 'third_party/sleef' 2025-07-17T08:34:18.8929852Z Submodule 'third_party/tensorpipe' (https://github.com/pytorch/tensorpipe.git) registered for path 'third_party/tensorpipe' 2025-07-17T08:34:18.8973346Z Cloning into '/home/runner/_work/pytorch/pytorch/android/libs/fbjni'... 2025-07-17T08:34:19.3844139Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/psimd'... 2025-07-17T08:34:19.3844542Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/FXdiv'... 2025-07-17T08:34:19.3844877Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/FP16'... 2025-07-17T08:34:19.3845248Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/pthreadpool'... 2025-07-17T08:34:19.3906082Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/NNPACK'... 2025-07-17T08:34:19.4093352Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/NVTX'... 2025-07-17T08:34:19.5657553Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/pocketfft'... 2025-07-17T08:34:19.5658278Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/python-peachpy'... 2025-07-17T08:34:19.5658927Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/ideep'... 2025-07-17T08:34:19.5659520Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/gemmlowp/gemmlowp'... 2025-07-17T08:34:19.5660131Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/benchmark'... 2025-07-17T08:34:19.5692812Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/VulkanMemoryAllocator'... 2025-07-17T08:34:20.6430055Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/gloo'... 2025-07-17T08:34:20.6431221Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/ittapi'... 2025-07-17T08:34:20.6432005Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/tensorpipe'... 2025-07-17T08:34:20.6432759Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/cpp-httplib'... 2025-07-17T08:34:20.6433454Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kleidiai'... 2025-07-17T08:34:20.6434747Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/cpuinfo'... 2025-07-17T08:34:20.6435252Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/flash-attention'... 2025-07-17T08:34:20.6435692Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/sleef'... 2025-07-17T08:34:20.6436115Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/pybind11'... 2025-07-17T08:34:20.6436532Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/googletest'... 2025-07-17T08:34:20.6436987Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/mimalloc'... 2025-07-17T08:34:20.6437652Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/flatbuffers'... 2025-07-17T08:34:20.7181704Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/XNNPACK'... 2025-07-17T08:34:28.1408400Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/fmt'... 2025-07-17T08:34:28.1408818Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto'... 2025-07-17T08:34:28.1409215Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/cudnn_frontend'... 2025-07-17T08:34:28.1409572Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/cutlass'... 2025-07-17T08:34:28.1409885Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/fbgemm'... 2025-07-17T08:34:28.1410208Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/onnx'... 2025-07-17T08:34:28.1410561Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/composable_kernel'... 2025-07-17T08:34:28.1410899Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/aiter'... 2025-07-17T08:34:28.1411258Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp'... 2025-07-17T08:34:28.1411614Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/protobuf'... 2025-07-17T08:34:28.1411938Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/nlohmann'... 2025-07-17T08:34:28.1686914Z Submodule path 'android/libs/fbjni': checked out '7e1e1fe3858c63c251c637ae41a20de425dde96f' 2025-07-17T08:34:28.1887846Z Submodule path 'third_party/FP16': checked out '4dfe081cf6bcd15db339cf2680b9281b8451eeb3' 2025-07-17T08:34:28.2054465Z Submodule path 'third_party/FXdiv': checked out 'b408327ac2a15ec3e43352421954f5b1967701d1' 2025-07-17T08:34:28.2492391Z Submodule path 'third_party/NNPACK': checked out 'c07e3a0400713d546e0dea2d5466dd22ea389c73' 2025-07-17T08:34:28.3762037Z Submodule path 'third_party/NVTX': checked out '2942f167cc30c5e3a44a2aecd5b0d9c07ff61a07' 2025-07-17T08:34:28.4536172Z Submodule path 'third_party/VulkanMemoryAllocator': checked out '1d8f600fd424278486eade7ed3e877c99f0846b1' 2025-07-17T08:34:29.8414268Z Submodule path 'third_party/XNNPACK': checked out '51a0103656eff6fc9bfd39a4597923c4b542c883' 2025-07-17T08:34:30.0607945Z Submodule path 'third_party/aiter': checked out '01aae101b9e5e94d6c16a9514c9fb8df99c93150' 2025-07-17T08:34:30.0648437Z Submodule '3rdparty/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/aiter/3rdparty/composable_kernel' 2025-07-17T08:34:30.0690717Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/aiter/3rdparty/composable_kernel'... 2025-07-17T08:34:33.1189684Z Submodule path 'third_party/aiter/3rdparty/composable_kernel': checked out 'cffe8fa2a442ac8e80dd236a1a5d24fe3d7e0cbf' 2025-07-17T08:34:33.1609990Z Submodule path 'third_party/benchmark': checked out '299e5928955cc62af9968370293b916f5130916f' 2025-07-17T08:34:33.6724200Z Submodule path 'third_party/composable_kernel': checked out '434d19f696da62c12b5372b32cbc9ba968588d7e' 2025-07-17T08:34:33.7477496Z Submodule path 'third_party/cpp-httplib': checked out '3af7f2c16147f3fbc6e4d717032daf505dc1652c' 2025-07-17T08:34:33.8884013Z Submodule path 'third_party/cpuinfo': checked out '5e3d2445e6a84d9599bee2bf78edbb4d80865e1d' 2025-07-17T08:34:33.9576100Z Submodule path 'third_party/cudnn_frontend': checked out 'f937055efc6d414d11f4c6577e3977fe74f35fb6' 2025-07-17T08:34:35.1597202Z Submodule path 'third_party/cutlass': checked out 'b995f933179c22d3fe0d871c3a53d11e4681950f' 2025-07-17T08:34:35.3654541Z Submodule path 'third_party/fbgemm': checked out '157e88b750c452bef2ab4653fe9d1eeb151ce4c3' 2025-07-17T08:34:35.3695107Z Submodule 'external/asmjit' (https://github.com/asmjit/asmjit.git) registered for path 'third_party/fbgemm/external/asmjit' 2025-07-17T08:34:35.3700174Z Submodule 'external/composable_kernel' (https://github.com/jwfromm/composable_kernel.git) registered for path 'third_party/fbgemm/external/composable_kernel' 2025-07-17T08:34:35.3706124Z Submodule 'external/cpuinfo' (https://github.com/pytorch/cpuinfo) registered for path 'third_party/fbgemm/external/cpuinfo' 2025-07-17T08:34:35.3712679Z Submodule 'external/cutlass' (https://github.com/jwfromm/cutlass) registered for path 'third_party/fbgemm/external/cutlass' 2025-07-17T08:34:35.3719221Z Submodule 'external/googletest' (https://github.com/google/googletest) registered for path 'third_party/fbgemm/external/googletest' 2025-07-17T08:34:35.3725958Z Submodule 'external/hipify_torch' (https://github.com/ROCmSoftwarePlatform/hipify_torch.git) registered for path 'third_party/fbgemm/external/hipify_torch' 2025-07-17T08:34:35.3732583Z Submodule 'external/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/fbgemm/external/json' 2025-07-17T08:34:35.3782365Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/fbgemm/external/asmjit'... 2025-07-17T08:34:36.6142989Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/fbgemm/external/hipify_torch'... 2025-07-17T08:34:36.6143518Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/fbgemm/external/cpuinfo'... 2025-07-17T08:34:36.6143998Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/fbgemm/external/googletest'... 2025-07-17T08:34:36.7144643Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/fbgemm/external/composable_kernel'... 2025-07-17T08:34:36.8364882Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/fbgemm/external/cutlass'... 2025-07-17T08:34:37.6498875Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/fbgemm/external/json'... 2025-07-17T08:34:41.8167256Z Submodule path 'third_party/fbgemm/external/asmjit': checked out 'e5d7c0bd5d9aec44d68830187138149e6a8c4e32' 2025-07-17T08:34:42.2651646Z Submodule path 'third_party/fbgemm/external/composable_kernel': checked out '4a61bdd4bd4ed730e078aebc7c0fcf046ff29406' 2025-07-17T08:34:42.4081652Z Submodule path 'third_party/fbgemm/external/cpuinfo': checked out '6543fec09b2f04ac4a666882998b534afc9c1349' 2025-07-17T08:34:43.0270148Z From https://github.com/jwfromm/cutlass 2025-07-17T08:34:43.0270569Z * branch 3ed8d2ec4ba35ef5d9d8353826209b6f868f63d3 -> FETCH_HEAD 2025-07-17T08:34:43.9961122Z Submodule path 'third_party/fbgemm/external/cutlass': checked out '3ed8d2ec4ba35ef5d9d8353826209b6f868f63d3' 2025-07-17T08:34:44.0635457Z Submodule path 'third_party/fbgemm/external/googletest': checked out 'f8d7d77c06936315286eb55f8de22cd23c188571' 2025-07-17T08:34:44.0822581Z Submodule path 'third_party/fbgemm/external/hipify_torch': checked out 'a4337c69fe0e2552a7b7b0669178926beeed828c' 2025-07-17T08:34:44.2745971Z Submodule path 'third_party/fbgemm/external/json': checked out '9cca280a4d0ccf0c08f47a99aa71d1b0e52f8d03' 2025-07-17T08:34:44.4120288Z Submodule path 'third_party/flash-attention': checked out '979702c87a8713a8e0a5e9fee122b90d2ef13be5' 2025-07-17T08:34:44.4159266Z Submodule 'csrc/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/flash-attention/csrc/composable_kernel' 2025-07-17T08:34:44.4164509Z Submodule 'csrc/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/flash-attention/csrc/cutlass' 2025-07-17T08:34:44.4210041Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/flash-attention/csrc/composable_kernel'... 2025-07-17T08:34:46.9869624Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/flash-attention/csrc/cutlass'... 2025-07-17T08:34:47.4407105Z Submodule path 'third_party/flash-attention/csrc/composable_kernel': checked out '888317e698e9803c62bd38568abc9e05d7709f33' 2025-07-17T08:34:48.3787177Z Submodule path 'third_party/flash-attention/csrc/cutlass': checked out 'c506e16788cb08416a4a57e11a9067beeee29420' 2025-07-17T08:34:48.6214838Z Submodule path 'third_party/flatbuffers': checked out 'a2cd1ea3b6d3fee220106b5fed3f7ce8da9eb757' 2025-07-17T08:34:48.6693068Z Submodule path 'third_party/fmt': checked out '40626af88bd7df9a5fb80be7b25ac85b122d6c21' 2025-07-17T08:34:48.7251801Z Submodule path 'third_party/gemmlowp/gemmlowp': checked out '3fb5c176c17c765a3492cd2f0321b0dab712f350' 2025-07-17T08:34:48.7685422Z Submodule path 'third_party/gloo': checked out 'c7b7b022c124d9643957d9bd55f57ac59fce8fa2' 2025-07-17T08:34:48.8332964Z Submodule path 'third_party/googletest': checked out '52eb8108c5bdec04579160ae17225d66034bd723' 2025-07-17T08:34:48.8554278Z Submodule path 'third_party/ideep': checked out '719d8e6cd7f7a0e01b155657526d693acf97c2b3' 2025-07-17T08:34:48.8582366Z Submodule 'mkl-dnn' (https://github.com/intel/mkl-dnn.git) registered for path 'third_party/ideep/mkl-dnn' 2025-07-17T08:34:48.8616517Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/ideep/mkl-dnn'... 2025-07-17T08:34:58.4138398Z Submodule path 'third_party/ideep/mkl-dnn': checked out '8d263e693366ef8db40acc569cc7d8edf644556d' 2025-07-17T08:34:58.4495419Z Submodule path 'third_party/ittapi': checked out 'dec1d23ca65ab069d225dfe40dea14f455170959' 2025-07-17T08:34:58.5770450Z Submodule path 'third_party/kineto': checked out '5e7501833f1021ce6f618572d3baf657b6319658' 2025-07-17T08:34:58.5806859Z Submodule 'libkineto/third_party/dynolog' (https://github.com/facebookincubator/dynolog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog' 2025-07-17T08:34:58.5811637Z Submodule 'libkineto/third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/fmt' 2025-07-17T08:34:58.5818009Z Submodule 'libkineto/third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/googletest' 2025-07-17T08:34:58.5862599Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog'... 2025-07-17T08:34:59.3979798Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/fmt'... 2025-07-17T08:34:59.8865437Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/googletest'... 2025-07-17T08:35:00.0012304Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog': checked out '7d04a0053a845370ae06ce317a22a48e9edcc74e' 2025-07-17T08:35:00.0045654Z Submodule 'third_party/DCGM' (https://github.com/NVIDIA/DCGM.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-07-17T08:35:00.0050782Z Submodule 'third_party/cpr' (https://github.com/libcpr/cpr.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-07-17T08:35:00.0057125Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-07-17T08:35:00.0063866Z Submodule 'third_party/gflags' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-07-17T08:35:00.0070644Z Submodule 'third_party/glog' (https://github.com/google/glog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-07-17T08:35:00.0077424Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-07-17T08:35:00.0084280Z Submodule 'third_party/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-07-17T08:35:00.0091563Z Submodule 'third_party/pfs' (https://github.com/dtrugman/pfs.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-07-17T08:35:00.0161889Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'... 2025-07-17T08:35:01.2951374Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'... 2025-07-17T08:35:01.2952014Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'... 2025-07-17T08:35:01.2952574Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'... 2025-07-17T08:35:01.2953756Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/glog'... 2025-07-17T08:35:01.2954326Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'... 2025-07-17T08:35:01.3461334Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'... 2025-07-17T08:35:01.4464038Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/json'... 2025-07-17T08:35:05.8333927Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM': checked out 'ffde4e54bc7249a6039a5e6b45b395141e1217f9' 2025-07-17T08:35:05.8655754Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr': checked out '871ed52d350214a034f6ef8a3b8f51c5ce1bd400' 2025-07-17T08:35:05.9212910Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt': checked out 'cd4af11efc9c622896a3e4cb599fa28668ca3d05' 2025-07-17T08:35:05.9442743Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags': checked out 'e171aa2d15ed9eb17054558e0b3a6a413bb01067' 2025-07-17T08:35:05.9469892Z Submodule 'doc' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-07-17T08:35:05.9508629Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'... 2025-07-17T08:35:06.5317065Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc': checked out '8411df715cf522606e3b1aca386ddfc0b63d34b4' 2025-07-17T08:35:06.5606646Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog': checked out 'b33e3bad4c46c8a6345525fd822af355e5ef9446' 2025-07-17T08:35:06.6239245Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest': checked out '58d77fa8070e8cec2dc1ed015d66b454c8d78850' 2025-07-17T08:35:06.8004600Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json': checked out '4f8fba14066156b73f1189a2b8bd568bde5284c5' 2025-07-17T08:35:06.8286948Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs': checked out 'f68a2fa8ea36c783bdd760371411fcb495aa3150' 2025-07-17T08:35:06.8787652Z Submodule path 'third_party/kineto/libkineto/third_party/fmt': checked out '0041a40c1350ba702d475b9c4ad62da77caea164' 2025-07-17T08:35:06.9648724Z Submodule path 'third_party/kineto/libkineto/third_party/googletest': checked out '7aca84427f224eeed3144123d5230d5871e93347' 2025-07-17T08:35:07.0383502Z Submodule path 'third_party/kleidiai': checked out 'cca02c2f69dd18e1f12647c1c0bdc8cf90e680c7' 2025-07-17T08:35:07.1012072Z Submodule path 'third_party/mimalloc': checked out 'fbd8b99c2b828428947d70fdc046bb55609be93e' 2025-07-17T08:35:07.3048922Z Submodule path 'third_party/nlohmann': checked out '55f93686c01528224f448c19128836e7df245f72' 2025-07-17T08:35:08.2682759Z Submodule path 'third_party/onnx': checked out 'e709452ef2bbc1d113faf678c24e6d3467696e83' 2025-07-17T08:35:08.2759186Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/onnx/third_party/pybind11' 2025-07-17T08:35:08.2801810Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/onnx/third_party/pybind11'... 2025-07-17T08:35:09.3557241Z Submodule path 'third_party/onnx/third_party/pybind11': checked out 'a2e59f0e7065404b44dfe92a28aca47ba1378dc4' 2025-07-17T08:35:09.4948001Z Submodule path 'third_party/opentelemetry-cpp': checked out 'a799f4aed9c94b765dcdaabaeab7d5e7e2310878' 2025-07-17T08:35:09.4980322Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark) registered for path 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-07-17T08:35:09.4985652Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/opentelemetry-cpp/third_party/googletest' 2025-07-17T08:35:09.4992089Z Submodule 'third_party/ms-gsl' (https://github.com/microsoft/GSL) registered for path 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-07-17T08:35:09.4998990Z Submodule 'third_party/nlohmann-json' (https://github.com/nlohmann/json) registered for path 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-07-17T08:35:09.5006127Z Submodule 'third_party/opentelemetry-proto' (https://github.com/open-telemetry/opentelemetry-proto) registered for path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-07-17T08:35:09.5012842Z Submodule 'third_party/opentracing-cpp' (https://github.com/opentracing/opentracing-cpp.git) registered for path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-07-17T08:35:09.5020021Z Submodule 'third_party/prometheus-cpp' (https://github.com/jupp0r/prometheus-cpp) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-07-17T08:35:09.5027326Z Submodule 'tools/vcpkg' (https://github.com/Microsoft/vcpkg) registered for path 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-07-17T08:35:09.5073230Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/benchmark'... 2025-07-17T08:35:10.1723807Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/opentracing-cpp'... 2025-07-17T08:35:10.1724889Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/opentelemetry-proto'... 2025-07-17T08:35:10.1725791Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/ms-gsl'... 2025-07-17T08:35:10.1726677Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp'... 2025-07-17T08:35:10.2725537Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/googletest'... 2025-07-17T08:35:10.6378848Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/nlohmann-json'... 2025-07-17T08:35:15.5183444Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/tools/vcpkg'... 2025-07-17T08:35:15.7482506Z Submodule path 'third_party/opentelemetry-cpp/third_party/benchmark': checked out 'd572f4777349d43653b21d6c2fc63020ab326db2' 2025-07-17T08:35:15.8103923Z Submodule path 'third_party/opentelemetry-cpp/third_party/googletest': checked out 'b796f7d44681514f58a683a3a71ff17c94edb0c1' 2025-07-17T08:35:15.8352946Z Submodule path 'third_party/opentelemetry-cpp/third_party/ms-gsl': checked out '6f4529395c5b7c2d661812257cd6780c67e54afa' 2025-07-17T08:35:16.0362653Z Submodule path 'third_party/opentelemetry-cpp/third_party/nlohmann-json': checked out 'bc889afb4c5bf1c0d8ee29ef35eaaf4c8bef8a5d' 2025-07-17T08:35:16.0660025Z Submodule path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto': checked out '4ca4f0335c63cda7ab31ea7ed70d6553aee14dce' 2025-07-17T08:35:16.1267664Z Submodule path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp': checked out '06b57f48ded1fa3bdd3d4346f6ef29e40e08eaf5' 2025-07-17T08:35:16.1810507Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp': checked out 'c9ffcdda9086ffd9e1283ea7a0276d831f3c8a8d' 2025-07-17T08:35:16.1852660Z Submodule 'civetweb' (https://github.com/civetweb/civetweb.git) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-07-17T08:35:16.1873639Z Submodule 'googletest' (https://github.com/google/googletest.git) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-07-17T08:35:16.1927797Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'... 2025-07-17T08:35:19.9231167Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'... 2025-07-17T08:35:20.2654470Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb': checked out 'eefb26f82b233268fc98577d265352720d477ba4' 2025-07-17T08:35:20.3353846Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest': checked out 'e2239ee6043f73722e7aa812a459f54a28552929' 2025-07-17T08:35:21.4857186Z Submodule path 'third_party/opentelemetry-cpp/tools/vcpkg': checked out '8eb57355a4ffb410a2e94c07b4dca2dffbee8e50' 2025-07-17T08:35:21.5054710Z Submodule path 'third_party/pocketfft': checked out '0fa0ef591e38c2758e3184c6c23e497b9f732ffa' 2025-07-17T08:35:21.9228307Z Submodule path 'third_party/protobuf': checked out 'd1eca4e4b421cd2997495c4b4e65cea6be4e9b8a' 2025-07-17T08:35:21.9273625Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/protobuf/third_party/benchmark' 2025-07-17T08:35:21.9278969Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/protobuf/third_party/googletest' 2025-07-17T08:35:21.9324609Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/protobuf/third_party/benchmark'... 2025-07-17T08:35:22.6382193Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/protobuf/third_party/googletest'... 2025-07-17T08:35:23.9763109Z Submodule path 'third_party/protobuf/third_party/benchmark': checked out '5b7683f49e1e9223cf9927b24f6fd3d6bd82e3f8' 2025-07-17T08:35:24.0674489Z Submodule path 'third_party/protobuf/third_party/googletest': checked out '5ec7f0c4a113e2f18ac2c6cc7df51ad6afc24081' 2025-07-17T08:35:24.0846976Z Submodule path 'third_party/psimd': checked out '072586a71b55b7f8c584153d223e95687148a900' 2025-07-17T08:35:24.1046989Z Submodule path 'third_party/pthreadpool': checked out '4fe0e1e183925bf8cfa6aae24237e724a96479b8' 2025-07-17T08:35:24.1625658Z Submodule path 'third_party/pybind11': checked out 'a2e59f0e7065404b44dfe92a28aca47ba1378dc4' 2025-07-17T08:35:24.2060467Z Submodule path 'third_party/python-peachpy': checked out 'f45429b087dd7d5bc78bb40dc7cf06425c252d67' 2025-07-17T08:35:24.2697331Z Submodule path 'third_party/sleef': checked out '5a1d179df9cf652951b59010a2d2075372d67f68' 2025-07-17T08:35:24.3179400Z Submodule path 'third_party/tensorpipe': checked out '52791a2fd214b2a9dc5759d36725909c1daa7f2e' 2025-07-17T08:35:24.3215030Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/tensorpipe/third_party/googletest' 2025-07-17T08:35:24.3219889Z Submodule 'third_party/libnop' (https://github.com/google/libnop.git) registered for path 'third_party/tensorpipe/third_party/libnop' 2025-07-17T08:35:24.3226425Z Submodule 'third_party/libuv' (https://github.com/libuv/libuv.git) registered for path 'third_party/tensorpipe/third_party/libuv' 2025-07-17T08:35:24.3233338Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/tensorpipe/third_party/pybind11' 2025-07-17T08:35:24.3269175Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/googletest'... 2025-07-17T08:35:25.3632684Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/libnop'... 2025-07-17T08:35:25.3634420Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/pybind11'... 2025-07-17T08:35:25.4633994Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/libuv'... 2025-07-17T08:35:25.6960734Z Submodule path 'third_party/tensorpipe/third_party/googletest': checked out 'aee0f9d9b5b87796ee8a0ab26b7587ec30e8858e' 2025-07-17T08:35:25.7225522Z Submodule path 'third_party/tensorpipe/third_party/libnop': checked out '910b55815be16109f04f4180e9adee14fb4ce281' 2025-07-17T08:35:25.8236577Z Submodule path 'third_party/tensorpipe/third_party/libuv': checked out '1dff88e5161cba5c59276d2070d2e304e4dcb242' 2025-07-17T08:35:25.8713806Z Submodule path 'third_party/tensorpipe/third_party/pybind11': checked out 'a23996fce38ff6ccfbcdc09f1e63f2c4be5ea2ef' 2025-07-17T08:35:25.8746007Z Submodule 'tools/clang' (https://github.com/wjakob/clang-cindex-python3) registered for path 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-07-17T08:35:25.8786614Z Cloning into '/home/runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/pybind11/tools/clang'... 2025-07-17T08:35:26.2891005Z Submodule path 'third_party/tensorpipe/third_party/pybind11/tools/clang': checked out '6a00cbc4a9b8e68b71caf7f774b3f9c753ae84d5' 2025-07-17T08:35:26.2948490Z [command]/usr/bin/git submodule foreach --recursive git config --local gc.auto 0 2025-07-17T08:35:26.3280083Z Entering 'android/libs/fbjni' 2025-07-17T08:35:26.3327263Z Entering 'third_party/FP16' 2025-07-17T08:35:26.3376539Z Entering 'third_party/FXdiv' 2025-07-17T08:35:26.3421765Z Entering 'third_party/NNPACK' 2025-07-17T08:35:26.3465745Z Entering 'third_party/NVTX' 2025-07-17T08:35:26.3512922Z Entering 'third_party/VulkanMemoryAllocator' 2025-07-17T08:35:26.3556954Z Entering 'third_party/XNNPACK' 2025-07-17T08:35:26.3614586Z Entering 'third_party/aiter' 2025-07-17T08:35:26.3663455Z Entering 'third_party/aiter/3rdparty/composable_kernel' 2025-07-17T08:35:26.3721250Z Entering 'third_party/benchmark' 2025-07-17T08:35:26.3768358Z Entering 'third_party/composable_kernel' 2025-07-17T08:35:26.3826273Z 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Entering 'third_party/protobuf/third_party/googletest' 2025-07-17T08:35:27.2018072Z Entering 'third_party/psimd' 2025-07-17T08:35:27.2070830Z Entering 'third_party/pthreadpool' 2025-07-17T08:35:27.2122039Z Entering 'third_party/pybind11' 2025-07-17T08:35:27.2174676Z Entering 'third_party/python-peachpy' 2025-07-17T08:35:27.2225018Z Entering 'third_party/sleef' 2025-07-17T08:35:27.2278973Z Entering 'third_party/tensorpipe' 2025-07-17T08:35:27.2332242Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-07-17T08:35:27.2381685Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-07-17T08:35:27.2433202Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-07-17T08:35:27.2484448Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-07-17T08:35:27.2531030Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-07-17T08:35:27.2609160Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local 'http.https://github.com/.extraheader' 'AUTHORIZATION: basic ***' && git config --local --show-origin --name-only --get-regexp remote.origin.url" 2025-07-17T08:35:27.2915233Z Entering 'android/libs/fbjni' 2025-07-17T08:35:27.2962605Z file:/home/runner/_work/pytorch/pytorch/.git/modules/android/libs/fbjni/config remote.origin.url 2025-07-17T08:35:27.2988184Z Entering 'third_party/FP16' 2025-07-17T08:35:27.3039657Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FP16/config remote.origin.url 2025-07-17T08:35:27.3063741Z Entering 'third_party/FXdiv' 2025-07-17T08:35:27.3113055Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FXdiv/config remote.origin.url 2025-07-17T08:35:27.3137036Z Entering 'third_party/NNPACK' 2025-07-17T08:35:27.3183716Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK/config remote.origin.url 2025-07-17T08:35:27.3208333Z Entering 'third_party/NVTX' 2025-07-17T08:35:27.3256288Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/NVTX/config remote.origin.url 2025-07-17T08:35:27.3281620Z Entering 'third_party/VulkanMemoryAllocator' 2025-07-17T08:35:27.3326761Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/VulkanMemoryAllocator/config remote.origin.url 2025-07-17T08:35:27.3351069Z Entering 'third_party/XNNPACK' 2025-07-17T08:35:27.3395868Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/XNNPACK/config remote.origin.url 2025-07-17T08:35:27.3432907Z Entering 'third_party/aiter' 2025-07-17T08:35:27.3478388Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/aiter/config remote.origin.url 2025-07-17T08:35:27.3503405Z Entering 'third_party/aiter/3rdparty/composable_kernel' 2025-07-17T08:35:27.3552927Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/aiter/modules/3rdparty/composable_kernel/config remote.origin.url 2025-07-17T08:35:27.3585242Z Entering 'third_party/benchmark' 2025-07-17T08:35:27.3631370Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/benchmark/config remote.origin.url 2025-07-17T08:35:27.3655189Z Entering 'third_party/composable_kernel' 2025-07-17T08:35:27.3702579Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/composable_kernel/config remote.origin.url 2025-07-17T08:35:27.3732797Z Entering 'third_party/cpp-httplib' 2025-07-17T08:35:27.3778679Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/cpp-httplib/config remote.origin.url 2025-07-17T08:35:27.3803327Z Entering 'third_party/cpuinfo' 2025-07-17T08:35:27.3849650Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/cpuinfo/config remote.origin.url 2025-07-17T08:35:27.3874737Z Entering 'third_party/cudnn_frontend' 2025-07-17T08:35:27.3927509Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/cudnn_frontend/config remote.origin.url 2025-07-17T08:35:27.3950461Z Entering 'third_party/cutlass' 2025-07-17T08:35:27.3997137Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/cutlass/config remote.origin.url 2025-07-17T08:35:27.4029256Z Entering 'third_party/fbgemm' 2025-07-17T08:35:27.4075922Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/config remote.origin.url 2025-07-17T08:35:27.4101609Z Entering 'third_party/fbgemm/external/asmjit' 2025-07-17T08:35:27.4148509Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/external/asmjit/config remote.origin.url 2025-07-17T08:35:27.4172060Z Entering 'third_party/fbgemm/external/composable_kernel' 2025-07-17T08:35:27.4219295Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/external/composable_kernel/config remote.origin.url 2025-07-17T08:35:27.4247366Z Entering 'third_party/fbgemm/external/cpuinfo' 2025-07-17T08:35:27.4292726Z file:/home/runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/external/cpuinfo/config 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'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-07-17T08:35:28.1318592Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-07-17T08:35:28.1368036Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-07-17T08:35:28.1418662Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-07-17T08:35:28.1470424Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-07-17T08:35:28.1523612Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-07-17T08:35:28.1572453Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-07-17T08:35:28.1625088Z Entering 'third_party/kleidiai' 2025-07-17T08:35:28.1674388Z Entering 'third_party/mimalloc' 2025-07-17T08:35:28.1721981Z Entering 'third_party/nlohmann' 2025-07-17T08:35:28.1770590Z Entering 'third_party/onnx' 2025-07-17T08:35:28.1838378Z Entering 'third_party/onnx/third_party/pybind11' 2025-07-17T08:35:28.1891104Z Entering 'third_party/opentelemetry-cpp' 2025-07-17T08:35:28.1940547Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-07-17T08:35:28.1988212Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-07-17T08:35:28.2034757Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-07-17T08:35:28.2085625Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-07-17T08:35:28.2133693Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-07-17T08:35:28.2180612Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-07-17T08:35:28.2225847Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-07-17T08:35:28.2273491Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-07-17T08:35:28.2326435Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-07-17T08:35:28.2380443Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-07-17T08:35:28.2445801Z Entering 'third_party/pocketfft' 2025-07-17T08:35:28.2497418Z Entering 'third_party/protobuf' 2025-07-17T08:35:28.2546589Z Entering 'third_party/protobuf/third_party/benchmark' 2025-07-17T08:35:28.2595610Z Entering 'third_party/protobuf/third_party/googletest' 2025-07-17T08:35:28.2645926Z Entering 'third_party/psimd' 2025-07-17T08:35:28.2695009Z Entering 'third_party/pthreadpool' 2025-07-17T08:35:28.2739980Z Entering 'third_party/pybind11' 2025-07-17T08:35:28.2788764Z Entering 'third_party/python-peachpy' 2025-07-17T08:35:28.2835867Z Entering 'third_party/sleef' 2025-07-17T08:35:28.2881692Z Entering 'third_party/tensorpipe' 2025-07-17T08:35:28.2925733Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-07-17T08:35:28.2973616Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-07-17T08:35:28.3018589Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-07-17T08:35:28.3066075Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-07-17T08:35:28.3113514Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-07-17T08:35:28.3187464Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'org-21003710@github.com:' 2025-07-17T08:35:28.3498823Z Entering 'android/libs/fbjni' 2025-07-17T08:35:28.3549511Z Entering 'third_party/FP16' 2025-07-17T08:35:28.3597997Z Entering 'third_party/FXdiv' 2025-07-17T08:35:28.3645436Z Entering 'third_party/NNPACK' 2025-07-17T08:35:28.3693095Z Entering 'third_party/NVTX' 2025-07-17T08:35:28.3742429Z Entering 'third_party/VulkanMemoryAllocator' 2025-07-17T08:35:28.3789431Z Entering 'third_party/XNNPACK' 2025-07-17T08:35:28.3847400Z Entering 'third_party/aiter' 2025-07-17T08:35:28.3893791Z Entering 'third_party/aiter/3rdparty/composable_kernel' 2025-07-17T08:35:28.3952876Z Entering 'third_party/benchmark' 2025-07-17T08:35:28.3999879Z Entering 'third_party/composable_kernel' 2025-07-17T08:35:28.4052084Z Entering 'third_party/cpp-httplib' 2025-07-17T08:35:28.4102018Z Entering 'third_party/cpuinfo' 2025-07-17T08:35:28.4151264Z Entering 'third_party/cudnn_frontend' 2025-07-17T08:35:28.4203034Z Entering 'third_party/cutlass' 2025-07-17T08:35:28.4259747Z Entering 'third_party/fbgemm' 2025-07-17T08:35:28.4311976Z Entering 'third_party/fbgemm/external/asmjit' 2025-07-17T08:35:28.4359301Z Entering 'third_party/fbgemm/external/composable_kernel' 2025-07-17T08:35:28.4410659Z Entering 'third_party/fbgemm/external/cpuinfo' 2025-07-17T08:35:28.4464408Z Entering 'third_party/fbgemm/external/cutlass' 2025-07-17T08:35:28.4520127Z Entering 'third_party/fbgemm/external/googletest' 2025-07-17T08:35:28.4566519Z Entering 'third_party/fbgemm/external/hipify_torch' 2025-07-17T08:35:28.4611893Z Entering 'third_party/fbgemm/external/json' 2025-07-17T08:35:28.4662376Z Entering 'third_party/flash-attention' 2025-07-17T08:35:28.4710945Z Entering 'third_party/flash-attention/csrc/composable_kernel' 2025-07-17T08:35:28.4763245Z Entering 'third_party/flash-attention/csrc/cutlass' 2025-07-17T08:35:28.4820838Z Entering 'third_party/flatbuffers' 2025-07-17T08:35:28.4874768Z Entering 'third_party/fmt' 2025-07-17T08:35:28.4925068Z Entering 'third_party/gemmlowp/gemmlowp' 2025-07-17T08:35:28.4976267Z Entering 'third_party/gloo' 2025-07-17T08:35:28.5024734Z Entering 'third_party/googletest' 2025-07-17T08:35:28.5074683Z Entering 'third_party/ideep' 2025-07-17T08:35:28.5135578Z Entering 'third_party/ideep/mkl-dnn' 2025-07-17T08:35:28.5179563Z Entering 'third_party/ittapi' 2025-07-17T08:35:28.5229531Z Entering 'third_party/kineto' 2025-07-17T08:35:28.5275867Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2025-07-17T08:35:28.5326051Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-07-17T08:35:28.5374841Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-07-17T08:35:28.5424211Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-07-17T08:35:28.5470087Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-07-17T08:35:28.5520823Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-07-17T08:35:28.5570482Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-07-17T08:35:28.5617623Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-07-17T08:35:28.5668352Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-07-17T08:35:28.5721781Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-07-17T08:35:28.5776994Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-07-17T08:35:28.5826593Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-07-17T08:35:28.5878342Z Entering 'third_party/kleidiai' 2025-07-17T08:35:28.5931327Z Entering 'third_party/mimalloc' 2025-07-17T08:35:28.5981064Z Entering 'third_party/nlohmann' 2025-07-17T08:35:28.6029754Z Entering 'third_party/onnx' 2025-07-17T08:35:28.6095197Z Entering 'third_party/onnx/third_party/pybind11' 2025-07-17T08:35:28.6149139Z Entering 'third_party/opentelemetry-cpp' 2025-07-17T08:35:28.6200258Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-07-17T08:35:28.6245757Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-07-17T08:35:28.6293562Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-07-17T08:35:28.6341414Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-07-17T08:35:28.6388400Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-07-17T08:35:28.6435820Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-07-17T08:35:28.6483704Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-07-17T08:35:28.6529888Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-07-17T08:35:28.6583209Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-07-17T08:35:28.6634171Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-07-17T08:35:28.6700673Z Entering 'third_party/pocketfft' 2025-07-17T08:35:28.6750288Z Entering 'third_party/protobuf' 2025-07-17T08:35:28.6800184Z Entering 'third_party/protobuf/third_party/benchmark' 2025-07-17T08:35:28.6848446Z Entering 'third_party/protobuf/third_party/googletest' 2025-07-17T08:35:28.6901453Z Entering 'third_party/psimd' 2025-07-17T08:35:28.6950054Z Entering 'third_party/pthreadpool' 2025-07-17T08:35:28.6999195Z Entering 'third_party/pybind11' 2025-07-17T08:35:28.7045885Z Entering 'third_party/python-peachpy' 2025-07-17T08:35:28.7093000Z Entering 'third_party/sleef' 2025-07-17T08:35:28.7142044Z Entering 'third_party/tensorpipe' 2025-07-17T08:35:28.7190582Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-07-17T08:35:28.7237454Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-07-17T08:35:28.7282391Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-07-17T08:35:28.7332132Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-07-17T08:35:28.7379214Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-07-17T08:35:28.7450430Z ##[endgroup] 2025-07-17T08:35:28.7506324Z [command]/usr/bin/git log -1 --format=%H 2025-07-17T08:35:28.7540090Z a38f433be2e94a64b095a44ba39879d02d0c2316 2025-07-17T08:35:28.7739285Z Prepare all required actions 2025-07-17T08:35:28.7739661Z Getting action download info 2025-07-17T08:35:28.9697219Z ##[group]Run ./.github/actions/setup-rocm 2025-07-17T08:35:28.9697474Z env: 2025-07-17T08:35:28.9697624Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:35:28.9697806Z ##[endgroup] 2025-07-17T08:35:28.9720909Z ##[group]Run dpkg -l | grep -E " rocm" 2025-07-17T08:35:28.9721153Z dpkg -l | grep -E " rocm" 2025-07-17T08:35:28.9734200Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T08:35:28.9734442Z env: 2025-07-17T08:35:28.9734600Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:35:28.9734762Z ##[endgroup] 2025-07-17T08:35:28.9837330Z ii rocm-cmake 0.14.0.60401-83~22.04 amd64 rocm-cmake built using CMake 2025-07-17T08:35:28.9837740Z ii rocm-core 6.4.1.60401-83~22.04 amd64 ROCm Runtime software stack 2025-07-17T08:35:28.9838243Z ii rocm-dbgapi 0.77.2.60401-83~22.04 amd64 Library to provide AMD GPU debugger API 2025-07-17T08:35:28.9838654Z ii rocm-debug-agent 2.0.4.60401-83~22.04 amd64 Radeon Open Compute Debug Agent (ROCdebug-agent) 2025-07-17T08:35:28.9839082Z ii rocm-dev 6.4.1.60401-83~22.04 amd64 Radeon Open Compute (ROCm) Runtime software stack 2025-07-17T08:35:28.9839880Z ii rocm-device-libs 1.0.0.60401-83~22.04 amd64 Radeon Open Compute - device libraries 2025-07-17T08:35:28.9840235Z ii rocm-gdb 15.2.60401-83~22.04 amd64 ROCgdb 2025-07-17T08:35:28.9840571Z ii rocm-llvm 19.0.0.25184.60401-83~22.04 amd64 ROCm core compiler 2025-07-17T08:35:28.9840951Z ii rocm-opencl 2.0.0.60401-83~22.04 amd64 clr built using CMake 2025-07-17T08:35:28.9841673Z ii rocm-opencl-dev 2.0.0.60401-83~22.04 amd64 clr built using CMake 2025-07-17T08:35:28.9842032Z ii rocm-smi-lib 7.5.0.60401-83~22.04 amd64 AMD System Management libraries 2025-07-17T08:35:28.9842423Z ii rocm-utils 6.4.1.60401-83~22.04 amd64 Radeon Open Compute (ROCm) Runtime software stack 2025-07-17T08:35:28.9842814Z ii rocminfo 1.0.0.60401-83~22.04 amd64 Radeon Open Compute (ROCm) Runtime rocminfo tool 2025-07-17T08:35:28.9861000Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2025-07-17T08:35:28.9861360Z # ignore expansion of "docker ps -q" since it could be empty 2025-07-17T08:35:28.9861616Z # shellcheck disable=SC2046 2025-07-17T08:35:28.9861847Z docker stop $(docker ps -q) || true 2025-07-17T08:35:28.9862062Z # Prune all stopped containers. 2025-07-17T08:35:28.9862283Z docker container prune -f 2025-07-17T08:35:28.9872230Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T08:35:28.9872473Z env: 2025-07-17T08:35:28.9872622Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:35:28.9872800Z ##[endgroup] 2025-07-17T08:35:29.0235230Z docker: 'docker stop' requires at least 1 argument 2025-07-17T08:35:29.0235447Z 2025-07-17T08:35:29.0235644Z Usage: docker stop [OPTIONS] CONTAINER [CONTAINER...] 2025-07-17T08:35:29.0236093Z 2025-07-17T08:35:29.0236206Z See 'docker stop --help' for more information 2025-07-17T08:35:29.0412275Z Total reclaimed space: 0B 2025-07-17T08:35:29.0455787Z ##[group]Run cat /etc/os-release || true 2025-07-17T08:35:29.0456070Z cat /etc/os-release || true 2025-07-17T08:35:29.0456320Z cat /etc/apt/sources.list.d/rocm.list || true 2025-07-17T08:35:29.0456559Z cat /opt/rocm/.info/version || true 2025-07-17T08:35:29.0456764Z whoami 2025-07-17T08:35:29.0469236Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T08:35:29.0469495Z env: 2025-07-17T08:35:29.0469654Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:35:29.0469844Z ##[endgroup] 2025-07-17T08:35:29.0513060Z PRETTY_NAME="Ubuntu 22.04.5 LTS" 2025-07-17T08:35:29.0513270Z NAME="Ubuntu" 2025-07-17T08:35:29.0513454Z VERSION_ID="22.04" 2025-07-17T08:35:29.0513619Z VERSION="22.04.5 LTS (Jammy Jellyfish)" 2025-07-17T08:35:29.0513861Z VERSION_CODENAME=jammy 2025-07-17T08:35:29.0514025Z ID=ubuntu 2025-07-17T08:35:29.0514166Z ID_LIKE=debian 2025-07-17T08:35:29.0514352Z HOME_URL="https://www.ubuntu.com/" 2025-07-17T08:35:29.0514561Z SUPPORT_URL="https://help.ubuntu.com/" 2025-07-17T08:35:29.0514805Z BUG_REPORT_URL="https://bugs.launchpad.net/ubuntu/" 2025-07-17T08:35:29.0515124Z PRIVACY_POLICY_URL="https://www.ubuntu.com/legal/terms-and-policies/privacy-policy" 2025-07-17T08:35:29.0515427Z UBUNTU_CODENAME=jammy 2025-07-17T08:35:29.0523435Z deb [arch=amd64 signed-by=/etc/apt/keyrings/rocm.gpg] https://repo.radeon.com/rocm/apt/6.4.1 jammy main 2025-07-17T08:35:29.0534228Z 6.4.1-83 2025-07-17T08:35:29.0544901Z runner 2025-07-17T08:35:29.0566795Z ##[group]Run dpkg -l | grep -E " amdgpu" 2025-07-17T08:35:29.0567032Z dpkg -l | grep -E " amdgpu" 2025-07-17T08:35:29.0577276Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T08:35:29.0577527Z env: 2025-07-17T08:35:29.0577671Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:35:29.0578072Z ##[endgroup] 2025-07-17T08:35:29.0664526Z ii amdgpu-core 1:6.4.60401-2164967.22.04 all Core meta package for unified amdgpu driver. 2025-07-17T08:35:29.0664962Z ii amdgpu-install 6.4.60401-2164967.22.04 all AMDGPU driver repository and installer 2025-07-17T08:35:29.0693152Z ##[group]Run rocm-smi 2025-07-17T08:35:29.0693373Z rocm-smi 2025-07-17T08:35:29.0705579Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T08:35:29.0705830Z env: 2025-07-17T08:35:29.0705997Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:35:29.0706198Z ##[endgroup] 2025-07-17T08:35:29.3149954Z 2025-07-17T08:35:29.3149968Z 2025-07-17T08:35:29.3150312Z ============================================ ROCm System Management Interface ============================================ 2025-07-17T08:35:29.3150683Z ====================================================== Concise Info ====================================================== 2025-07-17T08:35:29.3151080Z Device Node IDs Temp Power Partitions SCLK MCLK Fan Perf PwrCap VRAM% GPU% 2025-07-17T08:35:29.3151809Z  (DID, GUID) (Junction) (Socket) (Mem, Compute, ID)  2025-07-17T08:35:29.3152144Z ========================================================================================================================== 2025-07-17T08:35:29.3152940Z 0 8 0x74a1, 16738 36.0°C 138.0W NPS1, SPX, 0 133Mhz 900Mhz 0% auto 750.0W 0% 0% 2025-07-17T08:35:29.3153408Z 1 9 0x74a1, 63738 36.0°C 136.0W NPS1, SPX, 0 134Mhz 900Mhz 0% auto 750.0W 0% 0% 2025-07-17T08:35:29.3155264Z ========================================================================================================================== 2025-07-17T08:35:29.3155526Z ================================================== End of ROCm SMI Log =================================================== 2025-07-17T08:35:29.3266605Z ##[group]Run rocminfo 2025-07-17T08:35:29.3266814Z rocminfo 2025-07-17T08:35:29.3279264Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T08:35:29.3279505Z env: 2025-07-17T08:35:29.3279666Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:35:29.3279829Z ##[endgroup] 2025-07-17T08:35:29.5781246Z ROCk module version 6.12.12 is loaded 2025-07-17T08:35:29.5781690Z ===================== 2025-07-17T08:35:29.5781894Z HSA System Attributes 2025-07-17T08:35:29.5782090Z ===================== 2025-07-17T08:35:29.5782937Z Runtime Version: 1.15 2025-07-17T08:35:29.5783134Z Runtime Ext Version: 1.7 2025-07-17T08:35:29.5783319Z System Timestamp Freq.: 1000.000000MHz 2025-07-17T08:35:29.5783621Z Sig. Max Wait Duration: 18446744073709551615 (0xFFFFFFFFFFFFFFFF) (timestamp count) 2025-07-17T08:35:29.5783941Z Machine Model: LARGE 2025-07-17T08:35:29.5784238Z System Endianness: LITTLE 2025-07-17T08:35:29.5784484Z Mwaitx: DISABLED 2025-07-17T08:35:29.5784674Z XNACK enabled: NO 2025-07-17T08:35:29.5784845Z DMAbuf Support: YES 2025-07-17T08:35:29.5785011Z VMM Support: YES 2025-07-17T08:35:29.5785118Z 2025-07-17T08:35:29.5785174Z ========== 2025-07-17T08:35:29.5785441Z HSA Agents 2025-07-17T08:35:29.5785602Z ========== 2025-07-17T08:35:29.5785750Z ******* 2025-07-17T08:35:29.5785890Z Agent 1 2025-07-17T08:35:29.5786049Z ******* 2025-07-17T08:35:29.5786238Z Name: AMD EPYC 9534 64-Core Processor 2025-07-17T08:35:29.5786566Z Uuid: CPU-XX 2025-07-17T08:35:29.5786798Z Marketing Name: AMD EPYC 9534 64-Core Processor 2025-07-17T08:35:29.5787047Z Vendor Name: CPU 2025-07-17T08:35:29.5787511Z Feature: None specified 2025-07-17T08:35:29.5787753Z Profile: FULL_PROFILE 2025-07-17T08:35:29.5787991Z Float Round Mode: NEAR 2025-07-17T08:35:29.5788221Z Max Queue Number: 0(0x0) 2025-07-17T08:35:29.5788456Z Queue Min Size: 0(0x0) 2025-07-17T08:35:29.5788675Z Queue Max Size: 0(0x0) 2025-07-17T08:35:29.5788911Z Queue Type: MULTI 2025-07-17T08:35:29.5789122Z Node: 0 2025-07-17T08:35:29.5789342Z Device Type: CPU 2025-07-17T08:35:29.5789553Z Cache Info: 2025-07-17T08:35:29.5789729Z L1: 32768(0x8000) KB 2025-07-17T08:35:29.5789957Z Chip ID: 0(0x0) 2025-07-17T08:35:29.5790180Z ASIC Revision: 0(0x0) 2025-07-17T08:35:29.5790440Z Cacheline Size: 64(0x40) 2025-07-17T08:35:29.5790683Z Max Clock Freq. (MHz): 2450 2025-07-17T08:35:29.5790910Z BDFID: 0 2025-07-17T08:35:29.5791136Z Internal Node ID: 0 2025-07-17T08:35:29.5791365Z Compute Unit: 128 2025-07-17T08:35:29.5791607Z SIMDs per CU: 0 2025-07-17T08:35:29.5791833Z Shader Engines: 0 2025-07-17T08:35:29.5792076Z Shader Arrs. per Eng.: 0 2025-07-17T08:35:29.5792322Z WatchPts on Addr. Ranges:1 2025-07-17T08:35:29.5792562Z Memory Properties: 2025-07-17T08:35:29.5792734Z Features: None 2025-07-17T08:35:29.5792915Z Pool Info: 2025-07-17T08:35:29.5793092Z Pool 1 2025-07-17T08:35:29.5793297Z Segment: GLOBAL; FLAGS: FINE GRAINED 2025-07-17T08:35:29.5793542Z Size: 1188703736(0x46da2df8) KB 2025-07-17T08:35:29.5793769Z Allocatable: TRUE 2025-07-17T08:35:29.5794010Z Alloc Granule: 4KB 2025-07-17T08:35:29.5794398Z Alloc Recommended Granule:4KB 2025-07-17T08:35:29.5794661Z Alloc Alignment: 4KB 2025-07-17T08:35:29.5794925Z Accessible by all: TRUE 2025-07-17T08:35:29.5795147Z Pool 2 2025-07-17T08:35:29.5795353Z Segment: GLOBAL; FLAGS: EXTENDED FINE GRAINED 2025-07-17T08:35:29.5795606Z Size: 1188703736(0x46da2df8) KB 2025-07-17T08:35:29.5795835Z Allocatable: TRUE 2025-07-17T08:35:29.5796074Z Alloc Granule: 4KB 2025-07-17T08:35:29.5796328Z Alloc Recommended Granule:4KB 2025-07-17T08:35:29.5796584Z Alloc Alignment: 4KB 2025-07-17T08:35:29.5796824Z Accessible by all: TRUE 2025-07-17T08:35:29.5797040Z Pool 3 2025-07-17T08:35:29.5797233Z Segment: GLOBAL; FLAGS: KERNARG, FINE GRAINED 2025-07-17T08:35:29.5797467Z Size: 1188703736(0x46da2df8) KB 2025-07-17T08:35:29.5797686Z Allocatable: TRUE 2025-07-17T08:35:29.5797938Z Alloc Granule: 4KB 2025-07-17T08:35:29.5798318Z Alloc Recommended Granule:4KB 2025-07-17T08:35:29.5798609Z Alloc Alignment: 4KB 2025-07-17T08:35:29.5798847Z Accessible by all: TRUE 2025-07-17T08:35:29.5799071Z Pool 4 2025-07-17T08:35:29.5799268Z Segment: GLOBAL; FLAGS: COARSE GRAINED 2025-07-17T08:35:29.5799494Z Size: 1188703736(0x46da2df8) KB 2025-07-17T08:35:29.5815761Z Allocatable: TRUE 2025-07-17T08:35:29.5816095Z Alloc Granule: 4KB 2025-07-17T08:35:29.5816372Z Alloc Recommended Granule:4KB 2025-07-17T08:35:29.5816646Z Alloc Alignment: 4KB 2025-07-17T08:35:29.5816893Z Accessible by all: TRUE 2025-07-17T08:35:29.5817123Z ISA Info: 2025-07-17T08:35:29.5817313Z ******* 2025-07-17T08:35:29.5817467Z Agent 2 2025-07-17T08:35:29.5817629Z ******* 2025-07-17T08:35:29.5817809Z Name: AMD EPYC 9534 64-Core Processor 2025-07-17T08:35:29.5818059Z Uuid: CPU-XX 2025-07-17T08:35:29.5818296Z Marketing Name: AMD EPYC 9534 64-Core Processor 2025-07-17T08:35:29.5818550Z Vendor Name: CPU 2025-07-17T08:35:29.5818785Z Feature: None specified 2025-07-17T08:35:29.5819021Z Profile: FULL_PROFILE 2025-07-17T08:35:29.5819262Z Float Round Mode: NEAR 2025-07-17T08:35:29.5819499Z Max Queue Number: 0(0x0) 2025-07-17T08:35:29.5819740Z Queue Min Size: 0(0x0) 2025-07-17T08:35:29.5819966Z Queue Max Size: 0(0x0) 2025-07-17T08:35:29.5820196Z Queue Type: MULTI 2025-07-17T08:35:29.5820409Z Node: 1 2025-07-17T08:35:29.5820640Z Device Type: CPU 2025-07-17T08:35:29.5820855Z Cache Info: 2025-07-17T08:35:29.5821031Z L1: 32768(0x8000) KB 2025-07-17T08:35:29.5821258Z Chip ID: 0(0x0) 2025-07-17T08:35:29.5821696Z ASIC Revision: 0(0x0) 2025-07-17T08:35:29.5821947Z Cacheline Size: 64(0x40) 2025-07-17T08:35:29.5822179Z Max Clock Freq. (MHz): 2450 2025-07-17T08:35:29.5822410Z BDFID: 0 2025-07-17T08:35:29.5822648Z Internal Node ID: 1 2025-07-17T08:35:29.5822881Z Compute Unit: 128 2025-07-17T08:35:29.5823117Z SIMDs per CU: 0 2025-07-17T08:35:29.5823340Z Shader Engines: 0 2025-07-17T08:35:29.5823585Z Shader Arrs. per Eng.: 0 2025-07-17T08:35:29.5823830Z WatchPts on Addr. Ranges:1 2025-07-17T08:35:29.5824056Z Memory Properties: 2025-07-17T08:35:29.5824225Z Features: None 2025-07-17T08:35:29.5824416Z Pool Info: 2025-07-17T08:35:29.5824589Z Pool 1 2025-07-17T08:35:29.5824787Z Segment: GLOBAL; FLAGS: FINE GRAINED 2025-07-17T08:35:29.5825029Z Size: 1188946676(0x46dde2f4) KB 2025-07-17T08:35:29.5825257Z Allocatable: TRUE 2025-07-17T08:35:29.5825748Z Alloc Granule: 4KB 2025-07-17T08:35:29.5826006Z Alloc Recommended Granule:4KB 2025-07-17T08:35:29.5826275Z Alloc Alignment: 4KB 2025-07-17T08:35:29.5826528Z Accessible by all: TRUE 2025-07-17T08:35:29.5826741Z Pool 2 2025-07-17T08:35:29.5826945Z Segment: GLOBAL; FLAGS: EXTENDED FINE GRAINED 2025-07-17T08:35:29.5827173Z Size: 1188946676(0x46dde2f4) KB 2025-07-17T08:35:29.5827409Z Allocatable: TRUE 2025-07-17T08:35:29.5827641Z Alloc Granule: 4KB 2025-07-17T08:35:29.5827886Z Alloc Recommended Granule:4KB 2025-07-17T08:35:29.5828135Z Alloc Alignment: 4KB 2025-07-17T08:35:29.5828386Z Accessible by all: TRUE 2025-07-17T08:35:29.5828603Z Pool 3 2025-07-17T08:35:29.5828791Z Segment: GLOBAL; FLAGS: KERNARG, FINE GRAINED 2025-07-17T08:35:29.5829029Z Size: 1188946676(0x46dde2f4) KB 2025-07-17T08:35:29.5829251Z Allocatable: TRUE 2025-07-17T08:35:29.5829489Z Alloc Granule: 4KB 2025-07-17T08:35:29.5829739Z Alloc Recommended Granule:4KB 2025-07-17T08:35:29.5829989Z Alloc Alignment: 4KB 2025-07-17T08:35:29.5830239Z Accessible by all: TRUE 2025-07-17T08:35:29.5830445Z Pool 4 2025-07-17T08:35:29.5830644Z Segment: GLOBAL; FLAGS: COARSE GRAINED 2025-07-17T08:35:29.5830866Z Size: 1188946676(0x46dde2f4) KB 2025-07-17T08:35:29.5831095Z Allocatable: TRUE 2025-07-17T08:35:29.5831345Z Alloc Granule: 4KB 2025-07-17T08:35:29.5831607Z Alloc Recommended Granule:4KB 2025-07-17T08:35:29.5831858Z Alloc Alignment: 4KB 2025-07-17T08:35:29.5832097Z Accessible by all: TRUE 2025-07-17T08:35:29.5832318Z ISA Info: 2025-07-17T08:35:29.5832611Z ******* 2025-07-17T08:35:29.5832773Z Agent 3 2025-07-17T08:35:29.5832919Z ******* 2025-07-17T08:35:29.5833100Z Name: gfx942 2025-07-17T08:35:29.5833312Z Uuid: GPU-d7d27283ffe4d923 2025-07-17T08:35:29.5833553Z Marketing Name: AMD Instinct MI300X 2025-07-17T08:35:29.5833806Z Vendor Name: AMD 2025-07-17T08:35:29.5834033Z Feature: KERNEL_DISPATCH 2025-07-17T08:35:29.5834277Z Profile: BASE_PROFILE 2025-07-17T08:35:29.5834510Z Float Round Mode: NEAR 2025-07-17T08:35:29.5834755Z Max Queue Number: 128(0x80) 2025-07-17T08:35:29.5834983Z Queue Min Size: 64(0x40) 2025-07-17T08:35:29.5835221Z Queue Max Size: 131072(0x20000) 2025-07-17T08:35:29.5835458Z Queue Type: MULTI 2025-07-17T08:35:29.5835669Z Node: 2 2025-07-17T08:35:29.5835893Z Device Type: GPU 2025-07-17T08:35:29.5836093Z Cache Info: 2025-07-17T08:35:29.5836419Z L1: 32(0x20) KB 2025-07-17T08:35:29.5836621Z L2: 4096(0x1000) KB 2025-07-17T08:35:29.5836825Z L3: 262144(0x40000) KB 2025-07-17T08:35:29.5837044Z Chip ID: 29857(0x74a1) 2025-07-17T08:35:29.5837285Z ASIC Revision: 1(0x1) 2025-07-17T08:35:29.5837531Z Cacheline Size: 128(0x80) 2025-07-17T08:35:29.5837763Z Max Clock Freq. (MHz): 2100 2025-07-17T08:35:29.5837997Z BDFID: 50944 2025-07-17T08:35:29.5838219Z Internal Node ID: 2 2025-07-17T08:35:29.5838463Z Compute Unit: 304 2025-07-17T08:35:29.5838699Z SIMDs per CU: 4 2025-07-17T08:35:29.5838920Z Shader Engines: 32 2025-07-17T08:35:29.5839168Z Shader Arrs. per Eng.: 1 2025-07-17T08:35:29.5839421Z WatchPts on Addr. Ranges:4 2025-07-17T08:35:29.5839692Z Coherent Host Access: FALSE 2025-07-17T08:35:29.5839921Z Memory Properties: 2025-07-17T08:35:29.5840118Z Features: KERNEL_DISPATCH 2025-07-17T08:35:29.5840342Z Fast F16 Operation: TRUE 2025-07-17T08:35:29.5840598Z Wavefront Size: 64(0x40) 2025-07-17T08:35:29.5840849Z Workgroup Max Size: 1024(0x400) 2025-07-17T08:35:29.5841075Z Workgroup Max Size per Dimension: 2025-07-17T08:35:29.5841283Z x 1024(0x400) 2025-07-17T08:35:29.5841482Z y 1024(0x400) 2025-07-17T08:35:29.5841676Z z 1024(0x400) 2025-07-17T08:35:29.5841885Z Max Waves Per CU: 32(0x20) 2025-07-17T08:35:29.5842137Z Max Work-item Per CU: 2048(0x800) 2025-07-17T08:35:29.5842384Z Grid Max Size: 4294967295(0xffffffff) 2025-07-17T08:35:29.5842598Z Grid Max Size per Dimension: 2025-07-17T08:35:29.5842781Z x 4294967295(0xffffffff) 2025-07-17T08:35:29.5842977Z y 4294967295(0xffffffff) 2025-07-17T08:35:29.5843316Z z 4294967295(0xffffffff) 2025-07-17T08:35:29.5843546Z Max fbarriers/Workgrp: 32 2025-07-17T08:35:29.5849368Z Packet Processor uCode:: 177 2025-07-17T08:35:29.5849679Z SDMA engine uCode:: 24 2025-07-17T08:35:29.5849930Z IOMMU Support:: None 2025-07-17T08:35:29.5850165Z Pool Info: 2025-07-17T08:35:29.5850336Z Pool 1 2025-07-17T08:35:29.5850565Z Segment: GLOBAL; FLAGS: COARSE GRAINED 2025-07-17T08:35:29.5850807Z Size: 201310208(0xbffc000) KB 2025-07-17T08:35:29.5851048Z Allocatable: TRUE 2025-07-17T08:35:29.5851292Z Alloc Granule: 4KB 2025-07-17T08:35:29.5851546Z Alloc Recommended Granule:2048KB 2025-07-17T08:35:29.5851815Z Alloc Alignment: 4KB 2025-07-17T08:35:29.5852058Z Accessible by all: FALSE 2025-07-17T08:35:29.5852275Z Pool 2 2025-07-17T08:35:29.5852477Z Segment: GLOBAL; FLAGS: EXTENDED FINE GRAINED 2025-07-17T08:35:29.5852885Z Size: 201310208(0xbffc000) KB 2025-07-17T08:35:29.5853118Z Allocatable: TRUE 2025-07-17T08:35:29.5853352Z Alloc Granule: 4KB 2025-07-17T08:35:29.5853605Z Alloc Recommended Granule:2048KB 2025-07-17T08:35:29.5853870Z Alloc Alignment: 4KB 2025-07-17T08:35:29.5854130Z Accessible by all: FALSE 2025-07-17T08:35:29.5854346Z Pool 3 2025-07-17T08:35:29.5854557Z Segment: GLOBAL; FLAGS: FINE GRAINED 2025-07-17T08:35:29.5854792Z Size: 201310208(0xbffc000) KB 2025-07-17T08:35:29.5855018Z Allocatable: TRUE 2025-07-17T08:35:29.5855256Z Alloc Granule: 4KB 2025-07-17T08:35:29.5855508Z Alloc Recommended Granule:2048KB 2025-07-17T08:35:29.5855763Z Alloc Alignment: 4KB 2025-07-17T08:35:29.5856014Z Accessible by all: FALSE 2025-07-17T08:35:29.5856249Z Pool 4 2025-07-17T08:35:29.5856436Z Segment: GROUP 2025-07-17T08:35:29.5856660Z Size: 64(0x40) KB 2025-07-17T08:35:29.5856894Z Allocatable: FALSE 2025-07-17T08:35:29.5857132Z Alloc Granule: 0KB 2025-07-17T08:35:29.5857388Z Alloc Recommended Granule:0KB 2025-07-17T08:35:29.5857633Z Alloc Alignment: 0KB 2025-07-17T08:35:29.5857879Z Accessible by all: FALSE 2025-07-17T08:35:29.5858097Z ISA Info: 2025-07-17T08:35:29.5858276Z ISA 1 2025-07-17T08:35:29.5858491Z Name: amdgcn-amd-amdhsa--gfx942:sramecc+:xnack- 2025-07-17T08:35:29.5858760Z Machine Models: HSA_MACHINE_MODEL_LARGE 2025-07-17T08:35:29.5859033Z Profiles: HSA_PROFILE_BASE 2025-07-17T08:35:29.5859281Z Default Rounding Mode: NEAR 2025-07-17T08:35:29.5859542Z Default Rounding Mode: NEAR 2025-07-17T08:35:29.5859775Z Fast f16: TRUE 2025-07-17T08:35:29.5866818Z Workgroup Max Size: 1024(0x400) 2025-07-17T08:35:29.5867073Z Workgroup Max Size per Dimension: 2025-07-17T08:35:29.5867274Z x 1024(0x400) 2025-07-17T08:35:29.5867477Z y 1024(0x400) 2025-07-17T08:35:29.5867675Z z 1024(0x400) 2025-07-17T08:35:29.5867904Z Grid Max Size: 4294967295(0xffffffff) 2025-07-17T08:35:29.5868121Z Grid Max Size per Dimension: 2025-07-17T08:35:29.5868312Z x 4294967295(0xffffffff) 2025-07-17T08:35:29.5868516Z y 4294967295(0xffffffff) 2025-07-17T08:35:29.5868709Z z 4294967295(0xffffffff) 2025-07-17T08:35:29.5868937Z FBarrier Max Size: 32 2025-07-17T08:35:29.5869157Z ISA 2 2025-07-17T08:35:29.5869386Z Name: amdgcn-amd-amdhsa--gfx9-4-generic:sramecc+:xnack- 2025-07-17T08:35:29.5869659Z Machine Models: HSA_MACHINE_MODEL_LARGE 2025-07-17T08:35:29.5869912Z Profiles: HSA_PROFILE_BASE 2025-07-17T08:35:29.5870313Z Default Rounding Mode: NEAR 2025-07-17T08:35:29.5870560Z Default Rounding Mode: NEAR 2025-07-17T08:35:29.5870799Z Fast f16: TRUE 2025-07-17T08:35:29.5871031Z Workgroup Max Size: 1024(0x400) 2025-07-17T08:35:29.5871260Z Workgroup Max Size per Dimension: 2025-07-17T08:35:29.5871450Z x 1024(0x400) 2025-07-17T08:35:29.5871652Z y 1024(0x400) 2025-07-17T08:35:29.5871857Z z 1024(0x400) 2025-07-17T08:35:29.5872064Z Grid Max Size: 4294967295(0xffffffff) 2025-07-17T08:35:29.5872288Z Grid Max Size per Dimension: 2025-07-17T08:35:29.5872484Z x 4294967295(0xffffffff) 2025-07-17T08:35:29.5872684Z y 4294967295(0xffffffff) 2025-07-17T08:35:29.5872886Z z 4294967295(0xffffffff) 2025-07-17T08:35:29.5873110Z FBarrier Max Size: 32 2025-07-17T08:35:29.5873319Z ******* 2025-07-17T08:35:29.5873489Z Agent 4 2025-07-17T08:35:29.5873641Z ******* 2025-07-17T08:35:29.5873809Z Name: gfx942 2025-07-17T08:35:29.5874038Z Uuid: GPU-52dd8a4356753324 2025-07-17T08:35:29.5874285Z Marketing Name: AMD Instinct MI300X 2025-07-17T08:35:29.5874536Z Vendor Name: AMD 2025-07-17T08:35:29.5874764Z Feature: KERNEL_DISPATCH 2025-07-17T08:35:29.5874995Z Profile: BASE_PROFILE 2025-07-17T08:35:29.5875231Z Float Round Mode: NEAR 2025-07-17T08:35:29.5875467Z Max Queue Number: 128(0x80) 2025-07-17T08:35:29.5875704Z Queue Min Size: 64(0x40) 2025-07-17T08:35:29.5875928Z Queue Max Size: 131072(0x20000) 2025-07-17T08:35:29.5876163Z Queue Type: MULTI 2025-07-17T08:35:29.5876377Z Node: 3 2025-07-17T08:35:29.5876600Z Device Type: GPU 2025-07-17T08:35:29.5876812Z Cache Info: 2025-07-17T08:35:29.5877182Z L1: 32(0x20) KB 2025-07-17T08:35:29.5877398Z L2: 4096(0x1000) KB 2025-07-17T08:35:29.5877597Z L3: 262144(0x40000) KB 2025-07-17T08:35:29.5877815Z Chip ID: 29857(0x74a1) 2025-07-17T08:35:29.5878044Z ASIC Revision: 1(0x1) 2025-07-17T08:35:29.5878287Z Cacheline Size: 128(0x80) 2025-07-17T08:35:29.5878520Z Max Clock Freq. (MHz): 2100 2025-07-17T08:35:29.5878746Z BDFID: 58624 2025-07-17T08:35:29.5878980Z Internal Node ID: 3 2025-07-17T08:35:29.5879253Z Compute Unit: 304 2025-07-17T08:35:29.5879481Z SIMDs per CU: 4 2025-07-17T08:35:29.5879710Z Shader Engines: 32 2025-07-17T08:35:29.5879956Z Shader Arrs. per Eng.: 1 2025-07-17T08:35:29.5880197Z WatchPts on Addr. Ranges:4 2025-07-17T08:35:29.5880453Z Coherent Host Access: FALSE 2025-07-17T08:35:29.5880801Z Memory Properties: 2025-07-17T08:35:29.5880980Z Features: KERNEL_DISPATCH 2025-07-17T08:35:29.5881214Z Fast F16 Operation: TRUE 2025-07-17T08:35:29.5881453Z Wavefront Size: 64(0x40) 2025-07-17T08:35:29.5881703Z Workgroup Max Size: 1024(0x400) 2025-07-17T08:35:29.5881925Z Workgroup Max Size per Dimension: 2025-07-17T08:35:29.5882123Z x 1024(0x400) 2025-07-17T08:35:29.5882318Z y 1024(0x400) 2025-07-17T08:35:29.5882522Z z 1024(0x400) 2025-07-17T08:35:29.5882743Z Max Waves Per CU: 32(0x20) 2025-07-17T08:35:29.5882978Z Max Work-item Per CU: 2048(0x800) 2025-07-17T08:35:29.5883220Z Grid Max Size: 4294967295(0xffffffff) 2025-07-17T08:35:29.5883438Z Grid Max Size per Dimension: 2025-07-17T08:35:29.5883620Z x 4294967295(0xffffffff) 2025-07-17T08:35:29.5883814Z y 4294967295(0xffffffff) 2025-07-17T08:35:29.5884016Z z 4294967295(0xffffffff) 2025-07-17T08:35:29.5884254Z Max fbarriers/Workgrp: 32 2025-07-17T08:35:29.5884511Z Packet Processor uCode:: 177 2025-07-17T08:35:29.5884792Z SDMA engine uCode:: 24 2025-07-17T08:35:29.5885034Z IOMMU Support:: None 2025-07-17T08:35:29.5885254Z Pool Info: 2025-07-17T08:35:29.5885414Z Pool 1 2025-07-17T08:35:29.5885625Z Segment: GLOBAL; FLAGS: COARSE GRAINED 2025-07-17T08:35:29.5885864Z Size: 201310208(0xbffc000) KB 2025-07-17T08:35:29.5886096Z Allocatable: TRUE 2025-07-17T08:35:29.5886348Z Alloc Granule: 4KB 2025-07-17T08:35:29.5886591Z Alloc Recommended Granule:2048KB 2025-07-17T08:35:29.5886848Z Alloc Alignment: 4KB 2025-07-17T08:35:29.5887093Z Accessible by all: FALSE 2025-07-17T08:35:29.5887311Z Pool 2 2025-07-17T08:35:29.5887514Z Segment: GLOBAL; FLAGS: EXTENDED FINE GRAINED 2025-07-17T08:35:29.5887861Z Size: 201310208(0xbffc000) KB 2025-07-17T08:35:29.5888099Z Allocatable: TRUE 2025-07-17T08:35:29.5888337Z Alloc Granule: 4KB 2025-07-17T08:35:29.5888593Z Alloc Recommended Granule:2048KB 2025-07-17T08:35:29.5888840Z Alloc Alignment: 4KB 2025-07-17T08:35:29.5889084Z Accessible by all: FALSE 2025-07-17T08:35:29.5889300Z Pool 3 2025-07-17T08:35:29.5889484Z Segment: GLOBAL; FLAGS: FINE GRAINED 2025-07-17T08:35:29.5889711Z Size: 201310208(0xbffc000) KB 2025-07-17T08:35:29.5889928Z Allocatable: TRUE 2025-07-17T08:35:29.5890173Z Alloc Granule: 4KB 2025-07-17T08:35:29.5890423Z Alloc Recommended Granule:2048KB 2025-07-17T08:35:29.5890670Z Alloc Alignment: 4KB 2025-07-17T08:35:29.5890908Z Accessible by all: FALSE 2025-07-17T08:35:29.5891110Z Pool 4 2025-07-17T08:35:29.5891296Z Segment: GROUP 2025-07-17T08:35:29.5891644Z Size: 64(0x40) KB 2025-07-17T08:35:29.5891859Z Allocatable: FALSE 2025-07-17T08:35:29.5892078Z Alloc Granule: 0KB 2025-07-17T08:35:29.5892321Z Alloc Recommended Granule:0KB 2025-07-17T08:35:29.5892554Z Alloc Alignment: 0KB 2025-07-17T08:35:29.5892784Z Accessible by all: FALSE 2025-07-17T08:35:29.5893047Z ISA Info: 2025-07-17T08:35:29.5893205Z ISA 1 2025-07-17T08:35:29.5893407Z Name: amdgcn-amd-amdhsa--gfx942:sramecc+:xnack- 2025-07-17T08:35:29.5893660Z Machine Models: HSA_MACHINE_MODEL_LARGE 2025-07-17T08:35:29.5893914Z Profiles: HSA_PROFILE_BASE 2025-07-17T08:35:29.5894162Z Default Rounding Mode: NEAR 2025-07-17T08:35:29.5894418Z Default Rounding Mode: NEAR 2025-07-17T08:35:29.5894659Z Fast f16: TRUE 2025-07-17T08:35:29.5894889Z Workgroup Max Size: 1024(0x400) 2025-07-17T08:35:29.5895120Z Workgroup Max Size per Dimension: 2025-07-17T08:35:29.5895321Z x 1024(0x400) 2025-07-17T08:35:29.5895523Z y 1024(0x400) 2025-07-17T08:35:29.5895715Z z 1024(0x400) 2025-07-17T08:35:29.5895943Z Grid Max Size: 4294967295(0xffffffff) 2025-07-17T08:35:29.5896153Z Grid Max Size per Dimension: 2025-07-17T08:35:29.5896340Z x 4294967295(0xffffffff) 2025-07-17T08:35:29.5896610Z y 4294967295(0xffffffff) 2025-07-17T08:35:29.5896827Z z 4294967295(0xffffffff) 2025-07-17T08:35:29.5897067Z FBarrier Max Size: 32 2025-07-17T08:35:29.5897294Z ISA 2 2025-07-17T08:35:29.5897525Z Name: amdgcn-amd-amdhsa--gfx9-4-generic:sramecc+:xnack- 2025-07-17T08:35:29.5897805Z Machine Models: HSA_MACHINE_MODEL_LARGE 2025-07-17T08:35:29.5898078Z Profiles: HSA_PROFILE_BASE 2025-07-17T08:35:29.5898515Z Default Rounding Mode: NEAR 2025-07-17T08:35:29.5898753Z Default Rounding Mode: NEAR 2025-07-17T08:35:29.5898978Z Fast f16: TRUE 2025-07-17T08:35:29.5899194Z Workgroup Max Size: 1024(0x400) 2025-07-17T08:35:29.5899408Z Workgroup Max Size per Dimension: 2025-07-17T08:35:29.5899582Z x 1024(0x400) 2025-07-17T08:35:29.5899763Z y 1024(0x400) 2025-07-17T08:35:29.5899949Z z 1024(0x400) 2025-07-17T08:35:29.5900150Z Grid Max Size: 4294967295(0xffffffff) 2025-07-17T08:35:29.5900362Z Grid Max Size per Dimension: 2025-07-17T08:35:29.5900528Z x 4294967295(0xffffffff) 2025-07-17T08:35:29.5900722Z y 4294967295(0xffffffff) 2025-07-17T08:35:29.5900908Z z 4294967295(0xffffffff) 2025-07-17T08:35:29.5901120Z FBarrier Max Size: 32 2025-07-17T08:35:29.5901316Z *** Done *** 2025-07-17T08:35:29.6064007Z ##[group]Run ngpu=$(rocminfo | grep -c -E 'Name:.*\sgfx') 2025-07-17T08:35:29.6064607Z ngpu=$(rocminfo | grep -c -E 'Name:.*\sgfx') 2025-07-17T08:35:29.6065060Z msg="Please file an issue on pytorch/pytorch reporting the faulty runner. Include a link to the runner logs so the runner can be identified" 2025-07-17T08:35:29.6065610Z if [[ $ngpu -eq 0 ]]; then 2025-07-17T08:35:29.6065856Z  echo "Error: Failed to detect any GPUs on the runner" 2025-07-17T08:35:29.6066100Z  echo "$msg" 2025-07-17T08:35:29.6066282Z  exit 1 2025-07-17T08:35:29.6066435Z fi 2025-07-17T08:35:29.6066612Z if [[ $ngpu -eq 1 ]]; then 2025-07-17T08:35:29.6066893Z  echo "Error: only 1 GPU detected, at least 2 GPUs are needed for distributed jobs" 2025-07-17T08:35:29.6067184Z  echo "$msg" 2025-07-17T08:35:29.6067346Z  exit 1 2025-07-17T08:35:29.6067509Z fi 2025-07-17T08:35:29.6079379Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T08:35:29.6079631Z env: 2025-07-17T08:35:29.6079819Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:35:29.6080010Z ##[endgroup] 2025-07-17T08:35:29.8728954Z ##[group]Run pytorch/pytorch/.github/actions/diskspace-cleanup@main 2025-07-17T08:35:29.8729232Z with: 2025-07-17T08:35:29.8729390Z diskspace-cutoff: 70 2025-07-17T08:35:29.8729551Z env: 2025-07-17T08:35:29.8729704Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:35:29.8729871Z ##[endgroup] 2025-07-17T08:35:29.8764509Z ##[group]Run set -ex 2025-07-17T08:35:29.8764728Z set -ex 2025-07-17T08:35:29.8764896Z diskspace_cutoff=70 2025-07-17T08:35:29.8765159Z docker_root_dir=$(docker info -f '{{.DockerRootDir}}') 2025-07-17T08:35:29.8765423Z if [ ! -d "$docker_root_dir" ]; then 2025-07-17T08:35:29.8765744Z  echo "Docker root directory ($docker_root_dir) does not exist. Skipping disk space check." 2025-07-17T08:35:29.8766050Z  exit 0 2025-07-17T08:35:29.8766210Z fi 2025-07-17T08:35:29.8766490Z diskspace=$(df -H --output=pcent ${docker_root_dir} | sed -n 2p | sed 's/%//' | sed 's/ //') 2025-07-17T08:35:29.8767041Z msg="Please file an issue on pytorch/pytorch reporting the faulty runner. Include a link to the runner logs so the runner can be identified" 2025-07-17T08:35:29.8767488Z if [[ "$diskspace" -ge "$diskspace_cutoff" ]] ; then 2025-07-17T08:35:29.8767733Z  docker system prune -af 2025-07-17T08:35:29.8768074Z  diskspace_new=$(df -H --output=pcent ${docker_root_dir} | sed -n 2p | sed 's/%//' | sed 's/ //') 2025-07-17T08:35:29.8768661Z  if [[ "$diskspace_new" -gt "$diskspace_cutoff" ]] ; then 2025-07-17T08:35:29.8769141Z  echo "Error: Available diskspace is less than $diskspace_cutoff percent. Not enough diskspace." 2025-07-17T08:35:29.8769466Z  echo "$msg" 2025-07-17T08:35:29.8769643Z  exit 1 2025-07-17T08:35:29.8769804Z  else 2025-07-17T08:35:29.8769999Z  difference=$((diskspace - diskspace_new)) 2025-07-17T08:35:29.8770252Z  echo "Diskspace saved: $difference percent" 2025-07-17T08:35:29.8770470Z  fi 2025-07-17T08:35:29.8770620Z fi 2025-07-17T08:35:29.8782577Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T08:35:29.8782828Z env: 2025-07-17T08:35:29.8782970Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:35:29.8783141Z ##[endgroup] 2025-07-17T08:35:29.8817676Z + diskspace_cutoff=70 2025-07-17T08:35:29.8822853Z ++ docker info -f '{{.DockerRootDir}}' 2025-07-17T08:35:29.9517030Z + docker_root_dir=/home/runner/docker-data 2025-07-17T08:35:29.9517382Z + '[' '!' -d /home/runner/docker-data ']' 2025-07-17T08:35:29.9526428Z ++ df -H --output=pcent /home/runner/docker-data 2025-07-17T08:35:29.9526687Z ++ sed -n 2p 2025-07-17T08:35:29.9527258Z ++ sed s/%// 2025-07-17T08:35:29.9528558Z ++ sed 's/ //' 2025-07-17T08:35:29.9544175Z + diskspace=' 1' 2025-07-17T08:35:29.9545139Z + msg='Please file an issue on pytorch/pytorch reporting the faulty runner. Include a link to the runner logs so the runner can be identified' 2025-07-17T08:35:29.9545655Z + [[ 1 -ge 70 ]] 2025-07-17T08:35:29.9578492Z ##[group]Run RUNNER_ARTIFACT_DIR="${RUNNER_TEMP}/artifacts" 2025-07-17T08:35:29.9578831Z RUNNER_ARTIFACT_DIR="${RUNNER_TEMP}/artifacts" 2025-07-17T08:35:29.9579077Z rm -rf "${RUNNER_ARTIFACT_DIR}" 2025-07-17T08:35:29.9579306Z mkdir -p "${RUNNER_ARTIFACT_DIR}" 2025-07-17T08:35:29.9579589Z echo "RUNNER_ARTIFACT_DIR=${RUNNER_ARTIFACT_DIR}" >> "${GITHUB_ENV}" 2025-07-17T08:35:29.9579881Z  2025-07-17T08:35:29.9580087Z RUNNER_TEST_RESULTS_DIR="${RUNNER_TEMP}/test-results" 2025-07-17T08:35:29.9580350Z rm -rf "${RUNNER_TEST_RESULTS_DIR}" 2025-07-17T08:35:29.9580569Z mkdir -p "${RUNNER_TEST_RESULTS_DIR}" 2025-07-17T08:35:29.9580860Z echo "RUNNER_TEST_RESULTS_DIR=${RUNNER_TEST_RESULTS_DIR}" >> "${GITHUB_ENV}" 2025-07-17T08:35:29.9581156Z  2025-07-17T08:35:29.9581306Z RUNNER_DOCS_DIR="${RUNNER_TEMP}/docs" 2025-07-17T08:35:29.9581517Z rm -rf "${RUNNER_DOCS_DIR}" 2025-07-17T08:35:29.9581716Z mkdir -p "${RUNNER_DOCS_DIR}" 2025-07-17T08:35:29.9581959Z echo "RUNNER_DOCS_DIR=${RUNNER_DOCS_DIR}" >> "${GITHUB_ENV}" 2025-07-17T08:35:29.9594481Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T08:35:29.9594717Z env: 2025-07-17T08:35:29.9594861Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:35:29.9595021Z ##[endgroup] 2025-07-17T08:35:29.9708192Z ##[group]Run env | grep '^GITHUB' >> "${RUNNER_TEMP}/github_env_${GITHUB_RUN_ID}" 2025-07-17T08:35:29.9708546Z env | grep '^GITHUB' >> "${RUNNER_TEMP}/github_env_${GITHUB_RUN_ID}" 2025-07-17T08:35:29.9708842Z env | grep '^CI' >> "${RUNNER_TEMP}/github_env_${GITHUB_RUN_ID}" 2025-07-17T08:35:29.9718872Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T08:35:29.9719123Z env: 2025-07-17T08:35:29.9719283Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:35:29.9719494Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T08:35:29.9719777Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T08:35:29.9720031Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T08:35:29.9720234Z ##[endgroup] 2025-07-17T08:35:29.9794804Z ##[group]Run # All GPUs are visible to the runner; visibility, if needed, will be set by run_test.py. 2025-07-17T08:35:29.9795245Z # All GPUs are visible to the runner; visibility, if needed, will be set by run_test.py. 2025-07-17T08:35:29.9795795Z # Add render group for container creation. 2025-07-17T08:35:29.9796067Z render_gid=`cat /etc/group | grep render | cut -d: -f3` 2025-07-17T08:35:29.9796397Z # Ensure GPU isolation if pod is part of kubernetes setup with DEVICE_FLAG. 2025-07-17T08:35:29.9796716Z if [ -f "/etc/podinfo/gha-render-devices" ]; then 2025-07-17T08:35:29.9797003Z  DEVICE_FLAG=$(cat /etc/podinfo/gha-render-devices) 2025-07-17T08:35:29.9797232Z else 2025-07-17T08:35:29.9797391Z  DEVICE_FLAG="--device /dev/dri" 2025-07-17T08:35:29.9797579Z fi 2025-07-17T08:35:29.9797866Z # The --group-add daemon and --group-add bin are needed in the Ubuntu 24.04 and Almalinux OSs respectively. 2025-07-17T08:35:29.9798315Z # This is due to the device files (/dev/kfd & /dev/dri) being owned by video group on bare metal. 2025-07-17T08:35:29.9798739Z # This video group ID maps to subgid 1 inside the docker image due to the /etc/subgid entries. 2025-07-17T08:35:29.9799172Z # The group name corresponding to group ID 1 can change depending on the OS, so both are necessary. 2025-07-17T08:35:29.9800034Z echo "GPU_FLAG=--device=/dev/mem --device=/dev/kfd $DEVICE_FLAG --group-add video --group-add $render_gid --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host" >> "${GITHUB_ENV}" 2025-07-17T08:35:29.9810752Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T08:35:29.9810983Z env: 2025-07-17T08:35:29.9811130Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:35:29.9811340Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T08:35:29.9811624Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T08:35:29.9811875Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T08:35:29.9812076Z ##[endgroup] 2025-07-17T08:35:29.9939528Z ##[group]Run aws-actions/configure-aws-credentials@ececac1a45f3b08a01d2dd070d28d111c5fe6722 2025-07-17T08:35:29.9939861Z with: 2025-07-17T08:35:29.9940099Z role-to-assume: arn:aws:iam::308535385114:role/gha_workflow_s3_and_ecr_read_only 2025-07-17T08:35:29.9940377Z aws-region: us-east-1 2025-07-17T08:35:29.9940592Z role-duration-seconds: 18000 2025-07-17T08:35:29.9940826Z audience: sts.amazonaws.com 2025-07-17T08:35:29.9940999Z env: 2025-07-17T08:35:29.9941145Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:35:29.9941360Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T08:35:29.9941635Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T08:35:29.9941888Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T08:35:29.9942593Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T08:35:29.9943217Z ##[endgroup] 2025-07-17T08:35:30.2180628Z Assuming role with OIDC 2025-07-17T08:35:34.4167469Z Authenticated as assumedRoleId AROAUPVRELQNLLCOPFEJR:GitHubActions 2025-07-17T08:35:34.4681808Z ##[group]Run aws-actions/amazon-ecr-login@062b18b96a7aff071d4dc91bc00c4c1a7945b076 2025-07-17T08:35:34.4682146Z with: 2025-07-17T08:35:34.4682307Z mask-password: true 2025-07-17T08:35:34.4682492Z registry-type: private 2025-07-17T08:35:34.4682699Z skip-logout: false 2025-07-17T08:35:34.4682868Z env: 2025-07-17T08:35:34.4683023Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:35:34.4683254Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T08:35:34.4683538Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T08:35:34.4683811Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T08:35:34.4684503Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T08:35:34.4685175Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T08:35:34.4685396Z AWS_REGION: us-east-1 2025-07-17T08:35:34.4686050Z AWS_ACCESS_KEY_ID: *** 2025-07-17T08:35:34.4686352Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T08:35:34.4690304Z AWS_SESSION_TOKEN: *** 2025-07-17T08:35:34.4690501Z ##[endgroup] 2025-07-17T08:35:40.7153770Z Logging into registry 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-07-17T08:35:48.0034913Z ##[group]Run pytorch/test-infra/.github/actions/calculate-docker-image@main 2025-07-17T08:35:48.0035228Z with: 2025-07-17T08:35:48.0035682Z docker-image-name: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T08:35:48.0036168Z use-custom-docker-registry: true 2025-07-17T08:35:48.0036374Z docker-build-dir: .ci/docker 2025-07-17T08:35:48.0036567Z docker-build-script: ./build.sh 2025-07-17T08:35:48.0036760Z working-directory: . 2025-07-17T08:35:48.0036988Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-07-17T08:35:48.0037238Z force-push: false 2025-07-17T08:35:48.0037391Z env: 2025-07-17T08:35:48.0037539Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:35:48.0037760Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T08:35:48.0038052Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T08:35:48.0051457Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T08:35:48.0052150Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T08:35:48.0052822Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T08:35:48.0053007Z AWS_REGION: us-east-1 2025-07-17T08:35:48.0053327Z AWS_ACCESS_KEY_ID: *** 2025-07-17T08:35:48.0053587Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T08:35:48.0057529Z AWS_SESSION_TOKEN: *** 2025-07-17T08:35:48.0057711Z ##[endgroup] 2025-07-17T08:35:48.0076053Z ##[group]Run set -ex 2025-07-17T08:35:48.0076255Z set -ex 2025-07-17T08:35:48.0076407Z  2025-07-17T08:35:48.0076661Z # If the docker build directory or the build script doesn't exist, the action will 2025-07-17T08:35:48.0077078Z # gracefully return the docker image name as it is. Pulling docker image in Linux 2025-07-17T08:35:48.0077425Z # job could then download the pre-built image as usual 2025-07-17T08:35:48.0077850Z if [[ -d "${DOCKER_BUILD_DIR}" ]] && [[ -f "${DOCKER_BUILD_DIR}/${DOCKER_BUILD_SCRIPT}" ]] && [[ "${USE_CUSTOM_DOCKER_REGISTRY}" == "true" ]]; then 2025-07-17T08:35:48.0078237Z  echo "skip=false" >> "${GITHUB_OUTPUT}" 2025-07-17T08:35:48.0078444Z else 2025-07-17T08:35:48.0078621Z  echo "skip=true" >> "${GITHUB_OUTPUT}" 2025-07-17T08:35:48.0078903Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2025-07-17T08:35:48.0079155Z  2025-07-17T08:35:48.0079500Z  echo "Not using custom ECR registry. Either it was not requested or there is no Docker build script in the ${REPO_NAME} repo..." 2025-07-17T08:35:48.0079886Z  exit 0 2025-07-17T08:35:48.0080036Z fi 2025-07-17T08:35:48.0080187Z  2025-07-17T08:35:48.0080415Z if [[ "${DOCKER_IMAGE_NAME}" == *"${DOCKER_REGISTRY}/${REPO_NAME}"* ]]; then 2025-07-17T08:35:48.0080782Z  # The docker image name already includes the ECR prefix and tag, so we can just 2025-07-17T08:35:48.0081114Z  # use it as it is, but first let's extract the tag 2025-07-17T08:35:48.0081435Z  DOCKER_TAG=$(echo "${DOCKER_IMAGE_NAME}" | awk -F '[:,]' '{print $2}') 2025-07-17T08:35:48.0081754Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-07-17T08:35:48.0082056Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2025-07-17T08:35:48.0082308Z else 2025-07-17T08:35:48.0082482Z  if [[ "${DOCKER_IMAGE_NAME}" == *:* ]]; then 2025-07-17T08:35:48.0082731Z  CUSTOM_TAG_PREFIX=${DOCKER_IMAGE_NAME#*:} 2025-07-17T08:35:48.0082981Z  DOCKER_IMAGE_NAME=${DOCKER_IMAGE_NAME%%:*} 2025-07-17T08:35:48.0083193Z  fi 2025-07-17T08:35:48.0083709Z  DOCKER_TAG=${CUSTOM_TAG_PREFIX:+${CUSTOM_TAG_PREFIX}-}$(git rev-parse HEAD:"${DOCKER_BUILD_DIR}") 2025-07-17T08:35:48.0084084Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-07-17T08:35:48.0084478Z  echo "docker-image=${DOCKER_REGISTRY}/${REPO_NAME}/${DOCKER_IMAGE_NAME}:${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-07-17T08:35:48.0084897Z  echo "custom-tag-prefix=${CUSTOM_TAG_PREFIX}" >> "${GITHUB_OUTPUT}" 2025-07-17T08:35:48.0085158Z fi 2025-07-17T08:35:48.0096689Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T08:35:48.0096934Z env: 2025-07-17T08:35:48.0097086Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:35:48.0097311Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T08:35:48.0097606Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T08:35:48.0097879Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T08:35:48.0098571Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T08:35:48.0099382Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T08:35:48.0099581Z AWS_REGION: us-east-1 2025-07-17T08:35:48.0099804Z AWS_ACCESS_KEY_ID: *** 2025-07-17T08:35:48.0100062Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T08:35:48.0104039Z AWS_SESSION_TOKEN: *** 2025-07-17T08:35:48.0104227Z REPO_NAME: pytorch 2025-07-17T08:35:48.0104687Z DOCKER_IMAGE_NAME: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T08:35:48.0105162Z DOCKER_BUILD_DIR: .ci/docker 2025-07-17T08:35:48.0105442Z DOCKER_BUILD_SCRIPT: ./build.sh 2025-07-17T08:35:48.0105691Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-07-17T08:35:48.0105952Z USE_CUSTOM_DOCKER_REGISTRY: true 2025-07-17T08:35:48.0106156Z CUSTOM_TAG_PREFIX: 2025-07-17T08:35:48.0106321Z ##[endgroup] 2025-07-17T08:35:48.0138618Z + [[ -d .ci/docker ]] 2025-07-17T08:35:48.0138821Z + [[ -f .ci/docker/./build.sh ]] 2025-07-17T08:35:48.0139027Z + [[ true == \t\r\u\e ]] 2025-07-17T08:35:48.0139796Z + echo skip=false 2025-07-17T08:35:48.0140576Z + [[ 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-01345e7669bb7198df9fce7a02a4a12ce8c84f2d == *\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* ]] 2025-07-17T08:35:48.0149026Z ++ echo 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T08:35:48.0150352Z ++ awk -F '[:,]' '{print $2}' 2025-07-17T08:35:48.0163463Z + DOCKER_TAG=pytorch-linux-noble-rocm-n-py3-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T08:35:48.0164010Z + echo docker-tag=pytorch-linux-noble-rocm-n-py3-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T08:35:48.0164728Z + echo docker-image=308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T08:35:48.0196220Z ##[group]Run set +e 2025-07-17T08:35:48.0196445Z set +e 2025-07-17T08:35:48.0196616Z set -x 2025-07-17T08:35:48.0196770Z  2025-07-17T08:35:48.0196926Z login() { 2025-07-17T08:35:48.0197249Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2025-07-17T08:35:48.0197573Z } 2025-07-17T08:35:48.0197728Z  2025-07-17T08:35:48.0197882Z retry () { 2025-07-17T08:35:48.0198069Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2025-07-17T08:35:48.0198280Z } 2025-07-17T08:35:48.0198420Z  2025-07-17T08:35:48.0198577Z retry login "${DOCKER_REGISTRY}" 2025-07-17T08:35:48.0198772Z  2025-07-17T08:35:48.0198946Z START_TIME=$(date +%s) 2025-07-17T08:35:48.0199429Z # Wait up to 120 minutes 2025-07-17T08:35:48.0199676Z while [[ $(( $(date +%s) - 7200 )) -lt $START_TIME ]]; do 2025-07-17T08:35:48.0199979Z  # Check if image already exists, if it does then skip building it 2025-07-17T08:35:48.0200287Z  if docker manifest inspect "${DOCKER_IMAGE}"; then 2025-07-17T08:35:48.0200520Z  exit 0 2025-07-17T08:35:48.0200681Z  fi 2025-07-17T08:35:48.0200826Z  2025-07-17T08:35:48.0201077Z  # NB: This flag is used by Docker build workflow to push the image to ECR, so we can 2025-07-17T08:35:48.0201481Z  # use this to differentiate between the Docker build and regular build jobs. For the 2025-07-17T08:35:48.0201881Z  # latter, it will wait for the Docker images to become available before continuing 2025-07-17T08:35:48.0202200Z  if [ "${DOCKER_PUSH:-false}" == "true" ]; then 2025-07-17T08:35:48.0202680Z  # It's a Docker build job, let's build the image 2025-07-17T08:35:48.0202913Z  break 2025-07-17T08:35:48.0203073Z  else 2025-07-17T08:35:48.0203297Z  # It's a regular build job, wait for the image to become available 2025-07-17T08:35:48.0203555Z  sleep 300 2025-07-17T08:35:48.0203729Z  fi 2025-07-17T08:35:48.0203882Z done 2025-07-17T08:35:48.0204028Z  2025-07-17T08:35:48.0204258Z # NB: This part requires a full checkout. Otherwise, the merge base will 2025-07-17T08:35:48.0204599Z # be empty. The default action would be to continue rebuild the image 2025-07-17T08:35:48.0204911Z if [[ "$BASE_REVISION" = "$(git rev-parse HEAD)" ]]; then 2025-07-17T08:35:48.0205199Z  # if we're on the base branch then use the parent commit 2025-07-17T08:35:48.0205459Z  MERGE_BASE=$(git rev-parse HEAD~) 2025-07-17T08:35:48.0205659Z else 2025-07-17T08:35:48.0205881Z  # otherwise we're on a PR, so use the most recent base commit 2025-07-17T08:35:48.0206181Z  MERGE_BASE=$(git merge-base HEAD "$BASE_REVISION") 2025-07-17T08:35:48.0206411Z fi 2025-07-17T08:35:48.0206556Z  2025-07-17T08:35:48.0206717Z if [[ -z "${MERGE_BASE}" ]]; then 2025-07-17T08:35:48.0206943Z  echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2025-07-17T08:35:48.0207152Z  2025-07-17T08:35:48.0207439Z  echo "Finding merge base only works with full checkout, please set fetch-depth to 0, continuing ..." 2025-07-17T08:35:48.0207785Z  exit 0 2025-07-17T08:35:48.0207934Z fi 2025-07-17T08:35:48.0208086Z  2025-07-17T08:35:48.0208292Z if ! git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}"; then 2025-07-17T08:35:48.0208701Z  echo "Directory '${DOCKER_BUILD_DIR}' not found in commit $MERGE_BASE, you should rebase onto a more recent commit" 2025-07-17T08:35:48.0209057Z  exit 1 2025-07-17T08:35:48.0209207Z fi 2025-07-17T08:35:48.0209355Z  2025-07-17T08:35:48.0209586Z PREVIOUS_DOCKER_TAG=$(git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}") 2025-07-17T08:35:48.0209983Z # If no image exists but the hash is the same as the previous hash then we should error out here 2025-07-17T08:35:48.0210339Z if [[ "${PREVIOUS_DOCKER_TAG}" == "${DOCKER_TAG}" ]]; then 2025-07-17T08:35:48.0210747Z  echo "WARNING: Something has gone wrong and the previous image isn't available for the merge-base of your branch" 2025-07-17T08:35:48.0211200Z  echo " Will re-build docker image to store in local cache, TTS may be longer" 2025-07-17T08:35:48.0211477Z fi 2025-07-17T08:35:48.0211621Z  2025-07-17T08:35:48.0211794Z echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2025-07-17T08:35:48.0223377Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T08:35:48.0223624Z env: 2025-07-17T08:35:48.0223788Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:35:48.0224139Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T08:35:48.0224430Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T08:35:48.0224699Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T08:35:48.0225504Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T08:35:48.0226193Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T08:35:48.0226393Z AWS_REGION: us-east-1 2025-07-17T08:35:48.0226668Z AWS_ACCESS_KEY_ID: *** 2025-07-17T08:35:48.0226958Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T08:35:48.0230887Z AWS_SESSION_TOKEN: *** 2025-07-17T08:35:48.0231076Z DOCKER_BUILD_DIR: .ci/docker 2025-07-17T08:35:48.0231296Z BASE_REVISION: a38f433be2e94a64b095a44ba39879d02d0c2316 2025-07-17T08:35:48.0231970Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T08:35:48.0232548Z DOCKER_TAG: pytorch-linux-noble-rocm-n-py3-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T08:35:48.0232922Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-07-17T08:35:48.0233170Z DOCKER_PUSH: 2025-07-17T08:35:48.0233330Z ##[endgroup] 2025-07-17T08:35:48.0264710Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-07-17T08:35:48.0265044Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-07-17T08:35:48.0267647Z + aws ecr get-login-password --region us-east-1 2025-07-17T08:35:48.0268600Z /home/runner/_work/_temp/890807df-ca33-4597-9efb-03bf95e08a86.sh: line 5: aws: command not found 2025-07-17T08:35:48.0269108Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-07-17T08:35:48.0439201Z Error: Cannot perform an interactive login from a non TTY device 2025-07-17T08:35:48.0458999Z + sleep 1 2025-07-17T08:35:49.0472203Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-07-17T08:35:49.0475905Z + aws ecr get-login-password --region us-east-1 2025-07-17T08:35:49.0477683Z /home/runner/_work/_temp/890807df-ca33-4597-9efb-03bf95e08a86.sh: line 5: aws: command not found 2025-07-17T08:35:49.0478228Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-07-17T08:35:49.0629815Z Error: Cannot perform an interactive login from a non TTY device 2025-07-17T08:35:49.0650107Z + sleep 2 2025-07-17T08:35:51.0664892Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-07-17T08:35:51.0668045Z + aws ecr get-login-password --region us-east-1 2025-07-17T08:35:51.0668985Z /home/runner/_work/_temp/890807df-ca33-4597-9efb-03bf95e08a86.sh: line 5: aws: command not found 2025-07-17T08:35:51.0669652Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-07-17T08:35:51.0820651Z Error: Cannot perform an interactive login from a non TTY device 2025-07-17T08:35:51.0846509Z ++ date +%s 2025-07-17T08:35:51.0856030Z + START_TIME=1752741351 2025-07-17T08:35:51.0860825Z ++ date +%s 2025-07-17T08:35:51.0870642Z + [[ 1752734151 -lt 1752741351 ]] 2025-07-17T08:35:51.0871284Z + docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T08:35:53.6843479Z { 2025-07-17T08:35:53.6844017Z "schemaVersion": 2, 2025-07-17T08:35:53.6844467Z "mediaType": "application/vnd.docker.distribution.manifest.v2+json", 2025-07-17T08:35:53.6845040Z "config": { 2025-07-17T08:35:53.6845372Z "mediaType": 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"sha256:88d77fb451a4cbb5d7c86b95cc850f83679bb2f75fc7a931023cf8166e4c30e7" 2025-07-17T08:35:53.6930170Z }, 2025-07-17T08:35:53.6930316Z { 2025-07-17T08:35:53.6930532Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T08:35:53.6930784Z "size": 175, 2025-07-17T08:35:53.6931054Z "digest": "sha256:eb98dfa00416bfd6b6d30396e39b46cb2d85205a25108022278c641176a1023a" 2025-07-17T08:35:53.6931344Z }, 2025-07-17T08:35:53.6931484Z { 2025-07-17T08:35:53.6931696Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T08:35:53.6931949Z "size": 1897, 2025-07-17T08:35:53.6932208Z "digest": "sha256:0971ac0664a4884417ddb7be991372a3420280874578d17dc678b3d2ef293c3a" 2025-07-17T08:35:53.6932492Z }, 2025-07-17T08:35:53.6932630Z { 2025-07-17T08:35:53.6933074Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T08:35:53.6933336Z "size": 162687915, 2025-07-17T08:35:53.6933603Z "digest": "sha256:9b9239f68e90235feae6f403c501599dea179aca869918e48adbe25ddfac9bf4" 2025-07-17T08:35:53.6933891Z }, 2025-07-17T08:35:53.6934027Z { 2025-07-17T08:35:53.6934232Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T08:35:53.6934481Z "size": 304, 2025-07-17T08:35:53.6934734Z "digest": "sha256:4d3441d455038ca68278ebfb4461fac6943dfe5632034e6dcc202f1455ea1842" 2025-07-17T08:35:53.6935012Z }, 2025-07-17T08:35:53.6935148Z { 2025-07-17T08:35:53.6935355Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T08:35:53.6935603Z "size": 32, 2025-07-17T08:35:53.6935862Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-07-17T08:35:53.6936136Z }, 2025-07-17T08:35:53.6936396Z { 2025-07-17T08:35:53.6936601Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T08:35:53.6936851Z "size": 108, 2025-07-17T08:35:53.6937102Z "digest": "sha256:bc1ff02b1df01038762aecf59ce5f90041fe38b68e9b53988bac836a26398de3" 2025-07-17T08:35:53.6937381Z }, 2025-07-17T08:35:53.6937515Z { 2025-07-17T08:35:53.6937714Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T08:35:53.6937964Z "size": 54145699, 2025-07-17T08:35:53.6938224Z "digest": "sha256:e20c94532338189b44d993e4075ee1bda5c2e71da7f8e3ec6812b96dbc85502e" 2025-07-17T08:35:53.6938503Z } 2025-07-17T08:35:53.6938632Z ] 2025-07-17T08:35:53.6938767Z } 2025-07-17T08:35:53.6968033Z ##[group]Run set -eux 2025-07-17T08:35:53.6968248Z set -eux 2025-07-17T08:35:53.6968801Z 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 2025-07-17T08:35:53.6981338Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T08:35:53.6981603Z env: 2025-07-17T08:35:53.6981776Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:35:53.6982014Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T08:35:53.6982311Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T08:35:53.6982594Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T08:35:53.6983286Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T08:35:53.6983951Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T08:35:53.6984149Z AWS_REGION: us-east-1 2025-07-17T08:35:53.6984445Z AWS_ACCESS_KEY_ID: *** 2025-07-17T08:35:53.6984708Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T08:35:53.6988747Z AWS_SESSION_TOKEN: *** 2025-07-17T08:35:53.6988941Z ##[endgroup] 2025-07-17T08:35:53.7029887Z + aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token 2025-07-17T08:35:53.7030516Z + jq --raw-output .SecretString 2025-07-17T08:35:53.7030898Z /home/runner/_work/_temp/afbfa90f-8239-4a05-a738-c2d14c964c59.sh: line 2: aws: command not found 2025-07-17T08:35:53.7031263Z + jq -r .docker_hub_readonly_token 2025-07-17T08:35:53.7032626Z + docker login --username pytorchbot --password-stdin 2025-07-17T08:35:53.7185666Z Error: Cannot perform an interactive login from a non TTY device 2025-07-17T08:35:53.7212040Z ##[error]Process completed with exit code 1. 2025-07-17T08:35:53.7301152Z ##[group]Run pytorch/test-infra/.github/actions/pull-docker-image@main 2025-07-17T08:35:53.7301462Z with: 2025-07-17T08:35:53.7301905Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T08:35:53.7302442Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-07-17T08:35:53.7302731Z env: 2025-07-17T08:35:53.7302891Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:35:53.7303126Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T08:35:53.7303438Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T08:35:53.7303721Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T08:35:53.7304448Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T08:35:53.7305115Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T08:35:53.7305395Z AWS_REGION: us-east-1 2025-07-17T08:35:53.7305684Z AWS_ACCESS_KEY_ID: *** 2025-07-17T08:35:53.7305953Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T08:35:53.7309892Z AWS_SESSION_TOKEN: *** 2025-07-17T08:35:53.7310091Z ##[endgroup] 2025-07-17T08:35:53.7325335Z ##[group]Run set -x 2025-07-17T08:35:53.7325547Z set -x 2025-07-17T08:35:53.7325709Z set +e 2025-07-17T08:35:53.7325872Z  2025-07-17T08:35:53.7326036Z login() { 2025-07-17T08:35:53.7326356Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2025-07-17T08:35:53.7326693Z } 2025-07-17T08:35:53.7326853Z  2025-07-17T08:35:53.7327009Z retry () { 2025-07-17T08:35:53.7327194Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2025-07-17T08:35:53.7327421Z } 2025-07-17T08:35:53.7327575Z  2025-07-17T08:35:53.7327750Z retry login "${DOCKER_REGISTRY}" 2025-07-17T08:35:53.7327966Z  2025-07-17T08:35:53.7328281Z IMAGE_SIZE=$(docker manifest inspect "${DOCKER_IMAGE}" | jq '[.layers[].size, .config.size] | add / 1024 / 1024') 2025-07-17T08:35:53.7328694Z echo "Compressed size of image in MB: ${IMAGE_SIZE}" 2025-07-17T08:35:53.7328946Z  2025-07-17T08:35:53.7329099Z set -e 2025-07-17T08:35:53.7329332Z # ignore output since only exit code is used for conditional 2025-07-17T08:35:53.7329646Z # only pull docker image if it's not available locally 2025-07-17T08:35:53.7329989Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then 2025-07-17T08:35:53.7330305Z  retry docker pull "${DOCKER_IMAGE}" 2025-07-17T08:35:53.7330516Z fi 2025-07-17T08:35:53.7342171Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T08:35:53.7342435Z env: 2025-07-17T08:35:53.7342596Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:35:53.7342831Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T08:35:53.7343137Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T08:35:53.7343414Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T08:35:53.7344114Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T08:35:53.7344796Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T08:35:53.7344993Z AWS_REGION: us-east-1 2025-07-17T08:35:53.7345231Z AWS_ACCESS_KEY_ID: *** 2025-07-17T08:35:53.7345563Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T08:35:53.7349861Z AWS_SESSION_TOKEN: *** 2025-07-17T08:35:53.7350652Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T08:35:53.7351198Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-07-17T08:35:53.7351454Z ##[endgroup] 2025-07-17T08:35:53.7386064Z + set +e 2025-07-17T08:35:53.7390338Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-07-17T08:35:53.7390651Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-07-17T08:35:53.7391018Z + aws ecr get-login-password --region us-east-1 2025-07-17T08:35:53.7391374Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-07-17T08:35:53.7391817Z /home/runner/_work/_temp/68e25b6d-62ef-4a0d-9b8f-4cef075ea6ed.sh: line 5: aws: command not found 2025-07-17T08:35:53.7550889Z Error: Cannot perform an interactive login from a non TTY device 2025-07-17T08:35:53.7583266Z + sleep 1 2025-07-17T08:35:54.7598507Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-07-17T08:35:54.7601684Z + aws ecr get-login-password --region us-east-1 2025-07-17T08:35:54.7602173Z /home/runner/_work/_temp/68e25b6d-62ef-4a0d-9b8f-4cef075ea6ed.sh: line 5: aws: command not found 2025-07-17T08:35:54.7605049Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-07-17T08:35:54.7748881Z Error: Cannot perform an interactive login from a non TTY device 2025-07-17T08:35:54.7787509Z + sleep 2 2025-07-17T08:35:56.7804296Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-07-17T08:35:56.7808465Z + aws ecr get-login-password --region us-east-1 2025-07-17T08:35:56.7809914Z /home/runner/_work/_temp/68e25b6d-62ef-4a0d-9b8f-4cef075ea6ed.sh: line 5: aws: command not found 2025-07-17T08:35:56.7810497Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-07-17T08:35:56.7956038Z Error: Cannot perform an interactive login from a non TTY device 2025-07-17T08:35:56.7985847Z ++ docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T08:35:56.8005300Z ++ jq '[.layers[].size, .config.size] | add / 1024 / 1024' 2025-07-17T08:35:57.4260869Z + IMAGE_SIZE=22123.39352798462 2025-07-17T08:35:57.4261212Z Compressed size of image in MB: 22123.39352798462 2025-07-17T08:35:57.4261524Z + echo 'Compressed size of image in MB: 22123.39352798462' 2025-07-17T08:35:57.4261772Z + set -e 2025-07-17T08:35:57.4262263Z + docker inspect --type=image 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T08:35:57.4440580Z + retry docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T08:35:57.4441487Z + docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T08:35:57.9310843Z pytorch-linux-noble-rocm-n-py3-01345e7669bb7198df9fce7a02a4a12ce8c84f2d: Pulling from pytorch/ci-image 2025-07-17T08:35:57.9314218Z e87500e69896: Pulling fs layer 2025-07-17T08:35:57.9315548Z 3e261c1b63f9: Pulling fs layer 2025-07-17T08:35:57.9315880Z 6525f0fe3e66: Pulling fs layer 2025-07-17T08:35:57.9316105Z 52243ee284ea: Pulling fs layer 2025-07-17T08:35:57.9316346Z 01fd0a5c4393: Pulling fs layer 2025-07-17T08:35:57.9316561Z 9fbeb7a0b287: Pulling fs layer 2025-07-17T08:35:57.9316822Z 9a4035c74e1a: Pulling fs layer 2025-07-17T08:35:57.9317015Z ea39ccd54b27: Pulling fs layer 2025-07-17T08:35:57.9317205Z 73138d519c29: Pulling fs layer 2025-07-17T08:35:57.9317388Z 26819d564d7a: Pulling fs layer 2025-07-17T08:35:57.9317588Z 5f668547ed0d: Pulling fs layer 2025-07-17T08:35:57.9317777Z f8910737f31e: Pulling fs layer 2025-07-17T08:35:57.9317976Z 2548b702883d: Pulling fs layer 2025-07-17T08:35:57.9318170Z f949a24ea148: Pulling fs layer 2025-07-17T08:35:57.9318359Z 88bca81852d3: Pulling fs layer 2025-07-17T08:35:57.9318564Z 9c1dbde76e68: Pulling fs layer 2025-07-17T08:35:57.9318764Z 1a45a3124fac: Pulling fs layer 2025-07-17T08:35:57.9319360Z 44222d017546: Pulling fs layer 2025-07-17T08:35:57.9319593Z 3de0bbb94b90: Pulling fs layer 2025-07-17T08:35:57.9319779Z 396eca0560d4: Pulling fs layer 2025-07-17T08:35:57.9319958Z f7e0e33f00d7: Pulling fs layer 2025-07-17T08:35:57.9320143Z ff78e5c67765: Pulling fs layer 2025-07-17T08:35:57.9320337Z 103eb6a3b885: Pulling fs layer 2025-07-17T08:35:57.9320547Z b28bda57e36a: Pulling fs layer 2025-07-17T08:35:57.9320733Z 73138d519c29: Waiting 2025-07-17T08:35:57.9320925Z fd7bdb75a022: Pulling fs layer 2025-07-17T08:35:57.9321129Z 2baf05e68c26: Pulling fs layer 2025-07-17T08:35:57.9321327Z 2548b702883d: Waiting 2025-07-17T08:35:57.9321503Z c8d437fa52e4: Pulling fs layer 2025-07-17T08:35:57.9321686Z f949a24ea148: Waiting 2025-07-17T08:35:57.9321848Z e7f0c42a4150: Pulling fs layer 2025-07-17T08:35:57.9322029Z 01fd0a5c4393: Waiting 2025-07-17T08:35:57.9322204Z 5f668547ed0d: Waiting 2025-07-17T08:35:57.9322366Z f8910737f31e: Waiting 2025-07-17T08:35:57.9322528Z 9fbeb7a0b287: Waiting 2025-07-17T08:35:57.9322703Z 0459eb37c565: Pulling fs layer 2025-07-17T08:35:57.9322880Z 54bf6ee2f94c: Pulling fs layer 2025-07-17T08:35:57.9323069Z 7e4f3e71a58d: Pulling fs layer 2025-07-17T08:35:57.9323259Z 11d68569165e: Pulling fs layer 2025-07-17T08:35:57.9323441Z 9a4035c74e1a: Waiting 2025-07-17T08:35:57.9323596Z ea39ccd54b27: Waiting 2025-07-17T08:35:57.9323760Z 396eca0560d4: Waiting 2025-07-17T08:35:57.9324146Z 703d327e6498: Pulling fs layer 2025-07-17T08:35:57.9324332Z e73111c3b4ee: Pulling fs layer 2025-07-17T08:35:57.9324517Z de0a219b38cf: Pulling fs layer 2025-07-17T08:35:57.9324772Z 9c1dbde76e68: Waiting 2025-07-17T08:35:57.9324935Z 88bca81852d3: Waiting 2025-07-17T08:35:57.9325100Z 1a45a3124fac: Waiting 2025-07-17T08:35:57.9325269Z ff5883ff2486: Pulling fs layer 2025-07-17T08:35:57.9325453Z 2cb94b4ea22d: Pulling fs layer 2025-07-17T08:35:57.9325638Z 85520474c47b: Pulling fs layer 2025-07-17T08:35:57.9325818Z 4995dd45751b: Pulling fs layer 2025-07-17T08:35:57.9325985Z 103eb6a3b885: Waiting 2025-07-17T08:35:57.9326159Z 05db2817f254: Pulling fs layer 2025-07-17T08:35:57.9326341Z ff5883ff2486: Waiting 2025-07-17T08:35:57.9326513Z 07e5f9cd2b10: Pulling fs layer 2025-07-17T08:35:57.9326766Z 0459eb37c565: Waiting 2025-07-17T08:35:57.9326926Z 54bf6ee2f94c: Waiting 2025-07-17T08:35:57.9327096Z 21dab22df0ec: Pulling fs layer 2025-07-17T08:35:57.9327272Z 11d68569165e: Waiting 2025-07-17T08:35:57.9327446Z cd89850e2c44: Pulling fs layer 2025-07-17T08:35:57.9327625Z e7f0c42a4150: Waiting 2025-07-17T08:35:57.9327787Z 7290f4ad3db1: Pulling fs layer 2025-07-17T08:35:57.9327974Z ee114be1a250: Pulling fs layer 2025-07-17T08:35:57.9328167Z 88d77fb451a4: Pulling fs layer 2025-07-17T08:35:57.9328348Z e73111c3b4ee: Waiting 2025-07-17T08:35:57.9328516Z eb98dfa00416: Pulling fs layer 2025-07-17T08:35:57.9328701Z 0971ac0664a4: Pulling fs layer 2025-07-17T08:35:57.9328891Z 9b9239f68e90: Pulling fs layer 2025-07-17T08:35:57.9329071Z 4d3441d45503: Pulling fs layer 2025-07-17T08:35:57.9329248Z 2cb94b4ea22d: Waiting 2025-07-17T08:35:57.9329417Z 4f4fb700ef54: Pulling fs layer 2025-07-17T08:35:57.9329592Z 85520474c47b: Waiting 2025-07-17T08:35:57.9329743Z 7e4f3e71a58d: Waiting 2025-07-17T08:35:57.9329904Z 4995dd45751b: Waiting 2025-07-17T08:35:57.9330076Z bc1ff02b1df0: Pulling fs layer 2025-07-17T08:35:57.9330254Z 21dab22df0ec: Waiting 2025-07-17T08:35:57.9330418Z e20c94532338: Pulling fs layer 2025-07-17T08:35:57.9330599Z cd89850e2c44: Waiting 2025-07-17T08:35:57.9330760Z 703d327e6498: Waiting 2025-07-17T08:35:57.9330922Z ee114be1a250: Waiting 2025-07-17T08:35:57.9331077Z 88d77fb451a4: Waiting 2025-07-17T08:35:57.9331236Z 0971ac0664a4: Waiting 2025-07-17T08:35:57.9331384Z de0a219b38cf: Waiting 2025-07-17T08:35:57.9331545Z bc1ff02b1df0: Waiting 2025-07-17T08:35:57.9331707Z 4d3441d45503: Waiting 2025-07-17T08:35:57.9331872Z 7290f4ad3db1: Waiting 2025-07-17T08:35:57.9332037Z eb98dfa00416: Waiting 2025-07-17T08:35:57.9332201Z e20c94532338: Waiting 2025-07-17T08:35:57.9332356Z 44222d017546: Waiting 2025-07-17T08:35:57.9332515Z 05db2817f254: Waiting 2025-07-17T08:35:57.9332795Z 9b9239f68e90: Waiting 2025-07-17T08:35:58.1742930Z 3e261c1b63f9: Verifying Checksum 2025-07-17T08:35:58.1743267Z 3e261c1b63f9: Download complete 2025-07-17T08:35:58.4143635Z 52243ee284ea: Verifying Checksum 2025-07-17T08:35:58.4143950Z 52243ee284ea: Download complete 2025-07-17T08:35:58.5284241Z e87500e69896: Verifying Checksum 2025-07-17T08:35:58.5284633Z e87500e69896: Download complete 2025-07-17T08:35:58.6816523Z 01fd0a5c4393: Verifying Checksum 2025-07-17T08:35:58.6816861Z 01fd0a5c4393: Download complete 2025-07-17T08:35:58.7735146Z 9fbeb7a0b287: Verifying Checksum 2025-07-17T08:35:58.7735476Z 9fbeb7a0b287: Download complete 2025-07-17T08:35:59.7077140Z e87500e69896: Pull complete 2025-07-17T08:35:59.8685331Z 3e261c1b63f9: Pull complete 2025-07-17T08:36:00.1270802Z 9a4035c74e1a: Verifying Checksum 2025-07-17T08:36:00.1271133Z 9a4035c74e1a: Download complete 2025-07-17T08:36:01.0173394Z 73138d519c29: Download complete 2025-07-17T08:36:01.0234584Z ea39ccd54b27: Verifying Checksum 2025-07-17T08:36:01.0234888Z ea39ccd54b27: Download complete 2025-07-17T08:36:01.2620652Z 26819d564d7a: Verifying Checksum 2025-07-17T08:36:01.2620959Z 26819d564d7a: Download complete 2025-07-17T08:36:01.5054469Z f8910737f31e: Verifying Checksum 2025-07-17T08:36:01.5054779Z f8910737f31e: Download complete 2025-07-17T08:36:01.5521263Z 6525f0fe3e66: Verifying Checksum 2025-07-17T08:36:01.5522250Z 6525f0fe3e66: Download complete 2025-07-17T08:36:01.9270705Z f949a24ea148: Verifying Checksum 2025-07-17T08:36:01.9271014Z f949a24ea148: Download complete 2025-07-17T08:36:02.2068614Z 88bca81852d3: Verifying Checksum 2025-07-17T08:36:02.2068940Z 88bca81852d3: Download complete 2025-07-17T08:36:02.4054678Z 2548b702883d: Verifying Checksum 2025-07-17T08:36:02.4055009Z 2548b702883d: Download complete 2025-07-17T08:36:02.4889157Z 9c1dbde76e68: Verifying Checksum 2025-07-17T08:36:02.4889484Z 9c1dbde76e68: Download complete 2025-07-17T08:36:02.7278168Z 44222d017546: Verifying Checksum 2025-07-17T08:36:02.7278529Z 44222d017546: Download complete 2025-07-17T08:36:02.9759361Z 3de0bbb94b90: Verifying Checksum 2025-07-17T08:36:02.9759666Z 3de0bbb94b90: Download complete 2025-07-17T08:36:08.0395271Z 1a45a3124fac: Verifying Checksum 2025-07-17T08:36:08.0395687Z 1a45a3124fac: Download complete 2025-07-17T08:36:08.3009595Z f7e0e33f00d7: Verifying Checksum 2025-07-17T08:36:08.3009965Z f7e0e33f00d7: Download complete 2025-07-17T08:36:08.5635325Z ff78e5c67765: Verifying Checksum 2025-07-17T08:36:08.5635639Z ff78e5c67765: Download complete 2025-07-17T08:36:10.6881835Z 6525f0fe3e66: Pull complete 2025-07-17T08:36:10.8572926Z 52243ee284ea: Pull complete 2025-07-17T08:36:11.0349860Z 01fd0a5c4393: Pull complete 2025-07-17T08:36:11.1900033Z 9fbeb7a0b287: Pull complete 2025-07-17T08:36:13.5203711Z 9a4035c74e1a: Pull complete 2025-07-17T08:36:13.6946653Z ea39ccd54b27: Pull complete 2025-07-17T08:36:13.8371511Z 73138d519c29: Pull complete 2025-07-17T08:36:13.9826971Z 26819d564d7a: Pull complete 2025-07-17T08:36:15.6582736Z 103eb6a3b885: Verifying Checksum 2025-07-17T08:36:15.6583081Z 103eb6a3b885: Download complete 2025-07-17T08:36:16.0791851Z b28bda57e36a: Verifying Checksum 2025-07-17T08:36:16.0792188Z b28bda57e36a: Download complete 2025-07-17T08:36:16.3315483Z fd7bdb75a022: Verifying Checksum 2025-07-17T08:36:16.3315777Z fd7bdb75a022: Download complete 2025-07-17T08:36:16.5626918Z 2baf05e68c26: Verifying Checksum 2025-07-17T08:36:16.5627267Z 2baf05e68c26: Download complete 2025-07-17T08:36:16.8273752Z c8d437fa52e4: Verifying Checksum 2025-07-17T08:36:16.8274262Z c8d437fa52e4: Download complete 2025-07-17T08:36:17.2733218Z e7f0c42a4150: Verifying Checksum 2025-07-17T08:36:17.2733544Z e7f0c42a4150: Download complete 2025-07-17T08:36:17.5242726Z 0459eb37c565: Verifying Checksum 2025-07-17T08:36:17.5243049Z 0459eb37c565: Download complete 2025-07-17T08:36:17.7783104Z 54bf6ee2f94c: Verifying Checksum 2025-07-17T08:36:17.7783419Z 54bf6ee2f94c: Download complete 2025-07-17T08:36:18.3820139Z 7e4f3e71a58d: Verifying Checksum 2025-07-17T08:36:18.3821142Z 7e4f3e71a58d: Download complete 2025-07-17T08:36:18.6503880Z 11d68569165e: Download complete 2025-07-17T08:36:23.8941419Z 703d327e6498: Verifying Checksum 2025-07-17T08:36:23.8941744Z 703d327e6498: Download complete 2025-07-17T08:36:24.4284227Z e73111c3b4ee: Verifying Checksum 2025-07-17T08:36:24.4284546Z e73111c3b4ee: Download complete 2025-07-17T08:36:24.6764935Z de0a219b38cf: Verifying Checksum 2025-07-17T08:36:24.6765302Z de0a219b38cf: Download complete 2025-07-17T08:36:24.9064105Z ff5883ff2486: Download complete 2025-07-17T08:36:25.1270839Z 2cb94b4ea22d: Verifying Checksum 2025-07-17T08:36:25.1271157Z 2cb94b4ea22d: Download complete 2025-07-17T08:36:36.8073568Z 5f668547ed0d: Verifying Checksum 2025-07-17T08:36:36.8073871Z 5f668547ed0d: Download complete 2025-07-17T08:36:38.0858509Z 4995dd45751b: Verifying Checksum 2025-07-17T08:36:38.0858815Z 4995dd45751b: Download complete 2025-07-17T08:36:38.3275179Z 05db2817f254: Verifying Checksum 2025-07-17T08:36:38.3275457Z 05db2817f254: Download complete 2025-07-17T08:36:38.5931952Z 07e5f9cd2b10: Download complete 2025-07-17T08:36:38.8460368Z 21dab22df0ec: Verifying Checksum 2025-07-17T08:36:38.8460670Z 21dab22df0ec: Download complete 2025-07-17T08:36:39.0638256Z cd89850e2c44: Verifying Checksum 2025-07-17T08:36:39.0638562Z cd89850e2c44: Download complete 2025-07-17T08:36:39.2928581Z 7290f4ad3db1: Verifying Checksum 2025-07-17T08:36:39.2929694Z 7290f4ad3db1: Download complete 2025-07-17T08:36:39.5260896Z ee114be1a250: Verifying Checksum 2025-07-17T08:36:39.5261207Z ee114be1a250: Download complete 2025-07-17T08:37:24.9084066Z 5f668547ed0d: Pull complete 2025-07-17T08:37:25.1708134Z f8910737f31e: Pull complete 2025-07-17T08:37:26.3384899Z 2548b702883d: Pull complete 2025-07-17T08:37:26.5153191Z f949a24ea148: Pull complete 2025-07-17T08:37:26.6632009Z 88bca81852d3: Pull complete 2025-07-17T08:37:26.8015415Z 9c1dbde76e68: Pull complete 2025-07-17T08:37:32.7957293Z 1a45a3124fac: Pull complete 2025-07-17T08:37:32.9835862Z 44222d017546: Pull complete 2025-07-17T08:37:33.1543349Z 3de0bbb94b90: Pull complete 2025-07-17T08:38:00.6141568Z 85520474c47b: Verifying Checksum 2025-07-17T08:38:00.6141871Z 85520474c47b: Download complete 2025-07-17T08:38:00.8611220Z eb98dfa00416: Download complete 2025-07-17T08:38:01.0912574Z 0971ac0664a4: Verifying Checksum 2025-07-17T08:38:01.0912880Z 0971ac0664a4: Download complete 2025-07-17T08:38:01.1236141Z 396eca0560d4: Verifying Checksum 2025-07-17T08:38:01.1239285Z 396eca0560d4: Download complete 2025-07-17T08:38:01.3677425Z 4d3441d45503: Verifying Checksum 2025-07-17T08:38:01.3677728Z 4d3441d45503: Download complete 2025-07-17T08:38:01.4697431Z 4f4fb700ef54: Verifying Checksum 2025-07-17T08:38:01.4697768Z 4f4fb700ef54: Download complete 2025-07-17T08:38:01.7101110Z bc1ff02b1df0: Verifying Checksum 2025-07-17T08:38:01.7101454Z bc1ff02b1df0: Download complete 2025-07-17T08:38:02.4903857Z e20c94532338: Verifying Checksum 2025-07-17T08:38:02.4904190Z e20c94532338: Download complete 2025-07-17T08:38:02.9513857Z 9b9239f68e90: Verifying Checksum 2025-07-17T08:38:02.9514237Z 9b9239f68e90: Download complete 2025-07-17T08:38:27.5037394Z 88d77fb451a4: Verifying Checksum 2025-07-17T08:38:27.5039850Z 88d77fb451a4: Download complete 2025-07-17T08:39:11.2608882Z 396eca0560d4: Pull complete 2025-07-17T08:39:11.8796463Z f7e0e33f00d7: Pull complete 2025-07-17T08:39:12.0526183Z ff78e5c67765: Pull complete 2025-07-17T08:39:18.4047180Z 103eb6a3b885: Pull complete 2025-07-17T08:39:18.6033563Z b28bda57e36a: Pull complete 2025-07-17T08:39:18.7603653Z fd7bdb75a022: Pull complete 2025-07-17T08:39:18.9034580Z 2baf05e68c26: Pull complete 2025-07-17T08:39:19.0640920Z c8d437fa52e4: Pull complete 2025-07-17T08:39:19.2546209Z e7f0c42a4150: Pull complete 2025-07-17T08:39:19.4008988Z 0459eb37c565: Pull complete 2025-07-17T08:39:19.5397835Z 54bf6ee2f94c: Pull complete 2025-07-17T08:39:19.9748663Z 7e4f3e71a58d: Pull complete 2025-07-17T08:39:20.1336129Z 11d68569165e: Pull complete 2025-07-17T08:39:20.2721576Z 703d327e6498: Pull complete 2025-07-17T08:39:20.6055209Z e73111c3b4ee: Pull complete 2025-07-17T08:39:20.7816880Z de0a219b38cf: Pull complete 2025-07-17T08:39:21.1516844Z ff5883ff2486: Pull complete 2025-07-17T08:39:21.2987623Z 2cb94b4ea22d: Pull complete 2025-07-17T08:39:44.4736015Z 85520474c47b: Pull complete 2025-07-17T08:39:44.6866710Z 4995dd45751b: Pull complete 2025-07-17T08:39:44.8783576Z 05db2817f254: Pull complete 2025-07-17T08:39:45.0624606Z 07e5f9cd2b10: Pull complete 2025-07-17T08:39:45.2033973Z 21dab22df0ec: Pull complete 2025-07-17T08:39:45.3439394Z cd89850e2c44: Pull complete 2025-07-17T08:39:45.6799579Z 7290f4ad3db1: Pull complete 2025-07-17T08:39:45.8300567Z ee114be1a250: Pull complete 2025-07-17T08:40:35.4606514Z 88d77fb451a4: Pull complete 2025-07-17T08:40:35.8610945Z eb98dfa00416: Pull complete 2025-07-17T08:40:36.0388763Z 0971ac0664a4: Pull complete 2025-07-17T08:40:41.8153614Z 9b9239f68e90: Pull complete 2025-07-17T08:40:42.0010407Z 4d3441d45503: Pull complete 2025-07-17T08:40:42.1728213Z 4f4fb700ef54: Pull complete 2025-07-17T08:40:42.3138198Z bc1ff02b1df0: Pull complete 2025-07-17T08:40:43.6148375Z e20c94532338: Pull complete 2025-07-17T08:40:43.6212323Z Digest: sha256:7dce2798440bdcaa0daa2567baeca0695de58990a0e75ad9fa0ad65b9111a6d1 2025-07-17T08:40:43.6233310Z Status: Downloaded newer image for 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T08:40:43.6252313Z 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T08:40:43.6434709Z Prepare all required actions 2025-07-17T08:40:43.6469552Z ##[group]Run ./.github/actions/get-workflow-job-id 2025-07-17T08:40:43.6469805Z with: 2025-07-17T08:40:43.6470185Z github-token: *** 2025-07-17T08:40:43.6470362Z env: 2025-07-17T08:40:43.6470536Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:40:43.6470776Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T08:40:43.6471083Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T08:40:43.6471376Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T08:40:43.6472080Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T08:40:43.6472944Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T08:40:43.6473182Z AWS_REGION: us-east-1 2025-07-17T08:40:43.6473474Z AWS_ACCESS_KEY_ID: *** 2025-07-17T08:40:43.6473855Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T08:40:43.6478488Z AWS_SESSION_TOKEN: *** 2025-07-17T08:40:43.6478685Z ##[endgroup] 2025-07-17T08:40:43.6495862Z ##[group]Run set -eux 2025-07-17T08:40:43.6496069Z set -eux 2025-07-17T08:40:43.6496358Z python3 .github/scripts/get_workflow_job_id.py "${GITHUB_RUN_ID}" "${RUNNER_NAME}" 2025-07-17T08:40:43.6508699Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T08:40:43.6508967Z env: 2025-07-17T08:40:43.6509129Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:40:43.6509368Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T08:40:43.6509676Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T08:40:43.6509960Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T08:40:43.6510673Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T08:40:43.6511355Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T08:40:43.6511552Z AWS_REGION: us-east-1 2025-07-17T08:40:43.6511789Z AWS_ACCESS_KEY_ID: *** 2025-07-17T08:40:43.6512099Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T08:40:43.6517381Z AWS_SESSION_TOKEN: *** 2025-07-17T08:40:43.6517670Z GITHUB_TOKEN: *** 2025-07-17T08:40:43.6517839Z ##[endgroup] 2025-07-17T08:40:43.6551369Z + python3 .github/scripts/get_workflow_job_id.py 16337959895 linux.rocm.gpu.mi300.2-8zrv9-runner-r2mdd 2025-07-17T08:40:46.2351499Z Setting output job-id=46160759521 2025-07-17T08:40:46.2351994Z Setting output job-name=linux-noble-rocm-py3.12-mi300 / test (default, 1, 6, linux.rocm.gpu.mi300.2, unstable) 2025-07-17T08:40:46.2495871Z Prepare all required actions 2025-07-17T08:40:46.2496218Z Getting action download info 2025-07-17T08:40:46.7210590Z Download action repository 'seemethere/download-artifact-s3@v4' (SHA:1da556a7aa0a088e3153970611f6c432d58e80e6) 2025-07-17T08:40:50.1124509Z Download action repository 'actions/download-artifact@v4' (SHA:d3f86a106a0bac45b974a628896c90dbdf5c8093) 2025-07-17T08:40:50.8445321Z ##[group]Run ./.github/actions/download-build-artifacts 2025-07-17T08:40:50.8445596Z with: 2025-07-17T08:40:50.8445775Z name: linux-noble-rocm-py3.12-mi300 2025-07-17T08:40:50.8445995Z s3-bucket: gha-artifacts 2025-07-17T08:40:50.8446179Z env: 2025-07-17T08:40:50.8446339Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:40:50.8446568Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T08:40:50.8446870Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T08:40:50.8447149Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T08:40:50.8447878Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T08:40:50.8448851Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T08:40:50.8449044Z AWS_REGION: us-east-1 2025-07-17T08:40:50.8449319Z AWS_ACCESS_KEY_ID: *** 2025-07-17T08:40:50.8449666Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T08:40:50.8453641Z AWS_SESSION_TOKEN: *** 2025-07-17T08:40:50.8453839Z ##[endgroup] 2025-07-17T08:40:50.8481056Z ##[group]Run seemethere/download-artifact-s3@v4 2025-07-17T08:40:50.8481291Z with: 2025-07-17T08:40:50.8481469Z name: linux-noble-rocm-py3.12-mi300 2025-07-17T08:40:50.8481698Z s3-bucket: gha-artifacts 2025-07-17T08:40:50.8481892Z region: us-east-1 2025-07-17T08:40:50.8482070Z env: 2025-07-17T08:40:50.8482236Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:40:50.8482487Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T08:40:50.8482791Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T08:40:50.8483073Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T08:40:50.8483794Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T08:40:50.8484606Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T08:40:50.8484809Z AWS_REGION: us-east-1 2025-07-17T08:40:50.8485032Z AWS_ACCESS_KEY_ID: *** 2025-07-17T08:40:50.8485295Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T08:40:50.8489233Z AWS_SESSION_TOKEN: *** 2025-07-17T08:40:50.8489420Z ##[endgroup] 2025-07-17T08:40:51.2342593Z (node:5052) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2025-07-17T08:40:51.2342939Z 2025-07-17T08:40:51.2343148Z Please migrate your code to use AWS SDK for JavaScript (v3). 2025-07-17T08:40:51.2343535Z For more information, check the migration guide at https://a.co/7PzMCcy 2025-07-17T08:40:51.2343920Z (Use `node --trace-warnings ...` to show where the warning was created) 2025-07-17T08:40:51.5649951Z Found 1 objects with prefix pytorch/pytorch/16337959895/linux-noble-rocm-py3.12-mi300/ 2025-07-17T08:40:51.5650447Z Starting download (1/1): /home/runner/_work/pytorch/pytorch/artifacts.zip 2025-07-17T08:41:21.5355872Z Finished download (1/1): /home/runner/_work/pytorch/pytorch/artifacts.zip 2025-07-17T08:41:21.5360813Z Artifact download has finished successfully 2025-07-17T08:41:21.5624699Z ##[group]Run unzip -o artifacts.zip 2025-07-17T08:41:21.5624959Z unzip -o artifacts.zip 2025-07-17T08:41:21.5637303Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T08:41:21.5637579Z env: 2025-07-17T08:41:21.5637746Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:41:21.5637984Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T08:41:21.5638728Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T08:41:21.5639031Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T08:41:21.5639732Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T08:41:21.5640400Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T08:41:21.5640602Z AWS_REGION: us-east-1 2025-07-17T08:41:21.5640870Z AWS_ACCESS_KEY_ID: *** 2025-07-17T08:41:21.5641139Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T08:41:21.5645074Z AWS_SESSION_TOKEN: *** 2025-07-17T08:41:21.5645250Z ##[endgroup] 2025-07-17T08:41:21.5699078Z Archive: artifacts.zip 2025-07-17T08:41:21.5701483Z creating: dist/ 2025-07-17T08:41:26.8091895Z inflating: dist/torch-2.9.0a0+gita38f433-cp312-cp312-linux_x86_64.whl 2025-07-17T08:41:26.8207299Z inflating: dist/.ninja_log 2025-07-17T08:41:26.8208164Z creating: build/custom_test_artifacts/ 2025-07-17T08:41:26.8212581Z creating: build/custom_test_artifacts/custom-op-build/ 2025-07-17T08:41:26.8212955Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/ 2025-07-17T08:41:26.8213339Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/pkgRedirects/ 2025-07-17T08:41:26.8215141Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/CMakeConfigureLog.yaml 2025-07-17T08:41:26.8215773Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/4.0.0/ 2025-07-17T08:41:26.8217136Z inflating: 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build/bin/memory_overlapping_test 2025-07-17T08:41:33.0305925Z inflating: build/bin/operator_name_test 2025-07-17T08:41:33.0357164Z inflating: build/bin/operators_test 2025-07-17T08:41:33.0410922Z inflating: build/bin/native_test 2025-07-17T08:41:33.0501066Z inflating: build/bin/cpu_rng_test 2025-07-17T08:41:33.0551357Z inflating: build/bin/packedtensoraccessor_test 2025-07-17T08:41:33.0645514Z inflating: build/bin/ivalue_test 2025-07-17T08:41:33.0696657Z inflating: build/bin/mobile_memory_cleanup 2025-07-17T08:41:33.0753798Z inflating: build/bin/quantized_test 2025-07-17T08:41:33.0806006Z inflating: build/bin/reportMemoryUsage_test 2025-07-17T08:41:33.0856152Z inflating: build/bin/reduce_ops_test 2025-07-17T08:41:33.0920112Z inflating: build/bin/pow_test 2025-07-17T08:41:33.0976489Z inflating: build/bin/scalar_tensor_test 2025-07-17T08:41:33.1036772Z inflating: build/bin/scalar_test 2025-07-17T08:41:33.1088671Z inflating: build/bin/StorageUtils_test 2025-07-17T08:41:33.1138643Z inflating: build/bin/stride_properties_test 2025-07-17T08:41:33.1193685Z inflating: build/bin/type_ptr_test 2025-07-17T08:41:33.1248352Z inflating: build/bin/test_parallel 2025-07-17T08:41:33.1250234Z inflating: build/bin/verify_api_visibility 2025-07-17T08:41:33.1252626Z inflating: build/bin/thread_init_test 2025-07-17T08:41:33.1306048Z inflating: build/bin/undefined_tensor_test 2025-07-17T08:41:33.1381996Z inflating: build/bin/tensor_iterator_test 2025-07-17T08:41:33.1433999Z inflating: build/bin/weakref_test 2025-07-17T08:41:33.1483612Z inflating: build/bin/wrapdim_test 2025-07-17T08:41:33.1542753Z inflating: build/bin/IListRef_test 2025-07-17T08:41:33.1611108Z inflating: build/bin/legacy_vmap_test 2025-07-17T08:41:33.1663124Z inflating: build/bin/xla_tensor_test 2025-07-17T08:41:33.1722579Z inflating: build/bin/type_test 2025-07-17T08:41:33.1787632Z inflating: build/bin/KernelFunction_test 2025-07-17T08:41:33.1878222Z inflating: build/bin/kernel_function_test 2025-07-17T08:41:33.1980575Z inflating: build/bin/List_test 2025-07-17T08:41:33.2031952Z inflating: build/bin/CppSignature_test 2025-07-17T08:41:33.2089488Z inflating: build/bin/kernel_stackbased_test 2025-07-17T08:41:33.2204287Z inflating: build/bin/kernel_function_legacy_test 2025-07-17T08:41:33.2253921Z inflating: build/bin/op_allowlist_test 2025-07-17T08:41:33.2319505Z inflating: build/bin/inline_container_test 2025-07-17T08:41:33.2438378Z inflating: build/bin/kernel_lambda_legacy_test 2025-07-17T08:41:33.2489198Z inflating: build/bin/hip_apply_test 2025-07-17T08:41:33.2539076Z inflating: build/bin/hip_complex_math_test 2025-07-17T08:41:33.2589342Z inflating: build/bin/hip_complex_test 2025-07-17T08:41:33.2639307Z inflating: build/bin/hip_distributions_test 2025-07-17T08:41:33.2686554Z inflating: build/bin/hip_generator_test 2025-07-17T08:41:33.2785075Z inflating: build/bin/kernel_lambda_test 2025-07-17T08:41:33.2840161Z inflating: build/bin/backend_fallback_test 2025-07-17T08:41:33.2887538Z inflating: build/bin/hip_half_test 2025-07-17T08:41:33.2937422Z inflating: build/bin/hip_integer_divider_test 2025-07-17T08:41:33.2987599Z inflating: build/bin/hip_optional_test 2025-07-17T08:41:33.3037608Z inflating: build/bin/hip_packedtensoraccessor_test 2025-07-17T08:41:33.3085258Z inflating: build/bin/hip_vectorized_test 2025-07-17T08:41:33.3178028Z inflating: build/bin/make_boxed_from_unboxed_functor_test 2025-07-17T08:41:33.3230201Z inflating: build/bin/hip_dlconvertor_test 2025-07-17T08:41:33.3514600Z inflating: build/bin/op_registration_test 2025-07-17T08:41:33.4515813Z inflating: build/bin/test_jit 2025-07-17T08:41:33.4772653Z inflating: build/bin/test_nativert 2025-07-17T08:41:33.4786558Z inflating: build/bin/tutorial_tensorexpr 2025-07-17T08:41:33.4839230Z inflating: build/bin/test_dist_autograd 2025-07-17T08:41:33.4905244Z inflating: build/bin/test_cpp_rpc 2025-07-17T08:41:33.5623392Z inflating: build/bin/test_tensorexpr 2025-07-17T08:41:33.5625586Z inflating: build/bin/parallel_benchmark 2025-07-17T08:41:33.6670836Z inflating: build/bin/test_api 2025-07-17T08:41:33.6736165Z inflating: build/bin/test_mobile_nnc 2025-07-17T08:41:33.6744129Z inflating: build/bin/aot_model_compiler_test 2025-07-17T08:41:33.7061167Z inflating: build/bin/test_lazy 2025-07-17T08:41:33.7061466Z creating: .additional_ci_files/ 2025-07-17T08:41:33.7209284Z inflating: .additional_ci_files/test-times.json 2025-07-17T08:41:33.7765736Z inflating: .additional_ci_files/test-class-times.json 2025-07-17T08:41:33.7812334Z ##[group]Run rm artifacts.zip 2025-07-17T08:41:33.7812589Z rm artifacts.zip 2025-07-17T08:41:33.7824738Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T08:41:33.7825001Z env: 2025-07-17T08:41:33.7825156Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:41:33.7825525Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T08:41:33.7825820Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T08:41:33.7826091Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T08:41:33.7826784Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T08:41:33.7827464Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T08:41:33.7827660Z AWS_REGION: us-east-1 2025-07-17T08:41:33.7827924Z AWS_ACCESS_KEY_ID: *** 2025-07-17T08:41:33.7828180Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T08:41:33.7832357Z AWS_SESSION_TOKEN: *** 2025-07-17T08:41:33.7832531Z ##[endgroup] 2025-07-17T08:41:33.8947423Z ##[group]Run df -H 2025-07-17T08:41:33.8947630Z df -H 2025-07-17T08:41:33.8959669Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T08:41:33.8959923Z env: 2025-07-17T08:41:33.8960082Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:41:33.8960309Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T08:41:33.8960601Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T08:41:33.8960870Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T08:41:33.8961572Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T08:41:33.8962264Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T08:41:33.8962467Z AWS_REGION: us-east-1 2025-07-17T08:41:33.8962746Z AWS_ACCESS_KEY_ID: *** 2025-07-17T08:41:33.8963010Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T08:41:33.8966980Z AWS_SESSION_TOKEN: *** 2025-07-17T08:41:33.8967158Z ##[endgroup] 2025-07-17T08:41:33.9014245Z Filesystem Size Used Avail Use% Mounted on 2025-07-17T08:41:33.9014559Z overlay 900G 170G 692G 20% / 2025-07-17T08:41:33.9014800Z tmpfs 68M 0 68M 0% /dev 2025-07-17T08:41:33.9015025Z nvme 16T 14G 16T 1% /run 2025-07-17T08:41:33.9015266Z /dev/nvme1n1p2 900G 170G 692G 20% /etc/hostname 2025-07-17T08:41:33.9015514Z shm 68M 8.2k 68M 1% /dev/shm 2025-07-17T08:41:33.9015789Z tmpfs 2.5T 13k 2.5T 1% /run/secrets/kubernetes.io/serviceaccount 2025-07-17T08:41:33.9016083Z tmpfs 1.3T 0 1.3T 0% /proc/acpi 2025-07-17T08:41:33.9016310Z tmpfs 1.3T 0 1.3T 0% /proc/scsi 2025-07-17T08:41:33.9016567Z tmpfs 1.3T 0 1.3T 0% /sys/firmware 2025-07-17T08:41:33.9016873Z tmpfs 1.3T 0 1.3T 0% /sys/devices/virtual/powercap 2025-07-17T08:41:33.9049881Z Prepare all required actions 2025-07-17T08:41:33.9050196Z Getting action download info 2025-07-17T08:41:34.0916614Z ##[group]Run ./.github/actions/download-td-artifacts 2025-07-17T08:41:34.0916865Z with: 2025-07-17T08:41:34.0917020Z env: 2025-07-17T08:41:34.0917184Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:41:34.0917421Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T08:41:34.0917723Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T08:41:34.0918003Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T08:41:34.0918708Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T08:41:34.0919378Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T08:41:34.0919606Z AWS_REGION: us-east-1 2025-07-17T08:41:34.0919892Z AWS_ACCESS_KEY_ID: *** 2025-07-17T08:41:34.0920160Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T08:41:34.0924066Z AWS_SESSION_TOKEN: *** 2025-07-17T08:41:34.0924252Z ##[endgroup] 2025-07-17T08:41:34.0952522Z ##[group]Run seemethere/download-artifact-s3@v4 2025-07-17T08:41:34.0952762Z with: 2025-07-17T08:41:34.0952926Z name: td_results 2025-07-17T08:41:34.0953099Z s3-bucket: gha-artifacts 2025-07-17T08:41:34.0953281Z region: us-east-1 2025-07-17T08:41:34.0953438Z env: 2025-07-17T08:41:34.0953607Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:41:34.0953841Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T08:41:34.0954147Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T08:41:34.0954431Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T08:41:34.0955148Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T08:41:34.0956073Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T08:41:34.0956273Z AWS_REGION: us-east-1 2025-07-17T08:41:34.0956494Z AWS_ACCESS_KEY_ID: *** 2025-07-17T08:41:34.0956755Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T08:41:34.0960812Z AWS_SESSION_TOKEN: *** 2025-07-17T08:41:34.0961001Z ##[endgroup] 2025-07-17T08:41:34.5006597Z (node:5086) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2025-07-17T08:41:34.5006979Z 2025-07-17T08:41:34.5007121Z Please migrate your code to use AWS SDK for JavaScript (v3). 2025-07-17T08:41:34.5007493Z For more information, check the migration guide at https://a.co/7PzMCcy 2025-07-17T08:41:34.5007858Z (Use `node --trace-warnings ...` to show where the warning was created) 2025-07-17T08:41:34.6339659Z Found 1 objects with prefix pytorch/pytorch/16337959895/td_results/ 2025-07-17T08:41:34.6340200Z Starting download (1/1): /home/runner/_work/pytorch/pytorch/td_results.json 2025-07-17T08:41:34.8113672Z Finished download (1/1): /home/runner/_work/pytorch/pytorch/td_results.json 2025-07-17T08:41:34.8118253Z Artifact download has finished successfully 2025-07-17T08:41:34.8400108Z ##[group]Run mkdir -p .additional_ci_files 2025-07-17T08:41:34.8400381Z mkdir -p .additional_ci_files 2025-07-17T08:41:34.8400666Z mv td_results.json .additional_ci_files/td_results.json || true 2025-07-17T08:41:34.8412594Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T08:41:34.8412866Z env: 2025-07-17T08:41:34.8413022Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:41:34.8413262Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T08:41:34.8413566Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T08:41:34.8413871Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T08:41:34.8414900Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T08:41:34.8415615Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T08:41:34.8415823Z AWS_REGION: us-east-1 2025-07-17T08:41:34.8416083Z AWS_ACCESS_KEY_ID: *** 2025-07-17T08:41:34.8416341Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T08:41:34.8420261Z AWS_SESSION_TOKEN: *** 2025-07-17T08:41:34.8420431Z ##[endgroup] 2025-07-17T08:41:34.8533649Z ##[group]Run .github/scripts/parse_ref.py 2025-07-17T08:41:34.8533917Z .github/scripts/parse_ref.py 2025-07-17T08:41:34.8548899Z shell: /usr/bin/bash -e {0} 2025-07-17T08:41:34.8549104Z env: 2025-07-17T08:41:34.8549264Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:41:34.8549494Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T08:41:34.8549797Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T08:41:34.8550099Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T08:41:34.8550801Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T08:41:34.8551464Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T08:41:34.8551662Z AWS_REGION: us-east-1 2025-07-17T08:41:34.8551912Z AWS_ACCESS_KEY_ID: *** 2025-07-17T08:41:34.8552194Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T08:41:34.8556170Z AWS_SESSION_TOKEN: *** 2025-07-17T08:41:34.8556366Z ##[endgroup] 2025-07-17T08:41:34.8716756Z Setting output branch=main 2025-07-17T08:41:34.8832021Z Prepare all required actions 2025-07-17T08:41:34.8832348Z Getting action download info 2025-07-17T08:41:35.0225503Z ##[group]Run ./.github/actions/filter-test-configs 2025-07-17T08:41:35.0225775Z with: 2025-07-17T08:41:35.0226108Z github-token: *** 2025-07-17T08:41:35.0227587Z test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 6, "runner": "linux.rocm.gpu.mi300.2", "unstable": "unstable"}, {"config": "default", "shard": 2, "num_shards": 6, "runner": "linux.rocm.gpu.mi300.2", "unstable": "unstable"}, {"config": "default", "shard": 3, "num_shards": 6, "runner": "linux.rocm.gpu.mi300.2", "unstable": "unstable"}, {"config": "default", "shard": 4, "num_shards": 6, "runner": "linux.rocm.gpu.mi300.2", "unstable": "unstable"}, {"config": "default", "shard": 5, "num_shards": 6, "runner": "linux.rocm.gpu.mi300.2", "unstable": "unstable"}, {"config": "default", "shard": 6, "num_shards": 6, "runner": "linux.rocm.gpu.mi300.2", "unstable": "unstable"}]} 2025-07-17T08:41:35.0229504Z job-name: linux-noble-rocm-py3.12-mi300 / test (default, 1, 6, linux.rocm.gpu.mi300.2, unstable) 2025-07-17T08:41:35.0229836Z env: 2025-07-17T08:41:35.0229997Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:41:35.0230234Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T08:41:35.0230536Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T08:41:35.0230824Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T08:41:35.0231545Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T08:41:35.0232222Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T08:41:35.0232423Z AWS_REGION: us-east-1 2025-07-17T08:41:35.0232660Z AWS_ACCESS_KEY_ID: *** 2025-07-17T08:41:35.0232967Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T08:41:35.0236907Z AWS_SESSION_TOKEN: *** 2025-07-17T08:41:35.0237096Z ##[endgroup] 2025-07-17T08:41:35.0270667Z ##[group]Run nick-fields/retry@v3.0.0 2025-07-17T08:41:35.0270887Z with: 2025-07-17T08:41:35.0271037Z shell: bash 2025-07-17T08:41:35.0271207Z timeout_minutes: 10 2025-07-17T08:41:35.0271371Z max_attempts: 5 2025-07-17T08:41:35.0271547Z retry_wait_seconds: 30 2025-07-17T08:41:35.0272041Z 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 2025-07-17T08:41:35.0272538Z polling_interval_seconds: 1 2025-07-17T08:41:35.0272739Z warning_on_retry: true 2025-07-17T08:41:35.0272915Z continue_on_error: false 2025-07-17T08:41:35.0273091Z env: 2025-07-17T08:41:35.0273249Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:41:35.0273478Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T08:41:35.0273782Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T08:41:35.0274064Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T08:41:35.0274761Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T08:41:35.0275433Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T08:41:35.0275635Z AWS_REGION: us-east-1 2025-07-17T08:41:35.0275853Z AWS_ACCESS_KEY_ID: *** 2025-07-17T08:41:35.0276112Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T08:41:35.0280053Z AWS_SESSION_TOKEN: *** 2025-07-17T08:41:35.0280318Z GITHUB_TOKEN: *** 2025-07-17T08:41:35.0280495Z ##[endgroup] 2025-07-17T08:41:35.0905178Z + python3 -m pip install requests==2.27.1 pyyaml==6.0.1 2025-07-17T08:41:35.3249739Z Defaulting to user installation because normal site-packages is not writeable 2025-07-17T08:41:37.6718292Z Collecting requests==2.27.1 2025-07-17T08:41:37.7110971Z Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB) 2025-07-17T08:41:37.7242795Z ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 63.1/63.1 KB 4.7 MB/s eta 0:00:00 2025-07-17T08:41:37.7847292Z Collecting pyyaml==6.0.1 2025-07-17T08:41:37.7897563Z Downloading PyYAML-6.0.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (705 kB) 2025-07-17T08:41:37.8300061Z ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 705.5/705.5 KB 18.2 MB/s eta 0:00:00 2025-07-17T08:41:37.8632070Z Collecting certifi>=2017.4.17 2025-07-17T08:41:37.8684042Z Downloading certifi-2025.7.14-py3-none-any.whl (162 kB) 2025-07-17T08:41:37.8757097Z ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 162.7/162.7 KB 26.5 MB/s eta 0:00:00 2025-07-17T08:41:38.0016130Z Collecting charset-normalizer~=2.0.0 2025-07-17T08:41:38.0069732Z Downloading charset_normalizer-2.0.12-py3-none-any.whl (39 kB) 2025-07-17T08:41:38.0287582Z Collecting idna<4,>=2.5 2025-07-17T08:41:38.0335344Z Downloading idna-3.10-py3-none-any.whl (70 kB) 2025-07-17T08:41:38.0365896Z ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 70.4/70.4 KB 39.5 MB/s eta 0:00:00 2025-07-17T08:41:38.0764717Z Collecting urllib3<1.27,>=1.21.1 2025-07-17T08:41:38.0815235Z Downloading urllib3-1.26.20-py2.py3-none-any.whl (144 kB) 2025-07-17T08:41:38.0885703Z ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 144.2/144.2 KB 24.8 MB/s eta 0:00:00 2025-07-17T08:41:38.1620317Z Installing collected packages: urllib3, pyyaml, idna, charset-normalizer, certifi, requests 2025-07-17T08:41:38.3181245Z WARNING: The script normalizer is installed in '/home/runner/.local/bin' which is not on PATH. 2025-07-17T08:41:38.3181833Z Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. 2025-07-17T08:41:38.3465430Z Successfully installed certifi-2025.7.14 charset-normalizer-2.0.12 idna-3.10 pyyaml-6.0.1 requests-2.27.1 urllib3-1.26.20 2025-07-17T08:41:39.0938940Z Command completed after 1 attempt(s). 2025-07-17T08:41:39.1011493Z ##[group]Run set -x 2025-07-17T08:41:39.1011730Z set -x 2025-07-17T08:41:39.1011906Z  2025-07-17T08:41:39.1012171Z # Use relative path here as this could be checked out anywhere, not necessarily 2025-07-17T08:41:39.1012488Z # in runner workspace 2025-07-17T08:41:39.1012753Z python3 "${GITHUB_ACTION_PATH}/../../scripts/parse_ref.py" 2025-07-17T08:41:39.1024848Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T08:41:39.1025113Z env: 2025-07-17T08:41:39.1025418Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:41:39.1025657Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T08:41:39.1025960Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T08:41:39.1026247Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T08:41:39.1026958Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T08:41:39.1027619Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T08:41:39.1027820Z AWS_REGION: us-east-1 2025-07-17T08:41:39.1028071Z AWS_ACCESS_KEY_ID: *** 2025-07-17T08:41:39.1028340Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T08:41:39.1032263Z AWS_SESSION_TOKEN: *** 2025-07-17T08:41:39.1032456Z ##[endgroup] 2025-07-17T08:41:39.1068108Z + python3 /home/runner/_work/pytorch/pytorch/./.github/actions/filter-test-configs/../../scripts/parse_ref.py 2025-07-17T08:41:39.1197129Z Setting output branch=main 2025-07-17T08:41:39.1249154Z ##[group]Run echo "Workflow: ${GITHUB_WORKFLOW}" 2025-07-17T08:41:39.1249466Z echo "Workflow: ${GITHUB_WORKFLOW}" 2025-07-17T08:41:39.1249703Z echo "Job name: ${JOB_NAME}" 2025-07-17T08:41:39.1249903Z  2025-07-17T08:41:39.1250165Z # Use relative path here as this could be checked out anywhere, not necessarily 2025-07-17T08:41:39.1250485Z # in runner workspace 2025-07-17T08:41:39.1251058Z python3 "${GITHUB_ACTION_PATH}/../../scripts/filter_test_configs.py" \ 2025-07-17T08:41:39.1251398Z  --workflow "${GITHUB_WORKFLOW}" \ 2025-07-17T08:41:39.1251628Z  --job-name "${JOB_NAME}" \ 2025-07-17T08:41:39.1252948Z  --test-matrix "{"include": [{"config": "default", "shard": 1, "num_shards": 6, "runner": "linux.rocm.gpu.mi300.2", "unstable": "unstable"}, {"config": "default", "shard": 2, "num_shards": 6, "runner": "linux.rocm.gpu.mi300.2", "unstable": "unstable"}, {"config": "default", "shard": 3, "num_shards": 6, "runner": "linux.rocm.gpu.mi300.2", "unstable": "unstable"}, {"config": "default", "shard": 4, "num_shards": 6, "runner": "linux.rocm.gpu.mi300.2", "unstable": "unstable"}, {"config": "default", "shard": 5, "num_shards": 6, "runner": "linux.rocm.gpu.mi300.2", "unstable": "unstable"}, {"config": "default", "shard": 6, "num_shards": 6, "runner": "linux.rocm.gpu.mi300.2", "unstable": "unstable"}]}" \ 2025-07-17T08:41:39.1254533Z  --selected-test-configs "" \ 2025-07-17T08:41:39.1254764Z  --pr-number "${PR_NUMBER}" \ 2025-07-17T08:41:39.1254969Z  --tag "${TAG}" \ 2025-07-17T08:41:39.1255178Z  --event-name "${EVENT_NAME}" \ 2025-07-17T08:41:39.1255419Z  --schedule "${SCHEDULE}" \ 2025-07-17T08:41:39.1255645Z  --branch "${HEAD_BRANCH}" 2025-07-17T08:41:39.1267364Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T08:41:39.1267635Z env: 2025-07-17T08:41:39.1267804Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:41:39.1268047Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T08:41:39.1268353Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T08:41:39.1268633Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T08:41:39.1269343Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T08:41:39.1270014Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T08:41:39.1270211Z AWS_REGION: us-east-1 2025-07-17T08:41:39.1270465Z AWS_ACCESS_KEY_ID: *** 2025-07-17T08:41:39.1270729Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T08:41:39.1274706Z AWS_SESSION_TOKEN: *** 2025-07-17T08:41:39.1275043Z GITHUB_TOKEN: *** 2025-07-17T08:41:39.1275360Z JOB_NAME: linux-noble-rocm-py3.12-mi300 / test (default, 1, 6, linux.rocm.gpu.mi300.2, unstable) 2025-07-17T08:41:39.1275687Z PR_NUMBER: 2025-07-17T08:41:39.1275855Z TAG: 2025-07-17T08:41:39.1276011Z EVENT_NAME: push 2025-07-17T08:41:39.1276184Z SCHEDULE: 2025-07-17T08:41:39.1276344Z HEAD_BRANCH: main 2025-07-17T08:41:39.1276510Z ##[endgroup] 2025-07-17T08:41:39.1314797Z Workflow: rocm-mi300 2025-07-17T08:41:39.1315192Z Job name: linux-noble-rocm-py3.12-mi300 / test (default, 1, 6, linux.rocm.gpu.mi300.2, unstable) 2025-07-17T08:41:39.3887421Z Setting output keep-going=True 2025-07-17T08:41:39.3887717Z Setting output ci-verbose-test-logs=False 2025-07-17T08:41:39.3887962Z Setting output ci-test-showlocals=False 2025-07-17T08:41:39.3888191Z Setting output ci-no-test-timeout=False 2025-07-17T08:41:39.3888406Z Setting output ci-no-td=False 2025-07-17T08:41:39.3888629Z Setting output ci-td-distributed=False 2025-07-17T08:41:39.3888843Z Setting output is-unstable=True 2025-07-17T08:41:39.3889042Z Setting output reenabled-issues= 2025-07-17T08:41:39.3890778Z Setting output test-matrix={"include": [{"config": "default", "shard": 1, "num_shards": 6, "runner": "linux.rocm.gpu.mi300.2", "unstable": "unstable"}, {"config": "default", "shard": 2, "num_shards": 6, "runner": "linux.rocm.gpu.mi300.2", "unstable": "unstable"}, {"config": "default", "shard": 3, "num_shards": 6, "runner": "linux.rocm.gpu.mi300.2", "unstable": "unstable"}, {"config": "default", "shard": 4, "num_shards": 6, "runner": "linux.rocm.gpu.mi300.2", "unstable": "unstable"}, {"config": "default", "shard": 5, "num_shards": 6, "runner": "linux.rocm.gpu.mi300.2", "unstable": "unstable"}, {"config": "default", "shard": 6, "num_shards": 6, "runner": "linux.rocm.gpu.mi300.2", "unstable": "unstable"}]} 2025-07-17T08:41:39.3892099Z Setting output is-test-matrix-empty=False 2025-07-17T08:41:39.4000006Z ##[group]Run echo "Filtered matrix:" 2025-07-17T08:41:39.4000526Z echo "Filtered matrix:" 2025-07-17T08:41:39.4001827Z echo "{"include": [{"config": "default", "shard": 1, "num_shards": 6, "runner": "linux.rocm.gpu.mi300.2", "unstable": "unstable"}, {"config": "default", "shard": 2, "num_shards": 6, "runner": "linux.rocm.gpu.mi300.2", "unstable": "unstable"}, {"config": "default", "shard": 3, "num_shards": 6, "runner": "linux.rocm.gpu.mi300.2", "unstable": "unstable"}, {"config": "default", "shard": 4, "num_shards": 6, "runner": "linux.rocm.gpu.mi300.2", "unstable": "unstable"}, {"config": "default", "shard": 5, "num_shards": 6, "runner": "linux.rocm.gpu.mi300.2", "unstable": "unstable"}, {"config": "default", "shard": 6, "num_shards": 6, "runner": "linux.rocm.gpu.mi300.2", "unstable": "unstable"}]}" 2025-07-17T08:41:39.4003099Z  2025-07-17T08:41:39.4003257Z echo 2025-07-17T08:41:39.4003447Z echo "Is the current job unstable? True" 2025-07-17T08:41:39.4003674Z  2025-07-17T08:41:39.4003828Z echo 2025-07-17T08:41:39.4004014Z echo "Is keep-going label set? True" 2025-07-17T08:41:39.4004229Z  2025-07-17T08:41:39.4018069Z echo 2025-07-17T08:41:39.4018287Z echo "Reenabled issues? " 2025-07-17T08:41:39.4030320Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T08:41:39.4030598Z env: 2025-07-17T08:41:39.4030758Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:41:39.4031008Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T08:41:39.4031311Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T08:41:39.4031598Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T08:41:39.4032301Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T08:41:39.4032963Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T08:41:39.4033154Z AWS_REGION: us-east-1 2025-07-17T08:41:39.4033400Z AWS_ACCESS_KEY_ID: *** 2025-07-17T08:41:39.4033651Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T08:41:39.4037608Z AWS_SESSION_TOKEN: *** 2025-07-17T08:41:39.4037796Z ##[endgroup] 2025-07-17T08:41:39.4070408Z Filtered matrix: 2025-07-17T08:41:39.4071796Z {include: [{config: default, shard: 1, num_shards: 6, runner: linux.rocm.gpu.mi300.2, unstable: unstable}, {config: default, shard: 2, num_shards: 6, runner: linux.rocm.gpu.mi300.2, unstable: unstable}, {config: default, shard: 3, num_shards: 6, runner: linux.rocm.gpu.mi300.2, unstable: unstable}, {config: default, shard: 4, num_shards: 6, runner: linux.rocm.gpu.mi300.2, unstable: unstable}, {config: default, shard: 5, num_shards: 6, runner: linux.rocm.gpu.mi300.2, unstable: unstable}, {config: default, shard: 6, num_shards: 6, runner: linux.rocm.gpu.mi300.2, unstable: unstable}]} 2025-07-17T08:41:39.4073051Z 2025-07-17T08:41:39.4073154Z Is the current job unstable? True 2025-07-17T08:41:39.4073345Z 2025-07-17T08:41:39.4073449Z Is keep-going label set? True 2025-07-17T08:41:39.4073588Z 2025-07-17T08:41:39.4073667Z Reenabled issues? 2025-07-17T08:41:39.4115417Z ##[group]Run echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2025-07-17T08:41:39.4115761Z echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2025-07-17T08:41:39.4127903Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T08:41:39.4128165Z env: 2025-07-17T08:41:39.4128336Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:41:39.4128570Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T08:41:39.4128875Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T08:41:39.4129155Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T08:41:39.4129856Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T08:41:39.4130788Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T08:41:39.4131006Z AWS_REGION: us-east-1 2025-07-17T08:41:39.4131261Z AWS_ACCESS_KEY_ID: *** 2025-07-17T08:41:39.4131522Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T08:41:39.4135518Z AWS_SESSION_TOKEN: *** 2025-07-17T08:41:39.4135705Z JOB_TIMEOUT: 300 2025-07-17T08:41:39.4135870Z ##[endgroup] 2025-07-17T08:41:39.4220983Z ##[group]Run set -x 2025-07-17T08:41:39.4221226Z set -x 2025-07-17T08:41:39.4221389Z  2025-07-17T08:41:39.4221569Z if [[ $TEST_CONFIG == 'multigpu' ]]; then 2025-07-17T08:41:39.4221830Z  TEST_COMMAND=.ci/pytorch/multigpu-test.sh 2025-07-17T08:41:39.4222092Z elif [[ $BUILD_ENVIRONMENT == *onnx* ]]; then 2025-07-17T08:41:39.4222340Z  TEST_COMMAND=.ci/caffe2/test.sh 2025-07-17T08:41:39.4222550Z else 2025-07-17T08:41:39.4222729Z  TEST_COMMAND=.ci/pytorch/test.sh 2025-07-17T08:41:39.4222959Z fi 2025-07-17T08:41:39.4223110Z  2025-07-17T08:41:39.4223336Z # detached container should get cleaned up by teardown_ec2_linux 2025-07-17T08:41:39.4223678Z # TODO: Stop building test binaries as part of the build phase 2025-07-17T08:41:39.4223974Z # Used for GPU_FLAG since that doesn't play nice 2025-07-17T08:41:39.4224258Z # shellcheck disable=SC2086,SC2090 2025-07-17T08:41:39.4224488Z container_name=$(docker run \ 2025-07-17T08:41:39.4224708Z  ${GPU_FLAG:-} \ 2025-07-17T08:41:39.4224904Z  -e BUILD_ENVIRONMENT \ 2025-07-17T08:41:39.4225128Z  -e PR_NUMBER \ 2025-07-17T08:41:39.4225395Z  -e GITHUB_ACTIONS \ 2025-07-17T08:41:39.4225596Z  -e GITHUB_REPOSITORY \ 2025-07-17T08:41:39.4225794Z  -e GITHUB_WORKFLOW \ 2025-07-17T08:41:39.4225990Z  -e GITHUB_JOB \ 2025-07-17T08:41:39.4226186Z  -e GITHUB_RUN_ID \ 2025-07-17T08:41:39.4226381Z  -e GITHUB_RUN_NUMBER \ 2025-07-17T08:41:39.4226581Z  -e GITHUB_RUN_ATTEMPT \ 2025-07-17T08:41:39.4226784Z  -e JOB_ID \ 2025-07-17T08:41:39.4226958Z  -e JOB_NAME \ 2025-07-17T08:41:39.4227146Z  -e BRANCH \ 2025-07-17T08:41:39.4227318Z  -e SHA1 \ 2025-07-17T08:41:39.4227493Z  -e AWS_DEFAULT_REGION \ 2025-07-17T08:41:39.4227704Z  -e IN_WHEEL_TEST \ 2025-07-17T08:41:39.4227895Z  -e SHARD_NUMBER \ 2025-07-17T08:41:39.4228084Z  -e TEST_CONFIG \ 2025-07-17T08:41:39.4228276Z  -e NUM_TEST_SHARDS \ 2025-07-17T08:41:39.4228477Z  -e REENABLED_ISSUES \ 2025-07-17T08:41:39.4228684Z  -e CONTINUE_THROUGH_ERROR \ 2025-07-17T08:41:39.4228896Z  -e VERBOSE_TEST_LOGS \ 2025-07-17T08:41:39.4229088Z  -e TEST_SHOWLOCALS \ 2025-07-17T08:41:39.4229279Z  -e NO_TEST_TIMEOUT \ 2025-07-17T08:41:39.4229472Z  -e NO_TD \ 2025-07-17T08:41:39.4229672Z  -e MAX_JOBS="$(nproc --ignore=2)" \ 2025-07-17T08:41:39.4229913Z  -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK \ 2025-07-17T08:41:39.4230152Z  -e PYTORCH_TEST_RERUN_DISABLED_TESTS \ 2025-07-17T08:41:39.4230378Z  -e TESTS_TO_INCLUDE \ 2025-07-17T08:41:39.4230575Z  -e DASHBOARD_TAG \ 2025-07-17T08:41:39.4230821Z  --env-file="${RUNNER_TEMP}/github_env_${GITHUB_RUN_ID}" \ 2025-07-17T08:41:39.4231092Z  --ulimit stack=10485760:83886080 \ 2025-07-17T08:41:39.4231314Z  --ulimit core=0 \ 2025-07-17T08:41:39.4231523Z  --security-opt seccomp=unconfined \ 2025-07-17T08:41:39.4231761Z  --cap-add=SYS_PTRACE \ 2025-07-17T08:41:39.4231964Z  --shm-size="8g" \ 2025-07-17T08:41:39.4232150Z  --tty \ 2025-07-17T08:41:39.4232319Z  --detach \ 2025-07-17T08:41:39.4232509Z  --name="${container_name}" \ 2025-07-17T08:41:39.4232967Z  --user jenkins \ 2025-07-17T08:41:39.4233205Z  -v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \ 2025-07-17T08:41:39.4233475Z  -w /var/lib/jenkins/workspace \ 2025-07-17T08:41:39.4233693Z  "${DOCKER_IMAGE}" 2025-07-17T08:41:39.4233872Z ) 2025-07-17T08:41:39.4234049Z # save container name for later step 2025-07-17T08:41:39.4234495Z echo "CONTAINER_NAME=${container_name}" >> "$GITHUB_ENV" 2025-07-17T08:41:39.4234967Z # jenkins user does not have write permission to mounted workspace; work-around by copying within container to jenkins home 2025-07-17T08:41:39.4235555Z docker exec -t "${container_name}" sh -c "cd .. && cp -R workspace pytorch && cd pytorch && pip install dist/*.whl && ${TEST_COMMAND}" 2025-07-17T08:41:39.4247634Z shell: /usr/bin/bash -e {0} 2025-07-17T08:41:39.4247834Z env: 2025-07-17T08:41:39.4248006Z GIT_DEFAULT_BRANCH: main 2025-07-17T08:41:39.4248236Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T08:41:39.4248555Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T08:41:39.4248835Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T08:41:39.4249541Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T08:41:39.4250227Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T08:41:39.4250420Z AWS_REGION: us-east-1 2025-07-17T08:41:39.4250664Z AWS_ACCESS_KEY_ID: *** 2025-07-17T08:41:39.4250916Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T08:41:39.4254860Z AWS_SESSION_TOKEN: *** 2025-07-17T08:41:39.4255095Z BUILD_ENVIRONMENT: linux-noble-rocm-py3.12-mi300 2025-07-17T08:41:39.4255333Z PR_NUMBER: 2025-07-17T08:41:39.4255502Z GITHUB_REPOSITORY: pytorch/pytorch 2025-07-17T08:41:39.4255712Z GITHUB_WORKFLOW: rocm-mi300 2025-07-17T08:41:39.4255907Z GITHUB_JOB: test 2025-07-17T08:41:39.4256078Z GITHUB_RUN_ID: 16337959895 2025-07-17T08:41:39.4256271Z GITHUB_RUN_NUMBER: 7135 2025-07-17T08:41:39.4256458Z GITHUB_RUN_ATTEMPT: 1 2025-07-17T08:41:39.4256642Z JOB_ID: 46160759521 2025-07-17T08:41:39.4256950Z JOB_NAME: linux-noble-rocm-py3.12-mi300 / test (default, 1, 6, linux.rocm.gpu.mi300.2, unstable) 2025-07-17T08:41:39.4257267Z BRANCH: main 2025-07-17T08:41:39.4257461Z SHA1: a38f433be2e94a64b095a44ba39879d02d0c2316 2025-07-17T08:41:39.4257690Z CONTINUE_THROUGH_ERROR: True 2025-07-17T08:41:39.4257882Z VERBOSE_TEST_LOGS: False 2025-07-17T08:41:39.4258069Z TEST_SHOWLOCALS: False 2025-07-17T08:41:39.4258255Z NO_TEST_TIMEOUT: False 2025-07-17T08:41:39.4258431Z NO_TD: False 2025-07-17T08:41:39.4258589Z TEST_CONFIG: default 2025-07-17T08:41:39.4258776Z SHARD_NUMBER: 1 2025-07-17T08:41:39.4258942Z NUM_TEST_SHARDS: 6 2025-07-17T08:41:39.4259115Z REENABLED_ISSUES: 2025-07-17T08:41:39.4259568Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-noble-rocm-n-py3-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T08:41:39.4260048Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: 0 2025-07-17T08:41:39.4260269Z PYTORCH_TEST_RERUN_DISABLED_TESTS: 0 2025-07-17T08:41:39.4260471Z TESTS_TO_INCLUDE: 2025-07-17T08:41:39.4260629Z DASHBOARD_TAG: 2025-07-17T08:41:39.4260791Z ##[endgroup] 2025-07-17T08:41:39.4295541Z + [[ default == \m\u\l\t\i\g\p\u ]] 2025-07-17T08:41:39.4295854Z + [[ linux-noble-rocm-py3.12-mi300 == *onnx* ]] 2025-07-17T08:41:39.4296101Z + TEST_COMMAND=.ci/pytorch/test.sh 2025-07-17T08:41:39.4306748Z +++ nproc --ignore=2 2025-07-17T08:41:39.4321325Z ++ docker run --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host -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 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 MAX_JOBS=254 -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK -e PYTORCH_TEST_RERUN_DISABLED_TESTS -e TESTS_TO_INCLUDE -e DASHBOARD_TAG --env-file=/home/runner/_work/_temp/github_env_16337959895 --ulimit stack=10485760:83886080 --ulimit core=0 --security-opt seccomp=unconfined --cap-add=SYS_PTRACE --shm-size=8g --tty --detach --name= --user jenkins -v /home/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-noble-rocm-n-py3-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T08:41:39.8801699Z + container_name=be5eba77e402a727f4714ddb7ab9e94c4cfe9ef82df6800750eddceb7a771559 2025-07-17T08:41:39.8802209Z + echo CONTAINER_NAME=be5eba77e402a727f4714ddb7ab9e94c4cfe9ef82df6800750eddceb7a771559 2025-07-17T08:41:39.8802892Z + docker exec -t be5eba77e402a727f4714ddb7ab9e94c4cfe9ef82df6800750eddceb7a771559 sh -c 'cd .. && cp -R workspace pytorch && cd pytorch && pip install dist/*.whl && .ci/pytorch/test.sh' 2025-07-17T08:42:02.2731366Z Processing ./dist/torch-2.9.0a0+gita38f433-cp312-cp312-linux_x86_64.whl 2025-07-17T08:42:03.0308104Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.9.0a0+gita38f433) (3.18.0) 2025-07-17T08:42:03.0308845Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.9.0a0+gita38f433) (4.14.1) 2025-07-17T08:42:03.0317012Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.9.0a0+gita38f433) (80.9.0) 2025-07-17T08:42:03.0318778Z Requirement already satisfied: sympy>=1.13.3 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.9.0a0+gita38f433) (1.13.3) 2025-07-17T08:42:03.0321039Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.9.0a0+gita38f433) (2.8.8) 2025-07-17T08:42:03.0322783Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.9.0a0+gita38f433) (3.1.6) 2025-07-17T08:42:03.0324494Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.9.0a0+gita38f433) (2025.5.1) 2025-07-17T08:42:03.0335108Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from sympy>=1.13.3->torch==2.9.0a0+gita38f433) (1.3.0) 2025-07-17T08:42:03.0415109Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from jinja2->torch==2.9.0a0+gita38f433) (3.0.2) 2025-07-17T08:42:03.2840551Z Installing collected packages: torch 2025-07-17T08:42:14.0420691Z ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts. 2025-07-17T08:42:14.0421325Z timm 1.0.14 requires torchvision, which is not installed. 2025-07-17T08:42:14.0421678Z helion 0.0.9 requires filecheck, which is not installed. 2025-07-17T08:42:14.0422079Z Successfully installed torch-2.9.0a0+gita38f433 2025-07-17T08:42:14.1007131Z + export TERM=vt100 2025-07-17T08:42:14.1007390Z + TERM=vt100 2025-07-17T08:42:14.1010090Z ++ dirname .ci/pytorch/test.sh 2025-07-17T08:42:14.1024082Z + source .ci/pytorch/common.sh 2025-07-17T08:42:14.1028688Z +++ dirname .ci/pytorch/common.sh 2025-07-17T08:42:14.1039353Z ++ source .ci/pytorch/common_utils.sh 2025-07-17T08:42:14.1041166Z +++ declare -f -t trap_add 2025-07-17T08:42:14.1044226Z ++ set -ex -o pipefail 2025-07-17T08:42:14.1044451Z ++ [[ linux-noble-rocm-py3.12-mi300 == *rocm* ]] 2025-07-17T08:42:14.1046961Z ++ unset HIP_PLATFORM 2025-07-17T08:42:14.1047157Z ++ export PYTORCH_TEST_WITH_ROCM=1 2025-07-17T08:42:14.1047371Z ++ PYTORCH_TEST_WITH_ROCM=1 2025-07-17T08:42:14.1047565Z ++ BUILD_TEST_LIBTORCH=0 2025-07-17T08:42:14.1049985Z ++ dirname .ci/pytorch/test.sh 2025-07-17T08:42:14.1058310Z + source .ci/pytorch/common-build.sh 2025-07-17T08:42:14.1060788Z ++ [[ linux-noble-rocm-py3.12-mi300 != *win-* ]] 2025-07-17T08:42:14.1069698Z ++++ dirname .ci/pytorch/common-build.sh 2025-07-17T08:42:14.1082119Z +++ cd .ci/pytorch 2025-07-17T08:42:14.1082830Z +++ pwd -P 2025-07-17T08:42:14.1085707Z ++ script_dir=/var/lib/jenkins/pytorch/.ci/pytorch 2025-07-17T08:42:14.1086071Z ++ [[ linux-noble-rocm-py3.12-mi300 == *-pch* ]] 2025-07-17T08:42:14.1086309Z ++ which sccache 2025-07-17T08:42:14.1100794Z ++ [[ -z '' ]] 2025-07-17T08:42:14.1101011Z ++ unset SCCACHE_BUCKET 2025-07-17T08:42:14.1101217Z ++ unset SCCACHE_REGION 2025-07-17T08:42:14.1101410Z ++ sccache --stop-server 2025-07-17T08:42:14.1246497Z ++ true 2025-07-17T08:42:14.1246739Z ++ rm -f /var/lib/jenkins/sccache_error.log 2025-07-17T08:42:14.1260733Z ++ trap_add sccache_epilogue EXIT 2025-07-17T08:42:14.1260962Z ++ trap_add_cmd=sccache_epilogue 2025-07-17T08:42:14.1261164Z ++ shift 2025-07-17T08:42:14.1261335Z ++ for trap_add_name in "$@" 2025-07-17T08:42:14.1270383Z ++++ trap -p EXIT 2025-07-17T08:42:14.1271864Z +++ eval 'extract_trap_cmd ' 2025-07-17T08:42:14.1272115Z ++++ extract_trap_cmd 2025-07-17T08:42:14.1272331Z ++++ printf '%s\n' '' 2025-07-17T08:42:14.1272814Z +++ printf '%s\n' sccache_epilogue 2025-07-17T08:42:14.1274952Z ++ trap -- ' 2025-07-17T08:42:14.1275123Z sccache_epilogue' EXIT 2025-07-17T08:42:14.1275295Z ++ [[ -n '' ]] 2025-07-17T08:42:14.1275485Z ++ [[ linux-noble-rocm-py3.12-mi300 == *rocm* ]] 2025-07-17T08:42:14.1275753Z ++ SCCACHE_ERROR_LOG=/var/lib/jenkins/sccache_error.log 2025-07-17T08:42:14.1275998Z ++ SCCACHE_IDLE_TIMEOUT=0 2025-07-17T08:42:14.1276178Z ++ sccache --start-server 2025-07-17T08:42:14.1291903Z sccache: Starting the server... 2025-07-17T08:42:14.1821400Z sccache: Listening on address 127.0.0.1:4226 2025-07-17T08:42:14.1839956Z ++ sccache --zero-stats 2025-07-17T08:42:14.1865691Z Statistics zeroed. 2025-07-17T08:42:14.1871150Z ++ which ccache 2025-07-17T08:42:14.1885677Z + [[ linux-noble-rocm-py3.12-mi300 != *rocm* ]] 2025-07-17T08:42:14.1885932Z + echo 'Environment variables:' 2025-07-17T08:42:14.1886134Z Environment variables: 2025-07-17T08:42:14.1886315Z + env 2025-07-17T08:42:14.1899357Z GITHUB_WORKSPACE=/home/runner/_work/pytorch/pytorch 2025-07-17T08:42:14.1899679Z CONTINUE_THROUGH_ERROR=True 2025-07-17T08:42:14.1899928Z BUILD_ENVIRONMENT=linux-noble-rocm-py3.12-mi300 2025-07-17T08:42:14.1900204Z HOSTNAME=linux.rocm.gpu.mi300.2-8zrv9-runner-r2mdd 2025-07-17T08:42:14.1900589Z GITHUB_PATH=/home/runner/_work/_temp/_runner_file_commands/add_path_c80a2d34-30e3-46c6-ac94-f8a532da78fe 2025-07-17T08:42:14.1900917Z GITHUB_ACTION=__self 2025-07-17T08:42:14.1901106Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2025-07-17T08:42:14.1901322Z GITHUB_RUN_NUMBER=7135 2025-07-17T08:42:14.1901494Z TEST_CONFIG=default 2025-07-17T08:42:14.1901671Z GITHUB_REPOSITORY_OWNER_ID=21003710 2025-07-17T08:42:14.1901882Z AWS_DEFAULT_REGION=us-east-1 2025-07-17T08:42:14.1902113Z GITHUB_TRIGGERING_ACTOR=pytorchmergebot 2025-07-17T08:42:14.1902324Z GITHUB_REF_TYPE=branch 2025-07-17T08:42:14.1902823Z *** 2025-07-17T08:42:14.1902981Z GITHUB_REPOSITORY_ID=65600975 2025-07-17T08:42:14.1903178Z GITHUB_ACTIONS=true 2025-07-17T08:42:14.1903364Z SHA1=a38f433be2e94a64b095a44ba39879d02d0c2316 2025-07-17T08:42:14.1903642Z GITHUB_SHA=a38f433be2e94a64b095a44ba39879d02d0c2316 2025-07-17T08:42:14.1903987Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/rocm-mi300.yml@refs/heads/main 2025-07-17T08:42:14.1904286Z UCC_HOME=/usr 2025-07-17T08:42:14.1904443Z VERBOSE_TEST_LOGS=False 2025-07-17T08:42:14.1904620Z GITHUB_REF=refs/heads/main 2025-07-17T08:42:14.1904812Z SHARD_NUMBER=1 2025-07-17T08:42:14.1904978Z GITHUB_REF_PROTECTED=true 2025-07-17T08:42:14.1905768Z HOME=/var/lib/jenkins 2025-07-17T08:42:14.1905958Z GITHUB_API_URL=https://api.github.com 2025-07-17T08:42:14.1906201Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2025-07-17T08:42:14.1906402Z LANG=C.UTF-8 2025-07-17T08:42:14.1906588Z UCX_COMMIT=cc312eaa4655c0cc5c2bcd796db938f90563bcf6 2025-07-17T08:42:14.1906813Z PYTORCH_TEST_WITH_ROCM=1 2025-07-17T08:42:14.1906987Z NUM_TEST_SHARDS=6 2025-07-17T08:42:14.1907143Z UCX_HOME=/usr 2025-07-17T08:42:14.1907685Z GITHUB_STATE=/home/runner/_work/_temp/_runner_file_commands/save_state_c80a2d34-30e3-46c6-ac94-f8a532da78fe 2025-07-17T08:42:14.1908173Z JOB_NAME=linux-noble-rocm-py3.12-mi300 / test (default, 1, 6, linux.rocm.gpu.mi300.2, unstable) 2025-07-17T08:42:14.1908491Z MAGMA_HOME=/opt/rocm/magma 2025-07-17T08:42:14.1908813Z GITHUB_ENV=/home/runner/_work/_temp/_runner_file_commands/set_env_c80a2d34-30e3-46c6-ac94-f8a532da78fe 2025-07-17T08:42:14.1909204Z GITHUB_EVENT_PATH=/home/runner/_work/_temp/_github_workflow/event.json 2025-07-17T08:42:14.1909516Z GITHUB_EVENT_NAME=push 2025-07-17T08:42:14.1909776Z GITHUB_ACTIONS_RUNNER_EXTRA_USER_AGENT=actions-runner-controller/0.11.0 2025-07-17T08:42:14.1910039Z DASHBOARD_TAG= 2025-07-17T08:42:14.1910193Z GITHUB_RUN_ID=16337959895 2025-07-17T08:42:14.1910523Z GITHUB_STEP_SUMMARY=/home/runner/_work/_temp/_runner_file_commands/step_summary_c80a2d34-30e3-46c6-ac94-f8a532da78fe 2025-07-17T08:42:14.1910887Z GITHUB_ACTOR=pytorchmergebot 2025-07-17T08:42:14.1911069Z PR_NUMBER= 2025-07-17T08:42:14.1911221Z GITHUB_RUN_ATTEMPT=1 2025-07-17T08:42:14.1911395Z ANACONDA_PYTHON_VERSION=3.12 2025-07-17T08:42:14.1911612Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2025-07-17T08:42:14.1911835Z TERM=vt100 2025-07-17T08:42:14.1911982Z INSTALLED_VISION=yes 2025-07-17T08:42:14.1912141Z BRANCH=main 2025-07-17T08:42:14.1912294Z OPENSSL_ROOT_DIR=/opt/openssl 2025-07-17T08:42:14.1912476Z TESTS_TO_INCLUDE= 2025-07-17T08:42:14.1912726Z GITHUB_ACTION_PATH=/home/runner/_work/pytorch/pytorch/./.github/actions/setup-rocm 2025-07-17T08:42:14.1913027Z GITHUB_SERVER_URL=https://github.com 2025-07-17T08:42:14.1913240Z PYTORCH_ROCM_ARCH=gfx90a;gfx942 2025-07-17T08:42:14.1913453Z UCC_COMMIT=0c0fc21559835044ab107199e334f7157d6a0d3d 2025-07-17T08:42:14.1913666Z REENABLED_ISSUES= 2025-07-17T08:42:14.1913818Z SHLVL=1 2025-07-17T08:42:14.1913963Z MAX_JOBS=254 2025-07-17T08:42:14.1914122Z GITHUB_ACTOR_ID=97764156 2025-07-17T08:42:14.1914348Z GITHUB_WORKFLOW_SHA=a38f433be2e94a64b095a44ba39879d02d0c2316 2025-07-17T08:42:14.1914594Z GITHUB_REF_NAME=main 2025-07-17T08:42:14.1914756Z ROCM_PATH=/opt/rocm 2025-07-17T08:42:14.1914912Z GITHUB_JOB=test 2025-07-17T08:42:14.1915067Z NO_TEST_TIMEOUT=False 2025-07-17T08:42:14.1915245Z GITHUB_REPOSITORY=pytorch/pytorch 2025-07-17T08:42:14.1915451Z LC_ALL=C.UTF-8 2025-07-17T08:42:14.1915609Z GITHUB_RETENTION_DAYS=90 2025-07-17T08:42:14.1915785Z OPENSSL_DIR=/opt/openssl 2025-07-17T08:42:14.1915960Z GITHUB_ACTION_REPOSITORY= 2025-07-17T08:42:14.1916531Z PATH=/opt/cache/bin:/opt/rocm/llvm/bin:/opt/rocm/opencl/bin:/opt/rocm/hip/bin:/opt/rocm/hcc/bin:/opt/rocm/bin:/opt/conda/envs/py_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2025-07-17T08:42:14.1917105Z GITHUB_BASE_REF= 2025-07-17T08:42:14.1917255Z CI=true 2025-07-17T08:42:14.1917403Z GITHUB_REPOSITORY_OWNER=pytorch 2025-07-17T08:42:14.1917591Z JOB_ID=46160759521 2025-07-17T08:42:14.1917745Z GITHUB_HEAD_REF= 2025-07-17T08:42:14.1917894Z GITHUB_ACTION_REF= 2025-07-17T08:42:14.1918065Z TEST_SHOWLOCALS=False 2025-07-17T08:42:14.1918240Z GITHUB_WORKFLOW=rocm-mi300 2025-07-17T08:42:14.1918430Z DEBIAN_FRONTEND=noninteractive 2025-07-17T08:42:14.1918770Z GITHUB_OUTPUT=/home/runner/_work/_temp/_runner_file_commands/set_output_c80a2d34-30e3-46c6-ac94-f8a532da78fe 2025-07-17T08:42:14.1919101Z NO_TD=False 2025-07-17T08:42:14.1919255Z OLDPWD=/var/lib/jenkins 2025-07-17T08:42:14.1919420Z _=/usr/bin/env 2025-07-17T08:42:14.1919629Z ++ python -c 'import site; print(site.getsitepackages()[0])' 2025-07-17T08:42:14.2022195Z + TORCH_INSTALL_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch 2025-07-17T08:42:14.2022897Z + TORCH_BIN_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin 2025-07-17T08:42:14.2023236Z + TORCH_LIB_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib 2025-07-17T08:42:14.2023585Z + TORCH_TEST_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/test 2025-07-17T08:42:14.2023863Z + BUILD_DIR=build 2025-07-17T08:42:14.2024042Z + BUILD_RENAMED_DIR=build_renamed 2025-07-17T08:42:14.2024409Z + BUILD_BIN_DIR=build/bin 2025-07-17T08:42:14.2024584Z + SHARD_NUMBER=1 2025-07-17T08:42:14.2024744Z + NUM_TEST_SHARDS=6 2025-07-17T08:42:14.2024918Z + export TORCH_SERIALIZATION_DEBUG=1 2025-07-17T08:42:14.2025131Z + TORCH_SERIALIZATION_DEBUG=1 2025-07-17T08:42:14.2025424Z + export VALGRIND=ON 2025-07-17T08:42:14.2025588Z + VALGRIND=ON 2025-07-17T08:42:14.2025772Z + [[ linux-noble-rocm-py3.12-mi300 == *clang9* ]] 2025-07-17T08:42:14.2026012Z + [[ linux-noble-rocm-py3.12-mi300 == *xpu* ]] 2025-07-17T08:42:14.2026259Z + [[ linux-noble-rocm-py3.12-mi300 == *s390x* ]] 2025-07-17T08:42:14.2026477Z + [[ 0 == \1 ]] 2025-07-17T08:42:14.2026642Z + [[ True == \1 ]] 2025-07-17T08:42:14.2026822Z + [[ linux-noble-rocm-py3.12-mi300 != *bazel* ]] 2025-07-17T08:42:14.2027494Z ++ realpath build/custom_test_artifacts 2025-07-17T08:42:14.2039467Z + CUSTOM_TEST_ARTIFACT_BUILD_DIR=/var/lib/jenkins/pytorch/build/custom_test_artifacts 2025-07-17T08:42:14.2039811Z + [[ -n '' ]] 2025-07-17T08:42:14.2040002Z + echo 'Environment variables' 2025-07-17T08:42:14.2040206Z Environment variables 2025-07-17T08:42:14.2040362Z + env 2025-07-17T08:42:14.2050910Z GITHUB_WORKSPACE=/home/runner/_work/pytorch/pytorch 2025-07-17T08:42:14.2051172Z CONTINUE_THROUGH_ERROR=True 2025-07-17T08:42:14.2051414Z BUILD_ENVIRONMENT=linux-noble-rocm-py3.12-mi300 2025-07-17T08:42:14.2051680Z HOSTNAME=linux.rocm.gpu.mi300.2-8zrv9-runner-r2mdd 2025-07-17T08:42:14.2052072Z GITHUB_PATH=/home/runner/_work/_temp/_runner_file_commands/add_path_c80a2d34-30e3-46c6-ac94-f8a532da78fe 2025-07-17T08:42:14.2052411Z GITHUB_ACTION=__self 2025-07-17T08:42:14.2052593Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2025-07-17T08:42:14.2052789Z GITHUB_RUN_NUMBER=7135 2025-07-17T08:42:14.2052952Z TEST_CONFIG=default 2025-07-17T08:42:14.2053124Z GITHUB_REPOSITORY_OWNER_ID=21003710 2025-07-17T08:42:14.2053332Z AWS_DEFAULT_REGION=us-east-1 2025-07-17T08:42:14.2053530Z GITHUB_TRIGGERING_ACTOR=pytorchmergebot 2025-07-17T08:42:14.2053738Z GITHUB_REF_TYPE=branch 2025-07-17T08:42:14.2053968Z *** 2025-07-17T08:42:14.2054136Z GITHUB_REPOSITORY_ID=65600975 2025-07-17T08:42:14.2054353Z GITHUB_ACTIONS=true 2025-07-17T08:42:14.2054537Z SHA1=a38f433be2e94a64b095a44ba39879d02d0c2316 2025-07-17T08:42:14.2054775Z GITHUB_SHA=a38f433be2e94a64b095a44ba39879d02d0c2316 2025-07-17T08:42:14.2055114Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/rocm-mi300.yml@refs/heads/main 2025-07-17T08:42:14.2055422Z UCC_HOME=/usr 2025-07-17T08:42:14.2055588Z TORCH_SERIALIZATION_DEBUG=1 2025-07-17T08:42:14.2055771Z VERBOSE_TEST_LOGS=False 2025-07-17T08:42:14.2055948Z GITHUB_REF=refs/heads/main 2025-07-17T08:42:14.2056116Z SHARD_NUMBER=1 2025-07-17T08:42:14.2056275Z GITHUB_REF_PROTECTED=true 2025-07-17T08:42:14.2056448Z HOME=/var/lib/jenkins 2025-07-17T08:42:14.2056640Z GITHUB_API_URL=https://api.github.com 2025-07-17T08:42:14.2056866Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2025-07-17T08:42:14.2057059Z LANG=C.UTF-8 2025-07-17T08:42:14.2057284Z UCX_COMMIT=cc312eaa4655c0cc5c2bcd796db938f90563bcf6 2025-07-17T08:42:14.2057515Z PYTORCH_TEST_WITH_ROCM=1 2025-07-17T08:42:14.2057688Z NUM_TEST_SHARDS=6 2025-07-17T08:42:14.2057841Z UCX_HOME=/usr 2025-07-17T08:42:14.2058146Z GITHUB_STATE=/home/runner/_work/_temp/_runner_file_commands/save_state_c80a2d34-30e3-46c6-ac94-f8a532da78fe 2025-07-17T08:42:14.2058611Z JOB_NAME=linux-noble-rocm-py3.12-mi300 / test (default, 1, 6, linux.rocm.gpu.mi300.2, unstable) 2025-07-17T08:42:14.2058925Z MAGMA_HOME=/opt/rocm/magma 2025-07-17T08:42:14.2059232Z GITHUB_ENV=/home/runner/_work/_temp/_runner_file_commands/set_env_c80a2d34-30e3-46c6-ac94-f8a532da78fe 2025-07-17T08:42:14.2059873Z GITHUB_EVENT_PATH=/home/runner/_work/_temp/_github_workflow/event.json 2025-07-17T08:42:14.2060127Z GITHUB_EVENT_NAME=push 2025-07-17T08:42:14.2060377Z GITHUB_ACTIONS_RUNNER_EXTRA_USER_AGENT=actions-runner-controller/0.11.0 2025-07-17T08:42:14.2060641Z DASHBOARD_TAG= 2025-07-17T08:42:14.2060799Z GITHUB_RUN_ID=16337959895 2025-07-17T08:42:14.2061265Z GITHUB_STEP_SUMMARY=/home/runner/_work/_temp/_runner_file_commands/step_summary_c80a2d34-30e3-46c6-ac94-f8a532da78fe 2025-07-17T08:42:14.2061641Z GITHUB_ACTOR=pytorchmergebot 2025-07-17T08:42:14.2061822Z PR_NUMBER= 2025-07-17T08:42:14.2061969Z GITHUB_RUN_ATTEMPT=1 2025-07-17T08:42:14.2062127Z VALGRIND=ON 2025-07-17T08:42:14.2062280Z ANACONDA_PYTHON_VERSION=3.12 2025-07-17T08:42:14.2062496Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2025-07-17T08:42:14.2062710Z TERM=vt100 2025-07-17T08:42:14.2062859Z INSTALLED_VISION=yes 2025-07-17T08:42:14.2063014Z BRANCH=main 2025-07-17T08:42:14.2063170Z OPENSSL_ROOT_DIR=/opt/openssl 2025-07-17T08:42:14.2063357Z TESTS_TO_INCLUDE= 2025-07-17T08:42:14.2063611Z GITHUB_ACTION_PATH=/home/runner/_work/pytorch/pytorch/./.github/actions/setup-rocm 2025-07-17T08:42:14.2063914Z GITHUB_SERVER_URL=https://github.com 2025-07-17T08:42:14.2064121Z PYTORCH_ROCM_ARCH=gfx90a;gfx942 2025-07-17T08:42:14.2064332Z UCC_COMMIT=0c0fc21559835044ab107199e334f7157d6a0d3d 2025-07-17T08:42:14.2064542Z REENABLED_ISSUES= 2025-07-17T08:42:14.2064685Z SHLVL=1 2025-07-17T08:42:14.2064826Z MAX_JOBS=254 2025-07-17T08:42:14.2064982Z GITHUB_ACTOR_ID=97764156 2025-07-17T08:42:14.2065201Z GITHUB_WORKFLOW_SHA=a38f433be2e94a64b095a44ba39879d02d0c2316 2025-07-17T08:42:14.2065564Z GITHUB_REF_NAME=main 2025-07-17T08:42:14.2065730Z ROCM_PATH=/opt/rocm 2025-07-17T08:42:14.2065885Z GITHUB_JOB=test 2025-07-17T08:42:14.2066043Z NO_TEST_TIMEOUT=False 2025-07-17T08:42:14.2066220Z GITHUB_REPOSITORY=pytorch/pytorch 2025-07-17T08:42:14.2066410Z LC_ALL=C.UTF-8 2025-07-17T08:42:14.2066557Z GITHUB_RETENTION_DAYS=90 2025-07-17T08:42:14.2066737Z OPENSSL_DIR=/opt/openssl 2025-07-17T08:42:14.2066908Z GITHUB_ACTION_REPOSITORY= 2025-07-17T08:42:14.2067483Z PATH=/opt/cache/bin:/opt/rocm/llvm/bin:/opt/rocm/opencl/bin:/opt/rocm/hip/bin:/opt/rocm/hcc/bin:/opt/rocm/bin:/opt/conda/envs/py_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2025-07-17T08:42:14.2068059Z GITHUB_BASE_REF= 2025-07-17T08:42:14.2068219Z CI=true 2025-07-17T08:42:14.2068372Z GITHUB_REPOSITORY_OWNER=pytorch 2025-07-17T08:42:14.2068568Z JOB_ID=46160759521 2025-07-17T08:42:14.2068725Z GITHUB_HEAD_REF= 2025-07-17T08:42:14.2068876Z GITHUB_ACTION_REF= 2025-07-17T08:42:14.2069033Z TEST_SHOWLOCALS=False 2025-07-17T08:42:14.2069206Z GITHUB_WORKFLOW=rocm-mi300 2025-07-17T08:42:14.2069402Z DEBIAN_FRONTEND=noninteractive 2025-07-17T08:42:14.2069744Z GITHUB_OUTPUT=/home/runner/_work/_temp/_runner_file_commands/set_output_c80a2d34-30e3-46c6-ac94-f8a532da78fe 2025-07-17T08:42:14.2070081Z NO_TD=False 2025-07-17T08:42:14.2070235Z OLDPWD=/var/lib/jenkins 2025-07-17T08:42:14.2070405Z _=/usr/bin/env 2025-07-17T08:42:14.2070567Z + echo 'Testing pytorch' 2025-07-17T08:42:14.2070739Z Testing pytorch 2025-07-17T08:42:14.2070902Z + export LANG=C.UTF-8 2025-07-17T08:42:14.2071063Z + LANG=C.UTF-8 2025-07-17T08:42:14.2071206Z + PR_NUMBER= 2025-07-17T08:42:14.2071362Z + [[ default == \d\e\f\a\u\l\t ]] 2025-07-17T08:42:14.2071559Z + export CUDA_VISIBLE_DEVICES=0 2025-07-17T08:42:14.2071745Z + CUDA_VISIBLE_DEVICES=0 2025-07-17T08:42:14.2071929Z + export HIP_VISIBLE_DEVICES=0 2025-07-17T08:42:14.2072117Z + HIP_VISIBLE_DEVICES=0 2025-07-17T08:42:14.2072296Z + [[ default == \d\i\s\t\r\i\b\u\t\e\d ]] 2025-07-17T08:42:14.2072500Z + [[ default == \s\l\o\w ]] 2025-07-17T08:42:14.2072732Z + [[ linux-noble-rocm-py3.12-mi300 == *slow-gradcheck* ]] 2025-07-17T08:42:14.2072989Z + [[ linux-noble-rocm-py3.12-mi300 == *cuda* ]] 2025-07-17T08:42:14.2073218Z + [[ linux-noble-rocm-py3.12-mi300 == *rocm* ]] 2025-07-17T08:42:14.2073438Z + export PYTORCH_TESTING_DEVICE_ONLY_FOR=cuda 2025-07-17T08:42:14.2073812Z + PYTORCH_TESTING_DEVICE_ONLY_FOR=cuda 2025-07-17T08:42:14.2074020Z + [[ default == *crossref* ]] 2025-07-17T08:42:14.2074219Z + [[ linux-noble-rocm-py3.12-mi300 == *rocm* ]] 2025-07-17T08:42:14.2074422Z + export VALGRIND=OFF 2025-07-17T08:42:14.2074586Z + VALGRIND=OFF 2025-07-17T08:42:14.2074736Z + rocminfo 2025-07-17T08:42:14.2248160Z ROCk module version 6.12.12 is loaded 2025-07-17T08:42:14.4216040Z ===================== 2025-07-17T08:42:14.4216670Z HSA System Attributes 2025-07-17T08:42:14.4216862Z ===================== 2025-07-17T08:42:14.4217043Z Runtime Version: 1.15 2025-07-17T08:42:14.4217229Z Runtime Ext Version: 1.7 2025-07-17T08:42:14.4217429Z System Timestamp Freq.: 1000.000000MHz 2025-07-17T08:42:14.4217745Z Sig. Max Wait Duration: 18446744073709551615 (0xFFFFFFFFFFFFFFFF) (timestamp count) 2025-07-17T08:42:14.4218076Z Machine Model: LARGE 2025-07-17T08:42:14.4218341Z System Endianness: LITTLE 2025-07-17T08:42:14.4218582Z Mwaitx: DISABLED 2025-07-17T08:42:14.4218769Z XNACK enabled: NO 2025-07-17T08:42:14.4218962Z DMAbuf Support: YES 2025-07-17T08:42:14.4219141Z VMM Support: YES 2025-07-17T08:42:14.4219251Z 2025-07-17T08:42:14.4219325Z ========== 2025-07-17T08:42:14.4219511Z HSA Agents 2025-07-17T08:42:14.4219682Z ========== 2025-07-17T08:42:14.4219834Z ******* 2025-07-17T08:42:14.4219991Z Agent 1 2025-07-17T08:42:14.4220157Z ******* 2025-07-17T08:42:14.4220354Z Name: AMD EPYC 9534 64-Core Processor 2025-07-17T08:42:14.4220598Z Uuid: CPU-XX 2025-07-17T08:42:14.4220852Z Marketing Name: AMD EPYC 9534 64-Core Processor 2025-07-17T08:42:14.4221114Z Vendor Name: CPU 2025-07-17T08:42:14.4221362Z Feature: None specified 2025-07-17T08:42:14.4221630Z Profile: FULL_PROFILE 2025-07-17T08:42:14.4221878Z Float Round Mode: NEAR 2025-07-17T08:42:14.4222201Z Max Queue Number: 0(0x0) 2025-07-17T08:42:14.4222441Z Queue Min Size: 0(0x0) 2025-07-17T08:42:14.4222684Z Queue Max Size: 0(0x0) 2025-07-17T08:42:14.4222921Z Queue Type: MULTI 2025-07-17T08:42:14.4223159Z Node: 0 2025-07-17T08:42:14.4223386Z Device Type: CPU 2025-07-17T08:42:14.4223607Z Cache Info: 2025-07-17T08:42:14.4223800Z L1: 32768(0x8000) KB 2025-07-17T08:42:14.4224030Z Chip ID: 0(0x0) 2025-07-17T08:42:14.4224273Z ASIC Revision: 0(0x0) 2025-07-17T08:42:14.4224520Z Cacheline Size: 64(0x40) 2025-07-17T08:42:14.4224767Z Max Clock Freq. (MHz): 2450 2025-07-17T08:42:14.4224997Z BDFID: 0 2025-07-17T08:42:14.4225232Z Internal Node ID: 0 2025-07-17T08:42:14.4225632Z Compute Unit: 128 2025-07-17T08:42:14.4225902Z SIMDs per CU: 0 2025-07-17T08:42:14.4226149Z Shader Engines: 0 2025-07-17T08:42:14.4226404Z Shader Arrs. per Eng.: 0 2025-07-17T08:42:14.4226664Z WatchPts on Addr. Ranges:1 2025-07-17T08:42:14.4226899Z Memory Properties: 2025-07-17T08:42:14.4227084Z Features: None 2025-07-17T08:42:14.4227491Z Pool Info: 2025-07-17T08:42:14.4227663Z Pool 1 2025-07-17T08:42:14.4227884Z Segment: GLOBAL; FLAGS: FINE GRAINED 2025-07-17T08:42:14.4228127Z Size: 1188703736(0x46da2df8) KB 2025-07-17T08:42:14.4228369Z Allocatable: TRUE 2025-07-17T08:42:14.4240458Z Alloc Granule: 4KB 2025-07-17T08:42:14.4240754Z Alloc Recommended Granule:4KB 2025-07-17T08:42:14.4241038Z Alloc Alignment: 4KB 2025-07-17T08:42:14.4241300Z Accessible by all: TRUE 2025-07-17T08:42:14.4241530Z Pool 2 2025-07-17T08:42:14.4241758Z Segment: GLOBAL; FLAGS: EXTENDED FINE GRAINED 2025-07-17T08:42:14.4242028Z Size: 1188703736(0x46da2df8) KB 2025-07-17T08:42:14.4242291Z Allocatable: TRUE 2025-07-17T08:42:14.4242539Z Alloc Granule: 4KB 2025-07-17T08:42:14.4242802Z Alloc Recommended Granule:4KB 2025-07-17T08:42:14.4243067Z Alloc Alignment: 4KB 2025-07-17T08:42:14.4243328Z Accessible by all: TRUE 2025-07-17T08:42:14.4243555Z Pool 3 2025-07-17T08:42:14.4243772Z Segment: GLOBAL; FLAGS: KERNARG, FINE GRAINED 2025-07-17T08:42:14.4244006Z Size: 1188703736(0x46da2df8) KB 2025-07-17T08:42:14.4244240Z Allocatable: TRUE 2025-07-17T08:42:14.4244487Z Alloc Granule: 4KB 2025-07-17T08:42:14.4244748Z Alloc Recommended Granule:4KB 2025-07-17T08:42:14.4245010Z Alloc Alignment: 4KB 2025-07-17T08:42:14.4245262Z Accessible by all: TRUE 2025-07-17T08:42:14.4245483Z Pool 4 2025-07-17T08:42:14.4245689Z Segment: GLOBAL; FLAGS: COARSE GRAINED 2025-07-17T08:42:14.4245923Z Size: 1188703736(0x46da2df8) KB 2025-07-17T08:42:14.4246162Z Allocatable: TRUE 2025-07-17T08:42:14.4246416Z Alloc Granule: 4KB 2025-07-17T08:42:14.4246675Z Alloc Recommended Granule:4KB 2025-07-17T08:42:14.4246930Z Alloc Alignment: 4KB 2025-07-17T08:42:14.4247174Z Accessible by all: TRUE 2025-07-17T08:42:14.4247401Z ISA Info: 2025-07-17T08:42:14.4247581Z ******* 2025-07-17T08:42:14.4247753Z Agent 2 2025-07-17T08:42:14.4247919Z ******* 2025-07-17T08:42:14.4248110Z Name: AMD EPYC 9534 64-Core Processor 2025-07-17T08:42:14.4248350Z Uuid: CPU-XX 2025-07-17T08:42:14.4248601Z Marketing Name: AMD EPYC 9534 64-Core Processor 2025-07-17T08:42:14.4248861Z Vendor Name: CPU 2025-07-17T08:42:14.4249102Z Feature: None specified 2025-07-17T08:42:14.4249340Z Profile: FULL_PROFILE 2025-07-17T08:42:14.4249580Z Float Round Mode: NEAR 2025-07-17T08:42:14.4249826Z Max Queue Number: 0(0x0) 2025-07-17T08:42:14.4250064Z Queue Min Size: 0(0x0) 2025-07-17T08:42:14.4250301Z Queue Max Size: 0(0x0) 2025-07-17T08:42:14.4250670Z Queue Type: MULTI 2025-07-17T08:42:14.4250896Z Node: 1 2025-07-17T08:42:14.4251122Z Device Type: CPU 2025-07-17T08:42:14.4251341Z Cache Info: 2025-07-17T08:42:14.4251539Z L1: 32768(0x8000) KB 2025-07-17T08:42:14.4251882Z Chip ID: 0(0x0) 2025-07-17T08:42:14.4252128Z ASIC Revision: 0(0x0) 2025-07-17T08:42:14.4252379Z Cacheline Size: 64(0x40) 2025-07-17T08:42:14.4252626Z Max Clock Freq. (MHz): 2450 2025-07-17T08:42:14.4252855Z BDFID: 0 2025-07-17T08:42:14.4253086Z Internal Node ID: 1 2025-07-17T08:42:14.4253328Z Compute Unit: 128 2025-07-17T08:42:14.4253565Z SIMDs per CU: 0 2025-07-17T08:42:14.4253806Z Shader Engines: 0 2025-07-17T08:42:14.4254057Z Shader Arrs. per Eng.: 0 2025-07-17T08:42:14.4254320Z WatchPts on Addr. Ranges:1 2025-07-17T08:42:14.4254556Z Memory Properties: 2025-07-17T08:42:14.4254736Z Features: None 2025-07-17T08:42:14.4254917Z Pool Info: 2025-07-17T08:42:14.4255094Z Pool 1 2025-07-17T08:42:14.4255307Z Segment: GLOBAL; FLAGS: FINE GRAINED 2025-07-17T08:42:14.4255556Z Size: 1188946676(0x46dde2f4) KB 2025-07-17T08:42:14.4255814Z Allocatable: TRUE 2025-07-17T08:42:14.4256061Z Alloc Granule: 4KB 2025-07-17T08:42:14.4256323Z Alloc Recommended Granule:4KB 2025-07-17T08:42:14.4256588Z Alloc Alignment: 4KB 2025-07-17T08:42:14.4256845Z Accessible by all: TRUE 2025-07-17T08:42:14.4257070Z Pool 2 2025-07-17T08:42:14.4257283Z Segment: GLOBAL; FLAGS: EXTENDED FINE GRAINED 2025-07-17T08:42:14.4257526Z Size: 1188946676(0x46dde2f4) KB 2025-07-17T08:42:14.4257760Z Allocatable: TRUE 2025-07-17T08:42:14.4258007Z Alloc Granule: 4KB 2025-07-17T08:42:14.4258268Z Alloc Recommended Granule:4KB 2025-07-17T08:42:14.4258528Z Alloc Alignment: 4KB 2025-07-17T08:42:14.4258779Z Accessible by all: TRUE 2025-07-17T08:42:14.4259006Z Pool 3 2025-07-17T08:42:14.4259202Z Segment: GLOBAL; FLAGS: KERNARG, FINE GRAINED 2025-07-17T08:42:14.4259428Z Size: 1188946676(0x46dde2f4) KB 2025-07-17T08:42:14.4259661Z Allocatable: TRUE 2025-07-17T08:42:14.4259909Z Alloc Granule: 4KB 2025-07-17T08:42:14.4260166Z Alloc Recommended Granule:4KB 2025-07-17T08:42:14.4260424Z Alloc Alignment: 4KB 2025-07-17T08:42:14.4260673Z Accessible by all: TRUE 2025-07-17T08:42:14.4260889Z Pool 4 2025-07-17T08:42:14.4261088Z Segment: GLOBAL; FLAGS: COARSE GRAINED 2025-07-17T08:42:14.4261320Z Size: 1188946676(0x46dde2f4) KB 2025-07-17T08:42:14.4261676Z Allocatable: TRUE 2025-07-17T08:42:14.4261921Z Alloc Granule: 4KB 2025-07-17T08:42:14.4262171Z Alloc Recommended Granule:4KB 2025-07-17T08:42:14.4262428Z Alloc Alignment: 4KB 2025-07-17T08:42:14.4262780Z Accessible by all: TRUE 2025-07-17T08:42:14.4262998Z ISA Info: 2025-07-17T08:42:14.4263165Z ******* 2025-07-17T08:42:14.4263333Z Agent 3 2025-07-17T08:42:14.4263492Z ******* 2025-07-17T08:42:14.4263675Z Name: gfx942 2025-07-17T08:42:14.4263905Z Uuid: GPU-d7d27283ffe4d923 2025-07-17T08:42:14.4264150Z Marketing Name: AMD Instinct MI300X 2025-07-17T08:42:14.4264399Z Vendor Name: AMD 2025-07-17T08:42:14.4264645Z Feature: KERNEL_DISPATCH 2025-07-17T08:42:14.4264886Z Profile: BASE_PROFILE 2025-07-17T08:42:14.4265132Z Float Round Mode: NEAR 2025-07-17T08:42:14.4265454Z Max Queue Number: 128(0x80) 2025-07-17T08:42:14.4265722Z Queue Min Size: 64(0x40) 2025-07-17T08:42:14.4265962Z Queue Max Size: 131072(0x20000) 2025-07-17T08:42:14.4266204Z Queue Type: MULTI 2025-07-17T08:42:14.4266437Z Node: 2 2025-07-17T08:42:14.4266665Z Device Type: GPU 2025-07-17T08:42:14.4266891Z Cache Info: 2025-07-17T08:42:14.4267090Z L1: 32(0x20) KB 2025-07-17T08:42:14.4267314Z L2: 4096(0x1000) KB 2025-07-17T08:42:14.4267524Z L3: 262144(0x40000) KB 2025-07-17T08:42:14.4267744Z Chip ID: 29857(0x74a1) 2025-07-17T08:42:14.4267982Z ASIC Revision: 1(0x1) 2025-07-17T08:42:14.4268228Z Cacheline Size: 128(0x80) 2025-07-17T08:42:14.4268482Z Max Clock Freq. (MHz): 2100 2025-07-17T08:42:14.4268717Z BDFID: 50944 2025-07-17T08:42:14.4268944Z Internal Node ID: 2 2025-07-17T08:42:14.4269199Z Compute Unit: 304 2025-07-17T08:42:14.4269436Z SIMDs per CU: 4 2025-07-17T08:42:14.4269678Z Shader Engines: 32 2025-07-17T08:42:14.4269926Z Shader Arrs. per Eng.: 1 2025-07-17T08:42:14.4270183Z WatchPts on Addr. Ranges:4 2025-07-17T08:42:14.4270446Z Coherent Host Access: FALSE 2025-07-17T08:42:14.4270677Z Memory Properties: 2025-07-17T08:42:14.4270869Z Features: KERNEL_DISPATCH 2025-07-17T08:42:14.4271109Z Fast F16 Operation: TRUE 2025-07-17T08:42:14.4271362Z Wavefront Size: 64(0x40) 2025-07-17T08:42:14.4271614Z Workgroup Max Size: 1024(0x400) 2025-07-17T08:42:14.4271845Z Workgroup Max Size per Dimension: 2025-07-17T08:42:14.4272039Z x 1024(0x400) 2025-07-17T08:42:14.4272241Z y 1024(0x400) 2025-07-17T08:42:14.4272446Z z 1024(0x400) 2025-07-17T08:42:14.4272820Z Max Waves Per CU: 32(0x20) 2025-07-17T08:42:14.4273068Z Max Work-item Per CU: 2048(0x800) 2025-07-17T08:42:14.4273312Z Grid Max Size: 4294967295(0xffffffff) 2025-07-17T08:42:14.4273535Z Grid Max Size per Dimension: 2025-07-17T08:42:14.4273720Z x 4294967295(0xffffffff) 2025-07-17T08:42:14.4279203Z y 4294967295(0xffffffff) 2025-07-17T08:42:14.4279433Z z 4294967295(0xffffffff) 2025-07-17T08:42:14.4279673Z Max fbarriers/Workgrp: 32 2025-07-17T08:42:14.4279994Z Packet Processor uCode:: 177 2025-07-17T08:42:14.4280265Z SDMA engine uCode:: 24 2025-07-17T08:42:14.4280520Z IOMMU Support:: None 2025-07-17T08:42:14.4280762Z Pool Info: 2025-07-17T08:42:14.4280945Z Pool 1 2025-07-17T08:42:14.4281162Z Segment: GLOBAL; FLAGS: COARSE GRAINED 2025-07-17T08:42:14.4281414Z Size: 201310208(0xbffc000) KB 2025-07-17T08:42:14.4281658Z Allocatable: TRUE 2025-07-17T08:42:14.4281921Z Alloc Granule: 4KB 2025-07-17T08:42:14.4282199Z Alloc Recommended Granule:2048KB 2025-07-17T08:42:14.4282469Z Alloc Alignment: 4KB 2025-07-17T08:42:14.4282724Z Accessible by all: FALSE 2025-07-17T08:42:14.4282955Z Pool 2 2025-07-17T08:42:14.4283169Z Segment: GLOBAL; FLAGS: EXTENDED FINE GRAINED 2025-07-17T08:42:14.4283419Z Size: 201310208(0xbffc000) KB 2025-07-17T08:42:14.4283665Z Allocatable: TRUE 2025-07-17T08:42:14.4283915Z Alloc Granule: 4KB 2025-07-17T08:42:14.4284176Z Alloc Recommended Granule:2048KB 2025-07-17T08:42:14.4284438Z Alloc Alignment: 4KB 2025-07-17T08:42:14.4284698Z Accessible by all: FALSE 2025-07-17T08:42:14.4284925Z Pool 3 2025-07-17T08:42:14.4285130Z Segment: GLOBAL; FLAGS: FINE GRAINED 2025-07-17T08:42:14.4285362Z Size: 201310208(0xbffc000) KB 2025-07-17T08:42:14.4285601Z Allocatable: TRUE 2025-07-17T08:42:14.4285855Z Alloc Granule: 4KB 2025-07-17T08:42:14.4286106Z Alloc Recommended Granule:2048KB 2025-07-17T08:42:14.4286384Z Alloc Alignment: 4KB 2025-07-17T08:42:14.4286642Z Accessible by all: FALSE 2025-07-17T08:42:14.4286867Z Pool 4 2025-07-17T08:42:14.4287072Z Segment: GROUP 2025-07-17T08:42:14.4287306Z Size: 64(0x40) KB 2025-07-17T08:42:14.4287551Z Allocatable: FALSE 2025-07-17T08:42:14.4287792Z Alloc Granule: 0KB 2025-07-17T08:42:14.4288052Z Alloc Recommended Granule:0KB 2025-07-17T08:42:14.4288315Z Alloc Alignment: 0KB 2025-07-17T08:42:14.4288570Z Accessible by all: FALSE 2025-07-17T08:42:14.4288813Z ISA Info: 2025-07-17T08:42:14.4289005Z ISA 1 2025-07-17T08:42:14.4289441Z Name: amdgcn-amd-amdhsa--gfx942:sramecc+:xnack- 2025-07-17T08:42:14.4289728Z Machine Models: HSA_MACHINE_MODEL_LARGE 2025-07-17T08:42:14.4290002Z Profiles: HSA_PROFILE_BASE 2025-07-17T08:42:14.4290268Z Default Rounding Mode: NEAR 2025-07-17T08:42:14.4290646Z Default Rounding Mode: NEAR 2025-07-17T08:42:14.4290917Z Fast f16: TRUE 2025-07-17T08:42:14.4291177Z Workgroup Max Size: 1024(0x400) 2025-07-17T08:42:14.4291423Z Workgroup Max Size per Dimension: 2025-07-17T08:42:14.4291637Z x 1024(0x400) 2025-07-17T08:42:14.4291856Z y 1024(0x400) 2025-07-17T08:42:14.4292060Z z 1024(0x400) 2025-07-17T08:42:14.4292298Z Grid Max Size: 4294967295(0xffffffff) 2025-07-17T08:42:14.4292528Z Grid Max Size per Dimension: 2025-07-17T08:42:14.4292727Z x 4294967295(0xffffffff) 2025-07-17T08:42:14.4292947Z y 4294967295(0xffffffff) 2025-07-17T08:42:14.4293163Z z 4294967295(0xffffffff) 2025-07-17T08:42:14.4293411Z FBarrier Max Size: 32 2025-07-17T08:42:14.4293646Z ISA 2 2025-07-17T08:42:14.4293891Z Name: amdgcn-amd-amdhsa--gfx9-4-generic:sramecc+:xnack- 2025-07-17T08:42:14.4294184Z Machine Models: HSA_MACHINE_MODEL_LARGE 2025-07-17T08:42:14.4294442Z Profiles: HSA_PROFILE_BASE 2025-07-17T08:42:14.4294702Z Default Rounding Mode: NEAR 2025-07-17T08:42:14.4294964Z Default Rounding Mode: NEAR 2025-07-17T08:42:14.4295212Z Fast f16: TRUE 2025-07-17T08:42:14.4295454Z Workgroup Max Size: 1024(0x400) 2025-07-17T08:42:14.4295689Z Workgroup Max Size per Dimension: 2025-07-17T08:42:14.4295904Z x 1024(0x400) 2025-07-17T08:42:14.4296115Z y 1024(0x400) 2025-07-17T08:42:14.4296322Z z 1024(0x400) 2025-07-17T08:42:14.4296549Z Grid Max Size: 4294967295(0xffffffff) 2025-07-17T08:42:14.4296778Z Grid Max Size per Dimension: 2025-07-17T08:42:14.4296983Z x 4294967295(0xffffffff) 2025-07-17T08:42:14.4297203Z y 4294967295(0xffffffff) 2025-07-17T08:42:14.4297425Z z 4294967295(0xffffffff) 2025-07-17T08:42:14.4297663Z FBarrier Max Size: 32 2025-07-17T08:42:14.4297878Z ******* 2025-07-17T08:42:14.4298057Z Agent 4 2025-07-17T08:42:14.4298222Z ******* 2025-07-17T08:42:14.4298411Z Name: gfx942 2025-07-17T08:42:14.4298651Z Uuid: GPU-52dd8a4356753324 2025-07-17T08:42:14.4298899Z Marketing Name: AMD Instinct MI300X 2025-07-17T08:42:14.4299160Z Vendor Name: AMD 2025-07-17T08:42:14.4299406Z Feature: KERNEL_DISPATCH 2025-07-17T08:42:14.4299649Z Profile: BASE_PROFILE 2025-07-17T08:42:14.4299901Z Float Round Mode: NEAR 2025-07-17T08:42:14.4300154Z Max Queue Number: 128(0x80) 2025-07-17T08:42:14.4300551Z Queue Min Size: 64(0x40) 2025-07-17T08:42:14.4300798Z Queue Max Size: 131072(0x20000) 2025-07-17T08:42:14.4301033Z Queue Type: MULTI 2025-07-17T08:42:14.4301263Z Node: 3 2025-07-17T08:42:14.4301598Z Device Type: GPU 2025-07-17T08:42:14.4301827Z Cache Info: 2025-07-17T08:42:14.4302029Z L1: 32(0x20) KB 2025-07-17T08:42:14.4302254Z L2: 4096(0x1000) KB 2025-07-17T08:42:14.4302462Z L3: 262144(0x40000) KB 2025-07-17T08:42:14.4302684Z Chip ID: 29857(0x74a1) 2025-07-17T08:42:14.4302921Z ASIC Revision: 1(0x1) 2025-07-17T08:42:14.4303175Z Cacheline Size: 128(0x80) 2025-07-17T08:42:14.4303427Z Max Clock Freq. (MHz): 2100 2025-07-17T08:42:14.4303665Z BDFID: 58624 2025-07-17T08:42:14.4303900Z Internal Node ID: 3 2025-07-17T08:42:14.4304151Z Compute Unit: 304 2025-07-17T08:42:14.4304389Z SIMDs per CU: 4 2025-07-17T08:42:14.4304636Z Shader Engines: 32 2025-07-17T08:42:14.4304890Z Shader Arrs. per Eng.: 1 2025-07-17T08:42:14.4305155Z WatchPts on Addr. Ranges:4 2025-07-17T08:42:14.4305486Z Coherent Host Access: FALSE 2025-07-17T08:42:14.4305722Z Memory Properties: 2025-07-17T08:42:14.4305921Z Features: KERNEL_DISPATCH 2025-07-17T08:42:14.4306161Z Fast F16 Operation: TRUE 2025-07-17T08:42:14.4306417Z Wavefront Size: 64(0x40) 2025-07-17T08:42:14.4306677Z Workgroup Max Size: 1024(0x400) 2025-07-17T08:42:14.4306916Z Workgroup Max Size per Dimension: 2025-07-17T08:42:14.4307166Z x 1024(0x400) 2025-07-17T08:42:14.4307382Z y 1024(0x400) 2025-07-17T08:42:14.4307587Z z 1024(0x400) 2025-07-17T08:42:14.4307814Z Max Waves Per CU: 32(0x20) 2025-07-17T08:42:14.4308066Z Max Work-item Per CU: 2048(0x800) 2025-07-17T08:42:14.4308313Z Grid Max Size: 4294967295(0xffffffff) 2025-07-17T08:42:14.4308541Z Grid Max Size per Dimension: 2025-07-17T08:42:14.4308740Z x 4294967295(0xffffffff) 2025-07-17T08:42:14.4308954Z y 4294967295(0xffffffff) 2025-07-17T08:42:14.4309162Z z 4294967295(0xffffffff) 2025-07-17T08:42:14.4309405Z Max fbarriers/Workgrp: 32 2025-07-17T08:42:14.4309688Z Packet Processor uCode:: 177 2025-07-17T08:42:14.4309963Z SDMA engine uCode:: 24 2025-07-17T08:42:14.4310220Z IOMMU Support:: None 2025-07-17T08:42:14.4310442Z Pool Info: 2025-07-17T08:42:14.4310619Z Pool 1 2025-07-17T08:42:14.4310829Z Segment: GLOBAL; FLAGS: COARSE GRAINED 2025-07-17T08:42:14.4311081Z Size: 201310208(0xbffc000) KB 2025-07-17T08:42:14.4311326Z Allocatable: TRUE 2025-07-17T08:42:14.4311735Z Alloc Granule: 4KB 2025-07-17T08:42:14.4312012Z Alloc Recommended Granule:2048KB 2025-07-17T08:42:14.4312284Z Alloc Alignment: 4KB 2025-07-17T08:42:14.4312544Z Accessible by all: FALSE 2025-07-17T08:42:14.4312783Z Pool 2 2025-07-17T08:42:14.4313108Z Segment: GLOBAL; FLAGS: EXTENDED FINE GRAINED 2025-07-17T08:42:14.4313354Z Size: 201310208(0xbffc000) KB 2025-07-17T08:42:14.4313600Z Allocatable: TRUE 2025-07-17T08:42:14.4313848Z Alloc Granule: 4KB 2025-07-17T08:42:14.4314106Z Alloc Recommended Granule:2048KB 2025-07-17T08:42:14.4314361Z Alloc Alignment: 4KB 2025-07-17T08:42:14.4314621Z Accessible by all: FALSE 2025-07-17T08:42:14.4314849Z Pool 3 2025-07-17T08:42:14.4315041Z Segment: GLOBAL; FLAGS: FINE GRAINED 2025-07-17T08:42:14.4315276Z Size: 201310208(0xbffc000) KB 2025-07-17T08:42:14.4315506Z Allocatable: TRUE 2025-07-17T08:42:14.4315759Z Alloc Granule: 4KB 2025-07-17T08:42:14.4316017Z Alloc Recommended Granule:2048KB 2025-07-17T08:42:14.4316272Z Alloc Alignment: 4KB 2025-07-17T08:42:14.4316520Z Accessible by all: FALSE 2025-07-17T08:42:14.4316740Z Pool 4 2025-07-17T08:42:14.4316927Z Segment: GROUP 2025-07-17T08:42:14.4317148Z Size: 64(0x40) KB 2025-07-17T08:42:14.4317373Z Allocatable: FALSE 2025-07-17T08:42:14.4317613Z Alloc Granule: 0KB 2025-07-17T08:42:14.4317864Z Alloc Recommended Granule:0KB 2025-07-17T08:42:14.4318118Z Alloc Alignment: 0KB 2025-07-17T08:42:14.4318373Z Accessible by all: FALSE 2025-07-17T08:42:14.4318591Z ISA Info: 2025-07-17T08:42:14.4318757Z ISA 1 2025-07-17T08:42:14.4318965Z Name: amdgcn-amd-amdhsa--gfx942:sramecc+:xnack- 2025-07-17T08:42:14.4319228Z Machine Models: HSA_MACHINE_MODEL_LARGE 2025-07-17T08:42:14.4319485Z Profiles: HSA_PROFILE_BASE 2025-07-17T08:42:14.4319738Z Default Rounding Mode: NEAR 2025-07-17T08:42:14.4320006Z Default Rounding Mode: NEAR 2025-07-17T08:42:14.4320250Z Fast f16: TRUE 2025-07-17T08:42:14.4320486Z Workgroup Max Size: 1024(0x400) 2025-07-17T08:42:14.4320721Z Workgroup Max Size per Dimension: 2025-07-17T08:42:14.4320933Z x 1024(0x400) 2025-07-17T08:42:14.4321142Z y 1024(0x400) 2025-07-17T08:42:14.4321344Z z 1024(0x400) 2025-07-17T08:42:14.4321568Z Grid Max Size: 4294967295(0xffffffff) 2025-07-17T08:42:14.4321789Z Grid Max Size per Dimension: 2025-07-17T08:42:14.4321984Z x 4294967295(0xffffffff) 2025-07-17T08:42:14.4322189Z y 4294967295(0xffffffff) 2025-07-17T08:42:14.4322514Z z 4294967295(0xffffffff) 2025-07-17T08:42:14.4322736Z FBarrier Max Size: 32 2025-07-17T08:42:14.4322948Z ISA 2 2025-07-17T08:42:14.4323163Z Name: amdgcn-amd-amdhsa--gfx9-4-generic:sramecc+:xnack- 2025-07-17T08:42:14.4323462Z Machine Models: HSA_MACHINE_MODEL_LARGE 2025-07-17T08:42:14.4323833Z Profiles: HSA_PROFILE_BASE 2025-07-17T08:42:14.4324085Z Default Rounding Mode: NEAR 2025-07-17T08:42:14.4324339Z Default Rounding Mode: NEAR 2025-07-17T08:42:14.4324583Z Fast f16: TRUE 2025-07-17T08:42:14.4324833Z Workgroup Max Size: 1024(0x400) 2025-07-17T08:42:14.4325064Z Workgroup Max Size per Dimension: 2025-07-17T08:42:14.4325278Z x 1024(0x400) 2025-07-17T08:42:14.4325503Z y 1024(0x400) 2025-07-17T08:42:14.4325698Z z 1024(0x400) 2025-07-17T08:42:14.4325920Z Grid Max Size: 4294967295(0xffffffff) 2025-07-17T08:42:14.4326159Z Grid Max Size per Dimension: 2025-07-17T08:42:14.4326355Z x 4294967295(0xffffffff) 2025-07-17T08:42:14.4326564Z y 4294967295(0xffffffff) 2025-07-17T08:42:14.4326767Z z 4294967295(0xffffffff) 2025-07-17T08:42:14.4326988Z FBarrier Max Size: 32 2025-07-17T08:42:14.4327204Z *** Done *** 2025-07-17T08:42:14.4397888Z + rocminfo 2025-07-17T08:42:14.4398647Z + grep -E 'Name:.*\sgfx|Marketing' 2025-07-17T08:42:14.6682893Z Marketing Name: AMD EPYC 9534 64-Core Processor 2025-07-17T08:42:14.6683279Z Marketing Name: AMD EPYC 9534 64-Core Processor 2025-07-17T08:42:14.6683547Z Name: gfx942 2025-07-17T08:42:14.6683802Z Marketing Name: AMD Instinct MI300X 2025-07-17T08:42:14.6684053Z Name: gfx942 2025-07-17T08:42:14.6684331Z Marketing Name: AMD Instinct MI300X 2025-07-17T08:42:14.6835759Z + MAYBE_ROCM=rocm/ 2025-07-17T08:42:14.6836011Z + [[ linux-noble-rocm-py3.12-mi300 == *xpu* ]] 2025-07-17T08:42:14.6836274Z + [[ linux-noble-rocm-py3.12-mi300 != *-bazel-* ]] 2025-07-17T08:42:14.6836512Z + pip_install ninja==1.10.2 2025-07-17T08:42:14.6836764Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2025-07-17T08:42:14.6837063Z + python3 -m pip install --progress-bar off ninja==1.10.2 2025-07-17T08:42:14.9622474Z Collecting ninja==1.10.2 2025-07-17T08:42:15.2269611Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (5.0 kB) 2025-07-17T08:42:15.2379341Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (108 kB) 2025-07-17T08:42:15.4094562Z Installing collected packages: ninja 2025-07-17T08:42:15.4094870Z Attempting uninstall: ninja 2025-07-17T08:42:15.4123073Z Found existing installation: ninja 1.11.1.3 2025-07-17T08:42:15.4145165Z Uninstalling ninja-1.11.1.3: 2025-07-17T08:42:15.4172742Z Successfully uninstalled ninja-1.11.1.3 2025-07-17T08:42:15.4352249Z Successfully installed ninja-1.10.2 2025-07-17T08:42:15.4906080Z + export PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/opt/rocm/llvm/bin:/opt/rocm/opencl/bin:/opt/rocm/hip/bin:/opt/rocm/hcc/bin:/opt/rocm/bin:/opt/conda/envs/py_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2025-07-17T08:42:15.4907401Z + PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/opt/rocm/llvm/bin:/opt/rocm/opencl/bin:/opt/rocm/hip/bin:/opt/rocm/hcc/bin:/opt/rocm/bin:/opt/conda/envs/py_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2025-07-17T08:42:15.4908597Z + [[ linux-noble-rocm-py3.12-mi300 == *aarch64* ]] 2025-07-17T08:42:15.4908846Z + [[ linux-noble-rocm-py3.12-mi300 == *asan* ]] 2025-07-17T08:42:15.4909080Z + [[ linux-noble-rocm-py3.12-mi300 == *-debug* ]] 2025-07-17T08:42:15.4909562Z + [[ linux-noble-rocm-py3.12-mi300 != *-bazel-* ]] 2025-07-17T08:42:15.4909900Z + echo 'We are not in debug mode: linux-noble-rocm-py3.12-mi300. Expect the assertion to pass' 2025-07-17T08:42:15.4910305Z We are not in debug mode: linux-noble-rocm-py3.12-mi300. Expect the assertion to pass 2025-07-17T08:42:15.4910697Z + cd test 2025-07-17T08:42:15.4911803Z + python -c 'import torch; torch._C._crash_if_debug_asserts_fail(424242)' 2025-07-17T08:42:18.4159322Z + [[ default == \n\o\g\p\u\_\N\O\_\A\V\X\2 ]] 2025-07-17T08:42:18.4159649Z + [[ default == \n\o\g\p\u\_\A\V\X\5\1\2 ]] 2025-07-17T08:42:18.4159962Z + [[ default == \l\e\g\a\c\y\_\n\v\i\d\i\a\_\d\r\i\v\e\r ]] 2025-07-17T08:42:18.4160246Z + DYNAMO_BENCHMARK_FLAGS=() 2025-07-17T08:42:18.4160444Z + [[ default == *pr_time_benchmarks* ]] 2025-07-17T08:42:18.4160676Z + [[ default == *dynamo_eager* ]] 2025-07-17T08:42:18.4160878Z + [[ default == *aot_eager* ]] 2025-07-17T08:42:18.4161075Z + [[ default == *aot_inductor* ]] 2025-07-17T08:42:18.4161285Z + [[ default == *max_autotune_inductor* ]] 2025-07-17T08:42:18.4161517Z + [[ default == *inductor* ]] 2025-07-17T08:42:18.4161710Z + [[ default == *dynamic* ]] 2025-07-17T08:42:18.4161909Z + [[ default == *cpu* ]] 2025-07-17T08:42:18.4162143Z + DYNAMO_BENCHMARK_FLAGS+=(--device cuda) 2025-07-17T08:42:18.4177064Z + [[ linux-noble-rocm-py3.12-mi300 == *libtorch* ]] 2025-07-17T08:42:18.4177389Z + [[ linux-noble-rocm-py3.12-mi300 == *-bazel-* ]] 2025-07-17T08:42:18.4181957Z + cd test 2025-07-17T08:42:18.4182536Z + python -c 'import torch; print(torch.__config__.show())' 2025-07-17T08:42:19.6669730Z PyTorch built with: 2025-07-17T08:42:19.6670041Z - GCC 11.4 2025-07-17T08:42:19.6670222Z - C++ Version: 201703 2025-07-17T08:42:19.6670632Z - Intel(R) oneAPI Math Kernel Library Version 2024.2-Product Build 20240605 for Intel(R) 64 architecture applications 2025-07-17T08:42:19.6671085Z - Intel(R) MKL-DNN v3.7.1 (Git Hash 8d263e693366ef8db40acc569cc7d8edf644556d) 2025-07-17T08:42:19.6671360Z - OpenMP 201511 (a.k.a. OpenMP 4.5) 2025-07-17T08:42:19.6671646Z - LAPACK is enabled (usually provided by MKL) 2025-07-17T08:42:19.6671864Z - NNPACK is enabled 2025-07-17T08:42:19.6672056Z - CPU capability usage: AVX512 2025-07-17T08:42:19.6672251Z - HIP Runtime 6.4.43483 2025-07-17T08:42:19.6672439Z - MIOpen 3.4.0 2025-07-17T08:42:19.6672604Z - Magma 2.7.2 2025-07-17T08:42:19.6675228Z - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, COMMIT_SHA=a38f433be2e94a64b095a44ba39879d02d0c2316, CXX_COMPILER=/opt/cache/bin/c++, CXX_FLAGS= -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -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=range-loop-construct -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-unknown-pragmas -Wno-unused-parameter -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=old-style-cast -faligned-new -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, 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=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=ON, USE_ROCM_KERNEL_ASSERT=OFF, USE_XCCL=OFF, USE_XPU=OFF, 2025-07-17T08:42:19.6678079Z 2025-07-17T08:42:19.9438474Z + cd test 2025-07-17T08:42:21.6432591Z + python -c 'import torch; print(torch.__config__.parallel_info())' 2025-07-17T08:42:21.6433686Z ATen/Parallel: 2025-07-17T08:42:21.6433944Z at::get_num_threads() : 128 2025-07-17T08:42:21.6434208Z at::get_num_interop_threads() : 128 2025-07-17T08:42:21.6434533Z OpenMP 201511 (a.k.a. OpenMP 4.5) 2025-07-17T08:42:21.6434855Z omp_get_max_threads() : 128 2025-07-17T08:42:21.6435748Z Intel(R) oneAPI Math Kernel Library Version 2024.2-Product Build 20240605 for Intel(R) 64 architecture applications 2025-07-17T08:42:21.6436229Z mkl_get_max_threads() : 128 2025-07-17T08:42:21.6436543Z Intel(R) MKL-DNN v3.7.1 (Git Hash 8d263e693366ef8db40acc569cc7d8edf644556d) 2025-07-17T08:42:21.6436894Z std::thread::hardware_concurrency() : 256 2025-07-17T08:42:21.6437154Z Environment variables: 2025-07-17T08:42:21.6437370Z OMP_NUM_THREADS : [not set] 2025-07-17T08:42:21.6437591Z MKL_NUM_THREADS : [not set] 2025-07-17T08:42:21.6437814Z ATen parallel backend: OpenMP 2025-07-17T08:42:21.6437958Z 2025-07-17T08:42:22.1444015Z + [[ default == *numpy_2* ]] 2025-07-17T08:42:22.1444398Z + [[ linux-noble-rocm-py3.12-mi300 == *aarch64* ]] 2025-07-17T08:42:22.1444669Z + [[ default == *backward* ]] 2025-07-17T08:42:22.1444869Z + [[ default == *xla* ]] 2025-07-17T08:42:22.1445061Z + [[ default == *executorch* ]] 2025-07-17T08:42:22.1445261Z + [[ default == \j\i\t\_\l\e\g\a\c\y ]] 2025-07-17T08:42:22.1445500Z + [[ linux-noble-rocm-py3.12-mi300 == *libtorch* ]] 2025-07-17T08:42:22.1445734Z + [[ default == distributed ]] 2025-07-17T08:42:22.1445952Z + [[ default == *operator_benchmark* ]] 2025-07-17T08:42:22.1446173Z + [[ default == *inductor_distributed* ]] 2025-07-17T08:42:22.1446405Z + [[ default == *inductor-halide* ]] 2025-07-17T08:42:22.1446618Z + [[ default == *inductor-triton-cpu* ]] 2025-07-17T08:42:22.1446861Z + [[ default == *inductor-micro-benchmark* ]] 2025-07-17T08:42:22.1447095Z + [[ default == *huggingface* ]] 2025-07-17T08:42:22.1447294Z + [[ default == *timm* ]] 2025-07-17T08:42:22.1447480Z + [[ default == cachebench ]] 2025-07-17T08:42:22.1447707Z + [[ default == verify_cachebench ]] 2025-07-17T08:42:22.1447916Z + [[ default == *torchbench* ]] 2025-07-17T08:42:22.1448124Z + [[ default == *inductor_cpp_wrapper* ]] 2025-07-17T08:42:22.1448335Z + [[ default == *inductor* ]] 2025-07-17T08:42:22.1448511Z + [[ default == *einops* ]] 2025-07-17T08:42:22.1448703Z + [[ default == *dynamo_wrapped* ]] 2025-07-17T08:42:22.1448930Z + [[ linux-noble-rocm-py3.12-mi300 == *rocm* ]] 2025-07-17T08:42:22.1449163Z + [[ -n '' ]] 2025-07-17T08:42:22.1449318Z + [[ 1 == 1 ]] 2025-07-17T08:42:22.1449476Z + [[ 6 -gt 1 ]] 2025-07-17T08:42:22.1449661Z + test_lazy_tensor_meta_reference_disabled 2025-07-17T08:42:22.1449936Z + export TORCH_DISABLE_FUNCTIONALIZATION_META_REFERENCE=1 2025-07-17T08:42:22.1450214Z + TORCH_DISABLE_FUNCTIONALIZATION_META_REFERENCE=1 2025-07-17T08:42:22.1450501Z + echo 'Testing lazy tensor operations without meta reference' 2025-07-17T08:42:22.1450796Z Testing lazy tensor operations without meta reference 2025-07-17T08:42:22.1451119Z + python test/run_test.py --include lazy/test_ts_opinfo.py --verbose 2025-07-17T08:42:24.7558539Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:42:24.7559627Z import pkg_resources 2025-07-17T08:42:26.0214151Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/pytorch/test/.pytorch-disabled-tests.json 2025-07-17T08:42:26.6276015Z Ignoring disabled issues: [''] 2025-07-17T08:42:26.6474086Z Found test times from artifacts 2025-07-17T08:42:26.7251542Z Found test times from artifacts 2025-07-17T08:42:26.7278222Z Running all tests 2025-07-17T08:42:26.7282404Z Running parallel tests on 2 processes 2025-07-17T08:42:26.7282699Z Name: tests to run (est. time: 0.01min) 2025-07-17T08:42:26.7283673Z Serial tests (0): 2025-07-17T08:42:26.7283855Z Parallel tests (1): 2025-07-17T08:42:26.7284037Z lazy/test_ts_opinfo 1/1 2025-07-17T08:42:26.7284244Z Name: excluded (est. time: 0.0min) 2025-07-17T08:42:26.7284445Z Serial tests (0): 2025-07-17T08:42:26.7284611Z Parallel tests (0): 2025-07-17T08:42:26.7284856Z Running lazy/test_ts_opinfo 1/1 ... [2025-07-17 08:42:26.728301] 2025-07-17T08:42:26.7285129Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T08:42:26.7287709Z Executing ['/opt/conda/envs/py_3.12/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 08:42:26.728601] 2025-07-17T08:42:31.3282717Z 2025-07-17T08:42:31.3283767Z 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_15c2ce6ff5e3b94d_.log 2025-07-17T08:42:31.3284280Z Running 0 items in this shard: 2025-07-17T08:42:31.3284470Z 2025-07-17T08:42:31.3284645Z GITHUB_RUN_ID, GITHUB_RUN_ATTEMPT, or ARTIFACTS_FILE_SUFFIX not set, not uploading 2025-07-17T08:42:31.3284959Z Uploading artifacts took 0.00 seconds 2025-07-17T08:42:34.1242730Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:42:34.1243724Z import pkg_resources 2025-07-17T08:42:34.1244582Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:42:34.1245418Z import pkg_resources 2025-07-17T08:42:34.4787632Z Running lazy/test_ts_opinfo 1/1 ... [2025-07-17 08:42:34.478222] 2025-07-17T08:42:34.4787989Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T08:42:34.4788734Z Executing ['/opt/conda/envs/py_3.12/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 08:42:34.478601] 2025-07-17T08:42:38.9088463Z 2025-07-17T08:42:38.9089427Z 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_31c7ebc879d1aa0a_.log 2025-07-17T08:42:38.9090777Z Running 5 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::TestLazyDynamicOps::test_adaptiveavgpool3d_dynamic, test/lazy/test_ts_opinfo.py::TestLazyDynamicOps::test_nonzero_dynamic 2025-07-17T08:42:38.9091783Z 2025-07-17T08:42:39.4536701Z Running test batch 'tests to run' cost 12.73 seconds 2025-07-17T08:42:40.0371909Z 2025-07-17T08:42:40.0372607Z real 0m17.892s 2025-07-17T08:42:40.0372952Z user 0m34.719s 2025-07-17T08:42:40.0373186Z sys 0m34.505s 2025-07-17T08:42:40.0373548Z + export -n TORCH_DISABLE_FUNCTIONALIZATION_META_REFERENCE 2025-07-17T08:42:40.0374006Z + test_without_numpy 2025-07-17T08:42:40.0375729Z ++ dirname .ci/pytorch/test.sh 2025-07-17T08:42:40.0389622Z + pushd .ci/pytorch 2025-07-17T08:42:40.0389952Z ~/pytorch/.ci/pytorch ~/pytorch 2025-07-17T08:42:40.0390761Z + 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())' 2025-07-17T08:42:40.7502008Z /opt/conda/envs/py_3.12/lib/python3.12/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.) 2025-07-17T08:42:40.7503721Z cpu = _conversion_method_template(device=torch.device("cpu")) 2025-07-17T08:42:41.4580788Z + python -c 'import sys;sys.path.insert(0, '\''fake_numpy'\'');import torch;print(torch.tensor([torch.tensor(0.), torch.tensor(1.)]))' 2025-07-17T08:42:42.1589901Z /opt/conda/envs/py_3.12/lib/python3.12/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.) 2025-07-17T08:42:42.1590843Z cpu = _conversion_method_template(device=torch.device("cpu")) 2025-07-17T08:42:42.4664254Z tensor([0., 1.]) 2025-07-17T08:42:42.8584995Z + [[ default == *dynamo_wrapped* ]] 2025-07-17T08:42:42.8585367Z + popd 2025-07-17T08:42:42.8585601Z ~/pytorch 2025-07-17T08:42:42.8585771Z + install_torchvision 2025-07-17T08:42:42.8585941Z + local orig_preload 2025-07-17T08:42:42.8586111Z + local commit 2025-07-17T08:42:42.8589674Z ++ get_pinned_commit vision 2025-07-17T08:42:42.8589939Z ++ cat .github/ci_commit_pins/vision.txt 2025-07-17T08:42:42.8608674Z + commit=966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-07-17T08:42:42.8608958Z + orig_preload= 2025-07-17T08:42:42.8609139Z + '[' -n '' ']' 2025-07-17T08:42:42.8609543Z + pip_install --no-use-pep517 git+https://github.com/pytorch/vision.git@966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-07-17T08:42:42.8609988Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2025-07-17T08:42:42.8610466Z + python3 -m pip install --progress-bar off --no-use-pep517 git+https://github.com/pytorch/vision.git@966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-07-17T08:42:43.0947571Z Collecting git+https://github.com/pytorch/vision.git@966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-07-17T08:42:43.0960457Z Cloning https://github.com/pytorch/vision.git (to revision 966da7e46f65d6d49df3e31214470a4fe5cc8e66) to /tmp/pip-req-build-6c9bq34x 2025-07-17T08:42:43.1002723Z Running command git clone --filter=blob:none --quiet https://github.com/pytorch/vision.git /tmp/pip-req-build-6c9bq34x 2025-07-17T08:42:45.3885799Z Running command git rev-parse -q --verify 'sha^966da7e46f65d6d49df3e31214470a4fe5cc8e66' 2025-07-17T08:42:45.3909909Z Running command git fetch -q https://github.com/pytorch/vision.git 966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-07-17T08:42:45.6756865Z Running command git checkout -q 966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-07-17T08:42:46.1270990Z Resolved https://github.com/pytorch/vision.git to commit 966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-07-17T08:42:48.3312890Z Preparing metadata (setup.py) ... [?25l- \ | / done 2025-07-17T08:42:48.3339729Z [?25hRequirement already satisfied: numpy in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torchvision==0.22.0a0+966da7e) (1.26.2) 2025-07-17T08:42:48.3341599Z Requirement already satisfied: torch in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torchvision==0.22.0a0+966da7e) (2.9.0a0+gita38f433) 2025-07-17T08:42:48.3343797Z Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torchvision==0.22.0a0+966da7e) (11.0.0) 2025-07-17T08:42:48.3389303Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.22.0a0+966da7e) (3.18.0) 2025-07-17T08:42:48.3392184Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.22.0a0+966da7e) (4.14.1) 2025-07-17T08:42:48.3399997Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.22.0a0+966da7e) (80.9.0) 2025-07-17T08:42:48.3402908Z Requirement already satisfied: sympy>=1.13.3 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.22.0a0+966da7e) (1.13.3) 2025-07-17T08:42:48.3404885Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.22.0a0+966da7e) (2.8.8) 2025-07-17T08:42:48.3407365Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.22.0a0+966da7e) (3.1.6) 2025-07-17T08:42:48.3409529Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.22.0a0+966da7e) (2025.5.1) 2025-07-17T08:42:48.3419350Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from sympy>=1.13.3->torch->torchvision==0.22.0a0+966da7e) (1.3.0) 2025-07-17T08:42:48.3499750Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from jinja2->torch->torchvision==0.22.0a0+966da7e) (3.0.2) 2025-07-17T08:42:48.3557741Z Building wheels for collected packages: torchvision 2025-07-17T08:42:48.3624651Z  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 2025-07-17T08:43:34.2814323Z  Building wheel for torchvision (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - done 2025-07-17T08:43:34.2836257Z [?25h Created wheel for torchvision: filename=torchvision-0.22.0a0+966da7e-cp312-cp312-linux_x86_64.whl size=1571700 sha256=f717c0855a1bf3986c91d82b389c3417b55c277134c01fc93944bf02ed3df050 2025-07-17T08:43:34.2837119Z Stored in directory: /var/lib/jenkins/.cache/pip/wheels/10/ba/61/eb5228b3631dc6bb4f478b3aa59575551a5473e4596e4c001a 2025-07-17T08:43:34.2868003Z Successfully built torchvision 2025-07-17T08:43:34.4352180Z Installing collected packages: torchvision 2025-07-17T08:43:34.7998333Z Successfully installed torchvision-0.22.0a0+966da7e 2025-07-17T08:43:34.9541402Z + '[' -n '' ']' 2025-07-17T08:43:34.9541646Z + test_python_shard 1 2025-07-17T08:43:34.9541877Z + [[ -z 6 ]] 2025-07-17T08:43:34.9542342Z + python test/run_test.py --exclude-jit-executor --exclude-distributed-tests --shard 1 6 --verbose --upload-artifacts-while-running 2025-07-17T08:43:37.4254312Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:43:37.4255308Z import pkg_resources 2025-07-17T08:43:37.8714648Z Excluding test_cuda_nvml_based_avail on ROCm 2025-07-17T08:43:37.9266781Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/pytorch/test/.pytorch-disabled-tests.json 2025-07-17T08:43:37.9465845Z Found test times from artifacts 2025-07-17T08:43:38.0239982Z Found test times from artifacts 2025-07-17T08:43:38.0269329Z Running all tests 2025-07-17T08:43:38.0524841Z Running parallel tests on 2 processes 2025-07-17T08:43:38.0526131Z Name: tests to run (est. time: 103.97min) 2025-07-17T08:43:38.0526356Z Serial tests (52): 2025-07-17T08:43:38.0526591Z test_namedtuple_return_api 1/1 2025-07-17T08:43:38.0526808Z test_nn 1/1 2025-07-17T08:43:38.0526994Z distributions/test_distributions 1/1 2025-07-17T08:43:38.0527226Z test_spectral_ops 1/1 2025-07-17T08:43:38.0527427Z inductor/test_max_autotune 1/2 2025-07-17T08:43:38.0527634Z inductor/test_max_autotune 2/2 2025-07-17T08:43:38.0528499Z doctests 1/1 2025-07-17T08:43:38.0528704Z dynamo/test_fake_distributed 1/1 2025-07-17T08:43:38.0528913Z inductor/test_benchmark_fusion 1/1 2025-07-17T08:43:38.0529124Z inductor/test_cutlass_backend 1/1 2025-07-17T08:43:38.0529339Z inductor/test_distributed_patterns 1/1 2025-07-17T08:43:38.0529564Z inductor/test_flex_attention 1/5 2025-07-17T08:43:38.0529761Z inductor/test_flex_attention 2/5 2025-07-17T08:43:38.0530219Z inductor/test_flex_attention 3/5 2025-07-17T08:43:38.0530413Z inductor/test_flex_attention 4/5 2025-07-17T08:43:38.0530617Z inductor/test_flex_attention 5/5 2025-07-17T08:43:38.0530832Z nn/test_convolution 1/1 2025-07-17T08:43:38.0531025Z nn/test_pooling 1/1 2025-07-17T08:43:38.0531207Z test_autocast 1/1 2025-07-17T08:43:38.0531391Z test_autograd_fallback 1/1 2025-07-17T08:43:38.0531589Z test_autoload_disable 1/1 2025-07-17T08:43:38.0531779Z test_autoload_enable 1/1 2025-07-17T08:43:38.0531971Z test_ci_sanity_check_fail 1/1 2025-07-17T08:43:38.0532180Z test_cpp_api_parity 1/1 2025-07-17T08:43:38.0532373Z test_cpp_extensions_aot_ninja 1/1 2025-07-17T08:43:38.0532578Z test_cpp_extensions_aot_no_ninja 1/1 2025-07-17T08:43:38.0532789Z test_cpp_extensions_jit 1/1 2025-07-17T08:43:38.0532997Z test_cpp_extensions_mtia_backend 1/1 2025-07-17T08:43:38.0533241Z test_cpp_extensions_open_device_registration 1/1 2025-07-17T08:43:38.0533491Z test_cpp_extensions_stream_and_event 1/1 2025-07-17T08:43:38.0533712Z test_cuda_primary_ctx 1/1 2025-07-17T08:43:38.0533905Z test_cuda_trace 1/1 2025-07-17T08:43:38.0534079Z test_dispatch 1/1 2025-07-17T08:43:38.0534258Z test_extension_utils 1/1 2025-07-17T08:43:38.0534448Z test_fake_tensor 1/1 2025-07-17T08:43:38.0534613Z test_fx 1/1 2025-07-17T08:43:38.0534782Z test_jit_disabled 1/1 2025-07-17T08:43:38.0534965Z test_mobile_optimizer 1/1 2025-07-17T08:43:38.0535158Z test_multiprocessing 1/1 2025-07-17T08:43:38.0535348Z test_multiprocessing_spawn 1/1 2025-07-17T08:43:38.0535553Z test_native_mha 1/1 2025-07-17T08:43:38.0535720Z test_openreg 1/1 2025-07-17T08:43:38.0535884Z test_overrides 1/1 2025-07-17T08:43:38.0536057Z test_python_dispatch 1/1 2025-07-17T08:43:38.0536237Z test_reductions 1/1 2025-07-17T08:43:38.0536394Z test_show_pickle 1/1 2025-07-17T08:43:38.0536574Z test_sort_and_select 1/1 2025-07-17T08:43:38.0536762Z test_tensor_creation_ops 1/1 2025-07-17T08:43:38.0536973Z test_tensorexpr 1/1 2025-07-17T08:43:38.0537161Z test_torch 1/1 2025-07-17T08:43:38.0537340Z test_transformers_privateuse1 1/1 2025-07-17T08:43:38.0537565Z test_utils 1/1 2025-07-17T08:43:38.0537726Z Parallel tests (22): 2025-07-17T08:43:38.0537916Z inductor/test_cpu_cpp_wrapper 1/1 2025-07-17T08:43:38.0538127Z inductor/test_autoheuristic 1/1 2025-07-17T08:43:38.0538340Z inductor/test_minifier_isolate 1/1 2025-07-17T08:43:38.0538546Z optim/test_optim 1/1 2025-07-17T08:43:38.0538737Z test_import_stats 1/1 2025-07-17T08:43:38.0538915Z test_indexing 1/1 2025-07-17T08:43:38.0539088Z test_jit_autocast 1/1 2025-07-17T08:43:38.0539261Z test_jiterator 1/1 2025-07-17T08:43:38.0539442Z test_legacy_vmap 1/1 2025-07-17T08:43:38.0539611Z test_license 1/1 2025-07-17T08:43:38.0539771Z test_logging 1/1 2025-07-17T08:43:38.0539927Z test_masked 1/1 2025-07-17T08:43:38.0540097Z test_maskedtensor 1/1 2025-07-17T08:43:38.0540282Z test_matmul_cuda 1/1 2025-07-17T08:43:38.0540461Z test_monitor 1/1 2025-07-17T08:43:38.0540624Z test_namedtensor 1/1 2025-07-17T08:43:38.0540796Z test_native_functions 1/1 2025-07-17T08:43:38.0540992Z test_numba_integration 1/1 2025-07-17T08:43:38.0541186Z test_numpy_interop 1/1 2025-07-17T08:43:38.0541370Z test_openmp 1/1 2025-07-17T08:43:38.0541537Z test_ops_fwd_gradients 1/1 2025-07-17T08:43:38.0541724Z xpu/test_gemm 1/1 2025-07-17T08:43:38.0541905Z Name: excluded (est. time: 0.0min) 2025-07-17T08:43:38.0542106Z Serial tests (0): 2025-07-17T08:43:38.0542438Z Parallel tests (0): 2025-07-17T08:43:38.0542684Z Running test_namedtuple_return_api 1/1 ... [2025-07-17 08:43:38.053201] 2025-07-17T08:43:38.0542976Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T08:43:38.0543765Z Executing ['/opt/conda/envs/py_3.12/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 08:43:38.053571] 2025-07-17T08:43:42.4279331Z 2025-07-17T08:43:42.4280307Z 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_ccdfcefff518a3ff_.log 2025-07-17T08:43:42.4281440Z 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 2025-07-17T08:43:42.4282182Z 2025-07-17T08:43:42.4282362Z GITHUB_RUN_ID, GITHUB_RUN_ATTEMPT, or ARTIFACTS_FILE_SUFFIX not set, not uploading 2025-07-17T08:43:42.4282694Z Uploading artifacts took 0.00 seconds 2025-07-17T08:43:42.4285653Z Running test_nn 1/1 ... [2025-07-17 08:43:42.428143] 2025-07-17T08:43:42.4285906Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T08:43:42.4286532Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_nn.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:43:42.428451] 2025-07-17T08:51:36.0196527Z 2025-07-17T08:51:36.0197563Z test_nn 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_nn_1.1_f67be8137186acbd_.log 2025-07-17T08:51:36.0914741Z Running 2384 items in this shard: test/test_nn.py::TestNN::test_AdaptiveLogSoftmax, test/test_nn.py::TestNN::test_AdaptiveLogSoftmax_cuda_fp32, test/test_nn.py::TestNN::test_AdaptiveLogSoftmax_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_sum_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_none_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_Conv1d_circular_stride2_pad2_cuda_tf32, test/test_nn.py::TestNN::test_Conv1d_cuda_fp32, test/test_nn.py::TestNN::test_Conv1d_cuda_tf32, test/test_nn.py::TestNN::test_Conv1d_dilated, test/test_nn.py::TestNN::test_Conv1d_dilated_cuda_fp32, test/test_nn.py::TestNN::test_Conv1d_dilated_cuda_tf32, test/test_nn.py::TestNN::test_Conv1d_groups, test/test_nn.py::TestNN::test_Conv1d_groups_cuda_fp32, test/test_nn.py::TestNN::test_Conv1d_groups_cuda_tf32, test/test_nn.py::TestNN::test_Conv1d_pad1, test/test_nn.py::TestNN::test_Conv1d_pad1_cuda_fp32, test/test_nn.py::TestNN::test_Conv1d_pad1_cuda_tf32, test/test_nn.py::TestNN::test_Conv1d_pad1size1, test/test_nn.py::TestNN::test_Conv1d_pad1size1_cuda_fp32, test/test_nn.py::TestNN::test_Conv1d_pad1size1_cuda_tf32, test/test_nn.py::TestNN::test_Conv1d_pad2, test/test_nn.py::TestNN::test_Conv1d_pad2_cuda_fp32, test/test_nn.py::TestNN::test_Conv1d_pad2_cuda_tf32, test/test_nn.py::TestNN::test_Conv1d_pad2size1, test/test_nn.py::TestNN::test_Conv1d_pad2size1_cuda_fp32, test/test_nn.py::TestNN::test_Conv1d_pad2size1_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_Conv1d_pad_same2_cuda_tf32, test/test_nn.py::TestNN::test_Conv1d_pad_same_cuda_fp32, test/test_nn.py::TestNN::test_Conv1d_pad_same_cuda_tf32, test/test_nn.py::TestNN::test_Conv1d_pad_same_dilated, test/test_nn.py::TestNN::test_Conv1d_pad_same_dilated_cuda_fp32, test/test_nn.py::TestNN::test_Conv1d_pad_same_dilated_cuda_tf32, test/test_nn.py::TestNN::test_Conv1d_pad_valid, test/test_nn.py::TestNN::test_Conv1d_pad_valid_cuda_fp32, test/test_nn.py::TestNN::test_Conv1d_pad_valid_cuda_tf32, test/test_nn.py::TestNN::test_Conv1d_reflect_stride2_pad2, test/test_nn.py::TestNN::test_Conv1d_reflect_stride2_pad2_cuda_fp32, test/test_nn.py::TestNN::test_Conv1d_reflect_stride2_pad2_cuda_tf32, test/test_nn.py::TestNN::test_Conv1d_replicate_stride2_pad2, test/test_nn.py::TestNN::test_Conv1d_replicate_stride2_pad2_cuda_fp32, test/test_nn.py::TestNN::test_Conv1d_replicate_stride2_pad2_cuda_tf32, test/test_nn.py::TestNN::test_Conv1d_stride, test/test_nn.py::TestNN::test_Conv1d_stride_cuda_fp32, test/test_nn.py::TestNN::test_Conv1d_stride_cuda_tf32, test/test_nn.py::TestNN::test_Conv1d_zero_batch, test/test_nn.py::TestNN::test_Conv1d_zero_batch_cuda_fp32, test/test_nn.py::TestNN::test_Conv1d_zero_batch_cuda_tf32, test/test_nn.py::TestNN::test_Conv1d_zeros_stride2_pad2, test/test_nn.py::TestNN::test_Conv1d_zeros_stride2_pad2_cuda_fp32, test/test_nn.py::TestNN::test_Conv1d_zeros_stride2_pad2_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_Conv2d_circular_stride2_pad2_cuda_tf32, test/test_nn.py::TestNN::test_Conv2d_cuda_fp32, test/test_nn.py::TestNN::test_Conv2d_cuda_tf32, test/test_nn.py::TestNN::test_Conv2d_depthwise, test/test_nn.py::TestNN::test_Conv2d_depthwise_cuda_fp32, test/test_nn.py::TestNN::test_Conv2d_depthwise_cuda_tf32, test/test_nn.py::TestNN::test_Conv2d_depthwise_dilated, test/test_nn.py::TestNN::test_Conv2d_depthwise_dilated_cuda_fp32, test/test_nn.py::TestNN::test_Conv2d_depthwise_dilated_cuda_tf32, test/test_nn.py::TestNN::test_Conv2d_depthwise_padded, test/test_nn.py::TestNN::test_Conv2d_depthwise_padded_cuda_fp32, test/test_nn.py::TestNN::test_Conv2d_depthwise_padded_cuda_tf32, test/test_nn.py::TestNN::test_Conv2d_depthwise_strided, test/test_nn.py::TestNN::test_Conv2d_depthwise_strided_cuda_fp32, test/test_nn.py::TestNN::test_Conv2d_depthwise_strided_cuda_tf32, test/test_nn.py::TestNN::test_Conv2d_depthwise_with_multiplier, test/test_nn.py::TestNN::test_Conv2d_depthwise_with_multiplier_cuda_fp32, test/test_nn.py::TestNN::test_Conv2d_depthwise_with_multiplier_cuda_tf32, test/test_nn.py::TestNN::test_Conv2d_dilated, test/test_nn.py::TestNN::test_Conv2d_dilated_cuda_fp32, test/test_nn.py::TestNN::test_Conv2d_dilated_cuda_tf32, test/test_nn.py::TestNN::test_Conv2d_dilated_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_dilated_with_long_tensor_cuda_fp32, test/test_nn.py::TestNN::test_Conv2d_dilated_with_long_tensor_cuda_tf32, test/test_nn.py::TestNN::test_Conv2d_groups, test/test_nn.py::TestNN::test_Conv2d_groups_cuda_fp32, test/test_nn.py::TestNN::test_Conv2d_groups_cuda_tf32, test/test_nn.py::TestNN::test_Conv2d_groups_thnn, test/test_nn.py::TestNN::test_Conv2d_groups_thnn_cuda_fp32, test/test_nn.py::TestNN::test_Conv2d_groups_thnn_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_Conv2d_groups_thnn_with_long_tensor_cuda_tf32, test/test_nn.py::TestNN::test_Conv2d_groups_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_groups_with_long_tensor_cuda_fp32, test/test_nn.py::TestNN::test_Conv2d_groups_with_long_tensor_cuda_tf32, test/test_nn.py::TestNN::test_Conv2d_no_bias, test/test_nn.py::TestNN::test_Conv2d_no_bias_cuda_fp32, test/test_nn.py::TestNN::test_Conv2d_no_bias_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_Conv2d_no_bias_with_long_tensor_cuda_tf32, test/test_nn.py::TestNN::test_Conv2d_pad_same, test/test_nn.py::TestNN::test_Conv2d_pad_same_cuda_fp32, test/test_nn.py::TestNN::test_Conv2d_pad_same_cuda_tf32, test/test_nn.py::TestNN::test_Conv2d_pad_same_dilated, test/test_nn.py::TestNN::test_Conv2d_pad_same_dilated_cuda_fp32, test/test_nn.py::TestNN::test_Conv2d_pad_same_dilated_cuda_tf32, test/test_nn.py::TestNN::test_Conv2d_pad_valid, test/test_nn.py::TestNN::test_Conv2d_pad_valid_cuda_fp32, test/test_nn.py::TestNN::test_Conv2d_pad_valid_cuda_tf32, test/test_nn.py::TestNN::test_Conv2d_padding, test/test_nn.py::TestNN::test_Conv2d_padding_cuda_fp32, test/test_nn.py::TestNN::test_Conv2d_padding_cuda_tf32, test/test_nn.py::TestNN::test_Conv2d_padding_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_padding_with_long_tensor_cuda_fp32, test/test_nn.py::TestNN::test_Conv2d_padding_with_long_tensor_cuda_tf32, test/test_nn.py::TestNN::test_Conv2d_reflect_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_reflect_stride2_pad2_cuda_fp32, test/test_nn.py::TestNN::test_Conv2d_reflect_stride2_pad2_cuda_tf32, test/test_nn.py::TestNN::test_Conv2d_replicate_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_replicate_stride2_pad2_cuda_fp32, test/test_nn.py::TestNN::test_Conv2d_replicate_stride2_pad2_cuda_tf32, test/test_nn.py::TestNN::test_Conv2d_strided, test/test_nn.py::TestNN::test_Conv2d_strided_cuda_fp32, test/test_nn.py::TestNN::test_Conv2d_strided_cuda_tf32, test/test_nn.py::TestNN::test_Conv2d_strided_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_strided_with_long_tensor_cuda_fp32, test/test_nn.py::TestNN::test_Conv2d_strided_with_long_tensor_cuda_tf32, test/test_nn.py::TestNN::test_Conv2d_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_with_long_tensor_cuda_fp32, test/test_nn.py::TestNN::test_Conv2d_with_long_tensor_cuda_tf32, test/test_nn.py::TestNN::test_Conv2d_zero_batch, test/test_nn.py::TestNN::test_Conv2d_zero_batch_cuda_fp32, test/test_nn.py::TestNN::test_Conv2d_zero_batch_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_Conv2d_zero_batch_with_long_tensor_cuda_tf32, test/test_nn.py::TestNN::test_Conv2d_zeros_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_zeros_stride2_pad2_cuda_fp32, test/test_nn.py::TestNN::test_Conv2d_zeros_stride2_pad2_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_Conv3d_1x1x1_no_bias_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_Conv3d_1x1x1_no_bias_with_long_tensor_cuda_tf32, test/test_nn.py::TestNN::test_Conv3d_circular_stride2_pad2, test/test_nn.py::TestNN::test_Conv3d_circular_stride2_pad2_cuda_fp32, test/test_nn.py::TestNN::test_Conv3d_circular_stride2_pad2_cuda_tf32, test/test_nn.py::TestNN::test_Conv3d_cuda_fp32, test/test_nn.py::TestNN::test_Conv3d_cuda_tf32, test/test_nn.py::TestNN::test_Conv3d_dilated, test/test_nn.py::TestNN::test_Conv3d_dilated_cuda_fp32, test/test_nn.py::TestNN::test_Conv3d_dilated_cuda_tf32, test/test_nn.py::TestNN::test_Conv3d_dilated_strided, test/test_nn.py::TestNN::test_Conv3d_dilated_strided_cuda_fp32, test/test_nn.py::TestNN::test_Conv3d_dilated_strided_cuda_tf32, test/test_nn.py::TestNN::test_Conv3d_groups, test/test_nn.py::TestNN::test_Conv3d_groups_cuda_fp32, test/test_nn.py::TestNN::test_Conv3d_groups_cuda_tf32, test/test_nn.py::TestNN::test_Conv3d_groups_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_groups_with_long_tensor_cuda_fp32, test/test_nn.py::TestNN::test_Conv3d_groups_with_long_tensor_cuda_tf32, test/test_nn.py::TestNN::test_Conv3d_no_bias, test/test_nn.py::TestNN::test_Conv3d_no_bias_cuda_fp32, test/test_nn.py::TestNN::test_Conv3d_no_bias_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_Conv3d_no_bias_with_long_tensor_cuda_tf32, test/test_nn.py::TestNN::test_Conv3d_pad_same, test/test_nn.py::TestNN::test_Conv3d_pad_same_cuda_fp32, test/test_nn.py::TestNN::test_Conv3d_pad_same_cuda_tf32, test/test_nn.py::TestNN::test_Conv3d_pad_same_dilated, test/test_nn.py::TestNN::test_Conv3d_pad_same_dilated_cuda_fp32, test/test_nn.py::TestNN::test_Conv3d_pad_same_dilated_cuda_tf32, test/test_nn.py::TestNN::test_Conv3d_pad_valid, test/test_nn.py::TestNN::test_Conv3d_pad_valid_cuda_fp32, test/test_nn.py::TestNN::test_Conv3d_pad_valid_cuda_tf32, test/test_nn.py::TestNN::test_Conv3d_replicate_stride2_pad2, test/test_nn.py::TestNN::test_Conv3d_replicate_stride2_pad2_cuda_fp32, test/test_nn.py::TestNN::test_Conv3d_replicate_stride2_pad2_cuda_tf32, test/test_nn.py::TestNN::test_Conv3d_stride, test/test_nn.py::TestNN::test_Conv3d_stride_cuda_fp32, test/test_nn.py::TestNN::test_Conv3d_stride_cuda_tf32, test/test_nn.py::TestNN::test_Conv3d_stride_padding, test/test_nn.py::TestNN::test_Conv3d_stride_padding_cuda_fp32, test/test_nn.py::TestNN::test_Conv3d_stride_padding_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_Conv3d_stride_padding_with_long_tensor_cuda_tf32, test/test_nn.py::TestNN::test_Conv3d_stride_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_stride_with_long_tensor_cuda_fp32, test/test_nn.py::TestNN::test_Conv3d_stride_with_long_tensor_cuda_tf32, test/test_nn.py::TestNN::test_Conv3d_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_with_long_tensor_cuda_fp32, test/test_nn.py::TestNN::test_Conv3d_with_long_tensor_cuda_tf32, test/test_nn.py::TestNN::test_Conv3d_zero_batch, test/test_nn.py::TestNN::test_Conv3d_zero_batch_cuda_fp32, test/test_nn.py::TestNN::test_Conv3d_zero_batch_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_Conv3d_zero_batch_with_long_tensor_cuda_tf32, test/test_nn.py::TestNN::test_Conv3d_zeros_stride2_pad2, test/test_nn.py::TestNN::test_Conv3d_zeros_stride2_pad2_cuda_fp32, test/test_nn.py::TestNN::test_Conv3d_zeros_stride2_pad2_cuda_tf32, test/test_nn.py::TestNN::test_ConvTranspose1d, test/test_nn.py::TestNN::test_ConvTranspose1d_cuda_fp32, test/test_nn.py::TestNN::test_ConvTranspose1d_cuda_tf32, test/test_nn.py::TestNN::test_ConvTranspose1d_dilated, test/test_nn.py::TestNN::test_ConvTranspose1d_dilated_cuda_fp32, test/test_nn.py::TestNN::test_ConvTranspose1d_dilated_cuda_tf32, test/test_nn.py::TestNN::test_ConvTranspose1d_groups, test/test_nn.py::TestNN::test_ConvTranspose1d_groups_cuda_fp32, test/test_nn.py::TestNN::test_ConvTranspose1d_groups_cuda_tf32, test/test_nn.py::TestNN::test_ConvTranspose1d_no_bias, test/test_nn.py::TestNN::test_ConvTranspose1d_no_bias_cuda_fp32, test/test_nn.py::TestNN::test_ConvTranspose1d_no_bias_cuda_tf32, test/test_nn.py::TestNN::test_ConvTranspose2d, test/test_nn.py::TestNN::test_ConvTranspose2d_cuda_fp32, test/test_nn.py::TestNN::test_ConvTranspose2d_cuda_tf32, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated_cuda_fp32, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated_cuda_tf32, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated_with_long_tensor, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated_with_long_tensor_cuda_fp32, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated_with_long_tensor_cuda_tf32, test/test_nn.py::TestNN::test_ConvTranspose2d_groups, test/test_nn.py::TestNN::test_ConvTranspose2d_groups_cuda_fp32, test/test_nn.py::TestNN::test_ConvTranspose2d_groups_cuda_tf32, test/test_nn.py::TestNN::test_ConvTranspose2d_groups_with_long_tensor, test/test_nn.py::TestNN::test_ConvTranspose2d_groups_with_long_tensor_cuda_fp32, test/test_nn.py::TestNN::test_ConvTranspose2d_groups_with_long_tensor_cuda_tf32, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias_cuda_fp32, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias_with_long_tensor_cuda_tf32, test/test_nn.py::TestNN::test_ConvTranspose2d_with_long_tensor, test/test_nn.py::TestNN::test_ConvTranspose2d_with_long_tensor_cuda_fp32, test/test_nn.py::TestNN::test_ConvTranspose2d_with_long_tensor_cuda_tf32, test/test_nn.py::TestNN::test_ConvTranspose3d, test/test_nn.py::TestNN::test_ConvTranspose3d_cuda_fp32, test/test_nn.py::TestNN::test_ConvTranspose3d_cuda_tf32, test/test_nn.py::TestNN::test_ConvTranspose3d_dilated, test/test_nn.py::TestNN::test_ConvTranspose3d_dilated_cuda_fp32, test/test_nn.py::TestNN::test_ConvTranspose3d_dilated_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_mean_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_mean_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_none_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_sum_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_sum_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_sum_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_mean_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_sum_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_Linear_cuda_tf32, test/test_nn.py::TestNN::test_Linear_no_batch_dim, test/test_nn.py::TestNN::test_Linear_no_batch_dim_cuda_fp32, test/test_nn.py::TestNN::test_Linear_no_batch_dim_cuda_tf32, test/test_nn.py::TestNN::test_Linear_no_bias, test/test_nn.py::TestNN::test_Linear_no_bias_cuda_fp32, test/test_nn.py::TestNN::test_Linear_no_bias_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_none_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_sum_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_sum_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_mean_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_mean_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_sum_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_none_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_sum_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_mean_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_none_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_none_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_sum_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_mean_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_none_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_sum_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_TransformerDecoderLayer_gelu_activation_cuda_tf32, test/test_nn.py::TestNN::test_TransformerDecoderLayer_relu_activation, test/test_nn.py::TestNN::test_TransformerDecoderLayer_relu_activation_cuda_fp32, test/test_nn.py::TestNN::test_TransformerDecoderLayer_relu_activation_cuda_tf32, test/test_nn.py::TestNN::test_TransformerEncoderLayer_gelu_activation, test/test_nn.py::TestNN::test_TransformerEncoderLayer_gelu_activation_cuda_fp32, test/test_nn.py::TestNN::test_TransformerEncoderLayer_gelu_activation_cuda_tf32, test/test_nn.py::TestNN::test_TransformerEncoderLayer_relu_activation, test/test_nn.py::TestNN::test_TransformerEncoderLayer_relu_activation_cuda_fp32, test/test_nn.py::TestNN::test_TransformerEncoderLayer_relu_activation_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_Transformer_multilayer_coder_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_none_cuda_tf32, 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_fp32, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum_cuda_tf32, 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_backward_cl_cf_consistency_device_cuda_nd_2, test/test_nn.py::TestNN::test_affine_grid_backward_cl_cf_consistency_device_cuda_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_autograd_device_cuda_bias_weightCOO, test/test_nn.py::TestNN::test_linear_autograd_device_cuda_bias_weightCSC, test/test_nn.py::TestNN::test_linear_autograd_device_cuda_bias_weightCSR, test/test_nn.py::TestNN::test_linear_autograd_device_cuda_bias_weightStrided, test/test_nn.py::TestNN::test_linear_autograd_device_cuda_nobias_weightCOO, test/test_nn.py::TestNN::test_linear_autograd_device_cuda_nobias_weightCSC, test/test_nn.py::TestNN::test_linear_autograd_device_cuda_nobias_weightCSR, test/test_nn.py::TestNN::test_linear_autograd_device_cuda_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::TestNNDeviceTypeCUDA::test_BatchNorm_empty_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_Bilinear_empty_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_CTCLoss_cudnn_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_CTCLoss_empty_target_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_CTCLoss_no_batch_dim_reduction_mean_use_module_form_False_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_CTCLoss_no_batch_dim_reduction_mean_use_module_form_True_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_CTCLoss_no_batch_dim_reduction_none_use_module_form_False_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_CTCLoss_no_batch_dim_reduction_none_use_module_form_True_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_CTCLoss_no_batch_dim_reduction_sum_use_module_form_False_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_CTCLoss_no_batch_dim_reduction_sum_use_module_form_True_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_GRU_grad_and_gradgrad_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_GroupNorm_empty_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_GroupNorm_general_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_GroupNorm_memory_format_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_GroupNorm_numeric_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_GroupNorm_raises_error_if_one_value_per_group_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_InstanceNorm1d_general_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_InstanceNorm2d_general_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_InstanceNorm3d_general_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_LSTM_differentiable_backward_using_oneDNN_cuda_bfloat16, test/test_nn.py::TestNNDeviceTypeCUDA::test_LSTM_differentiable_backward_using_oneDNN_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_LSTM_grad_and_gradgrad_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_LayerNorm_general_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_LayerNorm_numeric_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_LocalResponseNorm_empty_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_MarginLoss_empty_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_MarginLoss_empty_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_MarginLoss_warnings_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_ReflectionPad2d_large_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_ReflectionPad2d_large_deterministic_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_ReflectionPad3d_large_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_ReflectionPad_empty_cuda_complex64, test/test_nn.py::TestNNDeviceTypeCUDA::test_ReflectionPad_empty_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_ReflectionPad_fails_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_ReplicationPad1d_large_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_ReplicationPad2d_large_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_ReplicationPad3d_large_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_ReplicationPad_empty_cuda_complex128, test/test_nn.py::TestNNDeviceTypeCUDA::test_ReplicationPad_empty_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_TransformerDecoderLayer_empty_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_TransformerDecoder_empty_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_TransformerEncoderLayer_empty_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_TransformerEncoder_empty_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_Transformer_empty_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_Unfold_empty_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_activations_bfloat16_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_activations_bfloat16_half_cpu_cuda_bfloat16, test/test_nn.py::TestNNDeviceTypeCUDA::test_activations_bfloat16_half_cpu_cuda_float16, test/test_nn.py::TestNNDeviceTypeCUDA::test_adaptiveavg_pool1d_shmem_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_affine_2d_rotate0_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_affine_2d_rotate45_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_affine_2d_rotate90_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_affine_2d_rotateRandom_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_affine_3d_rotateRandom_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_avg_pool_large_tensor2_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_avg_pool_large_tensor_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_batchnorm_affine_cuda_bfloat16, test/test_nn.py::TestNNDeviceTypeCUDA::test_batchnorm_affine_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_batchnorm_affine_mixed_cuda_bfloat16, test/test_nn.py::TestNNDeviceTypeCUDA::test_batchnorm_affine_mixed_cuda_float16, test/test_nn.py::TestNNDeviceTypeCUDA::test_batchnorm_eval_cuda_bfloat16, test/test_nn.py::TestNNDeviceTypeCUDA::test_batchnorm_eval_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_batchnorm_eval_mixed_cuda_bfloat16, test/test_nn.py::TestNNDeviceTypeCUDA::test_batchnorm_eval_mixed_cuda_float16, test/test_nn.py::TestNNDeviceTypeCUDA::test_batchnorm_grad_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_batchnorm_large_batch_cuda_float16, test/test_nn.py::TestNNDeviceTypeCUDA::test_batchnorm_large_batch_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_batchnorm_simple_average_cuda_bfloat16, test/test_nn.py::TestNNDeviceTypeCUDA::test_batchnorm_simple_average_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_batchnorm_simple_average_mixed_cuda_bfloat16, test/test_nn.py::TestNNDeviceTypeCUDA::test_batchnorm_simple_average_mixed_cuda_float16, test/test_nn.py::TestNNDeviceTypeCUDA::test_batchnorm_update_stats_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_channel_shuffle_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_clip_grad_norm_error_if_nonfinite_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_clip_grad_norm_foreach_False_norm_type_0_5_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_clip_grad_norm_foreach_False_norm_type_1_5_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_clip_grad_norm_foreach_False_norm_type_2_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_clip_grad_norm_foreach_False_norm_type_4_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_clip_grad_norm_foreach_False_norm_type_inf_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_clip_grad_norm_foreach_True_norm_type_0_5_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_clip_grad_norm_foreach_True_norm_type_1_5_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_clip_grad_norm_foreach_True_norm_type_2_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_clip_grad_norm_foreach_True_norm_type_4_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_clip_grad_norm_foreach_True_norm_type_inf_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_clip_grad_norm_multi_device_foreach_False_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_clip_grad_norm_multi_device_foreach_True_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_clip_grad_value_foreach_False_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_clip_grad_value_foreach_True_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_conv_empty_input_cuda_complex128, test/test_nn.py::TestNNDeviceTypeCUDA::test_conv_empty_input_cuda_float16, test/test_nn.py::TestNNDeviceTypeCUDA::test_conv_empty_input_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_conv_empty_input_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_cross_entropy_64bit_reduction_mean_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_cross_entropy_64bit_reduction_none_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_cross_entropy_64bit_reduction_sum_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_cross_entropy_label_smoothing_consistent_index_target_and_probs_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_cross_entropy_label_smoothing_errors_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_cross_entropy_label_smoothing_weight_ignore_indices_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_cross_entropy_label_smoothing_with_probs_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_cross_entropy_large_tensor_reduction_mean_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_cross_entropy_large_tensor_reduction_none_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_cross_entropy_large_tensor_reduction_sum_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_cross_entropy_loss_2d_out_of_bounds_class_index_cuda_float16, test/test_nn.py::TestNNDeviceTypeCUDA::test_cross_entropy_loss_2d_out_of_bounds_class_index_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_cross_entropy_loss_index_target_unit_weights_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_cross_entropy_loss_one_hot_target_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_cross_entropy_loss_prob_target_all_reductions_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_mean_weighted_False_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_mean_weighted_True_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_none_weighted_False_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_none_weighted_True_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_sum_weighted_False_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_sum_weighted_True_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_cross_entropy_loss_prob_target_unit_weights_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_ctc_loss_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_ctc_loss_cudnn_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_ctc_loss_cudnn_tensor_cpu_length_cuda_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_ctc_loss_cudnn_tensor_cuda_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_ctc_loss_error_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_device_mask_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_elu_inplace_overlap_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_elu_inplace_with_neg_alpha_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_fold_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_glu_bfloat16_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_grid_sample_bfloat16_precision_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_grid_sample_half_precision_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_grid_sample_large_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_grid_sample_large_index_2d_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_grid_sample_large_index_2d_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_grid_sample_large_index_3d_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_grid_sample_large_index_3d_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_grid_sample_nan_inf_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_grid_sample_nan_inf_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_groupnorm_nhwc_cuda_bfloat16, test/test_nn.py::TestNNDeviceTypeCUDA::test_groupnorm_nhwc_cuda_float16, test/test_nn.py::TestNNDeviceTypeCUDA::test_groupnorm_nhwc_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_groupnorm_nhwc_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_gumbel_softmax_cuda_float16, test/test_nn.py::TestNNDeviceTypeCUDA::test_gumbel_softmax_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_gumbel_softmax_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_hardsigmoid_grad_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_hardswish_grad_corner_cuda_bfloat16, test/test_nn.py::TestNNDeviceTypeCUDA::test_hardswish_grad_corner_cuda_float16, test/test_nn.py::TestNNDeviceTypeCUDA::test_hardswish_grad_corner_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_hardswish_grad_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_hardswish_inplace_overlap_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_instancenorm_raises_error_for_single_spatial_element_during_training_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm1d_no_batch_dim_False_affine_False_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm1d_no_batch_dim_False_affine_True_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm1d_no_batch_dim_True_affine_False_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm1d_no_batch_dim_True_affine_True_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm2d_no_batch_dim_False_affine_False_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm2d_no_batch_dim_False_affine_True_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm2d_no_batch_dim_True_affine_False_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm2d_no_batch_dim_True_affine_True_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_False_affine_False_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_False_affine_True_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_True_affine_False_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_True_affine_True_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_instancenorm_raises_error_if_less_than_one_value_per_channel_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_invalid_reduction_strings_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_layernorm_half_precision_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_layernorm_weight_bias_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_leaky_relu_inplace_overlap_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_leaky_relu_inplace_with_neg_slope_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_leaky_relu_inplace_with_zero_slope_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_linear_empty_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_log_softmax_big_cuda_float16, test/test_nn.py::TestNNDeviceTypeCUDA::test_log_softmax_big_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_log_softmax_cpu_cuda_bfloat16, test/test_nn.py::TestNNDeviceTypeCUDA::test_log_softmax_cpu_cuda_float16, test/test_nn.py::TestNNDeviceTypeCUDA::test_logsigmoid_out_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_lstmcell_backward_only_one_output_grad_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_masked_softmax_TxT_layout_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_masked_softmax_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_masked_softmax_devices_parity_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_masked_softmax_forward_with_nans_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_masked_softmax_grad_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_masked_softmax_lowp_cuda_bfloat16, test/test_nn.py::TestNNDeviceTypeCUDA::test_masked_softmax_lowp_cuda_float16, test/test_nn.py::TestNNDeviceTypeCUDA::test_masked_softmax_mask_types_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_masked_softmax_transformer_layout_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_mish_inplace_overlap_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_module_to_empty_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_module_to_empty_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_module_to_empty_non_recursive_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_mse_loss_error_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_nll_loss_all_ignored_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_nll_loss_byte_target_matches_long_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_nll_loss_empty_tensor_reduction_mean_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_nll_loss_empty_tensor_reduction_none_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_nll_loss_empty_tensor_reduction_sum_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_nll_loss_invalid_target_dim_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_nll_loss_invalid_weights_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_nll_loss_large_tensor_reduction_mean_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_nll_loss_large_tensor_reduction_none_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_nll_loss_large_tensor_reduction_sum_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_nll_loss_mismatched_batch_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_nll_loss_out_of_bounds_ignore_index_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_nll_loss_total_weight_is_zero_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_nn_empty_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_nn_scalars_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_nn_scalars_reductions_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_nonlinearity_propagate_nan_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_one_hot_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_overwrite_module_params_on_conversion_cpu_device_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_pad_cuda_complex128, test/test_nn.py::TestNNDeviceTypeCUDA::test_pad_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_prelu_backward_32bit_indexing_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_replicatepad_64bit_indexing_cuda_float16, test/test_nn.py::TestNNDeviceTypeCUDA::test_rmsnorm_numeric_cuda_bfloat16, test/test_nn.py::TestNNDeviceTypeCUDA::test_rmsnorm_numeric_cuda_float16, test/test_nn.py::TestNNDeviceTypeCUDA::test_rnn_fused_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_rnn_fused_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_rnn_retain_variables_cuda_float16, test/test_nn.py::TestNNDeviceTypeCUDA::test_rnn_retain_variables_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_rnn_retain_variables_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_save_lstm_compatibility_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_silu_inplace_overlap_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_skip_init_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_smooth_l1_loss_bfloat16_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_smooth_l1_loss_vs_huber_loss_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_smoothl1loss_backward_zero_beta_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_softmax_64bit_indexing_cuda_float16, test/test_nn.py::TestNNDeviceTypeCUDA::test_softmax_backward_64bit_indexing_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_softmax_backward_smem_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_softmax_backward_unaligned_grad_output_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_softmax_backward_unaligned_output_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_softmax_backward_without_fully_vectorized_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_softmax_bfloat16_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_softmax_cpu_cuda_bfloat16, test/test_nn.py::TestNNDeviceTypeCUDA::test_softmax_cpu_cuda_float16, test/test_nn.py::TestNNDeviceTypeCUDA::test_softmax_cuda_float16, test/test_nn.py::TestNNDeviceTypeCUDA::test_softmax_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_softmax_double_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_softmax_forward_64bit_indexing_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_softmax_results_cuda_float16, test/test_nn.py::TestNNDeviceTypeCUDA::test_softmax_results_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_softplus_inplace_overlap_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_softplus_low_threshold_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_softshrink_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_softshrink_inplace_overlap_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_softshrink_negative_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_threshold_inplace_overlap_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_to_complex_cuda_complex128, test/test_nn.py::TestNNDeviceTypeCUDA::test_to_complex_cuda_complex64, test/test_nn.py::TestNNDeviceTypeCUDA::test_to_complex_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_transformerencoderlayer_cuda_float16, test/test_nn.py::TestNNDeviceTypeCUDA::test_transformerencoderlayer_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_transformerencoderlayer_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_transformerencoderlayer_fast_path_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_transformerencoderlayer_gelu_cuda_float16, test/test_nn.py::TestNNDeviceTypeCUDA::test_transformerencoderlayer_gelu_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_triplet_margin_with_distance_loss_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_triplet_margin_with_distance_loss_default_parity_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format0_align_corners_False_input_size_399_output_size_437_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format0_align_corners_False_input_size_403_output_size_377_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format0_align_corners_True_input_size_399_output_size_437_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format0_align_corners_True_input_size_403_output_size_377_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_False_input_size_399_output_size_437_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_False_input_size_403_output_size_377_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_True_input_size_399_output_size_437_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_True_input_size_403_output_size_377_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_antialias_False_align_corners_False_mode_bicubic_memory_format0_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_antialias_False_align_corners_False_mode_bicubic_memory_format1_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_antialias_False_align_corners_False_mode_bilinear_memory_format0_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_antialias_False_align_corners_False_mode_bilinear_memory_format1_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_antialias_False_align_corners_True_mode_bicubic_memory_format0_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_antialias_False_align_corners_True_mode_bicubic_memory_format1_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_antialias_False_align_corners_True_mode_bilinear_memory_format0_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_antialias_False_align_corners_True_mode_bilinear_memory_format1_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_antialias_True_align_corners_False_mode_bicubic_memory_format0_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_antialias_True_align_corners_False_mode_bicubic_memory_format1_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_antialias_True_align_corners_False_mode_bilinear_memory_format0_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_antialias_True_align_corners_False_mode_bilinear_memory_format1_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_antialias_True_align_corners_True_mode_bicubic_memory_format0_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_antialias_True_align_corners_True_mode_bicubic_memory_format1_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_antialias_True_align_corners_True_mode_bilinear_memory_format0_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_antialias_True_align_corners_True_mode_bilinear_memory_format1_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::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_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_float32_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_float64_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_int16_cuda_int16, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_int32_cuda_int32, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_int64_cuda_int64, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_int8_cuda_int8, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_uint8_cuda_uint8, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_float32_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_float64_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_int16_cuda_int16, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_int32_cuda_int32, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_int64_cuda_int64, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_int8_cuda_int8, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_uint8_cuda_uint8, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_float32_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_float64_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_int16_cuda_int16, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_int32_cuda_int32, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_int64_cuda_int64, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_int8_cuda_int8, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_uint8_cuda_uint8, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_float32_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_float64_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_int16_cuda_int16, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_int32_cuda_int32, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_int64_cuda_int64, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_int8_cuda_int8, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_uint8_cuda_uint8, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_float32_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_float64_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_int16_cuda_int16, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_int32_cuda_int32, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_int64_cuda_int64, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_int8_cuda_int8, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_uint8_cuda_uint8, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_float32_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_float64_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_int16_cuda_int16, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_int32_cuda_int32, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_int64_cuda_int64, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_int8_cuda_int8, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_uint8_cuda_uint8, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_float32_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_float64_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_int16_cuda_int16, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_int32_cuda_int32, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_int64_cuda_int64, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_int8_cuda_int8, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_uint8_cuda_uint8, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_float32_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_float64_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_int16_cuda_int16, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_int32_cuda_int32, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_int64_cuda_int64, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_int8_cuda_int8, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_uint8_cuda_uint8, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_float32_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_float64_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_int16_cuda_int16, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_int32_cuda_int32, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_int64_cuda_int64, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_int8_cuda_int8, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_uint8_cuda_uint8, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_float32_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_float64_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_int16_cuda_int16, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_int32_cuda_int32, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_int64_cuda_int64, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_int8_cuda_int8, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_uint8_cuda_uint8, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_float32_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_float64_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_int16_cuda_int16, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_int32_cuda_int32, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_int64_cuda_int64, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_int8_cuda_int8, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_uint8_cuda_uint8, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_float32_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_float64_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_int16_cuda_int16, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_int32_cuda_int32, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_int64_cuda_int64, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_int8_cuda_int8, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_uint8_cuda_uint8, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_float32_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_float64_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_int16_cuda_int16, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_int32_cuda_int32, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_int64_cuda_int64, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_int8_cuda_int8, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_uint8_cuda_uint8, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_float32_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_float64_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_int16_cuda_int16, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_int32_cuda_int32, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_int64_cuda_int64, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_int8_cuda_int8, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_uint8_cuda_uint8, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_float32_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_float64_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_int16_cuda_int16, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_int32_cuda_int32, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_int64_cuda_int64, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_int8_cuda_int8, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_uint8_cuda_uint8, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_float32_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_float64_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_int16_cuda_int16, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_int32_cuda_int32, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_int64_cuda_int64, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_int8_cuda_int8, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_uint8_cuda_uint8, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBicubic2d_aa_correctness_memory_format0_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBicubic2d_aa_correctness_memory_format1_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBicubic2d_correctness_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBilinear2d_aa_correctness_memory_format0_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingBilinear2d_aa_correctness_memory_format1_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearest1d_correctness_isize_10_osize_15_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearest1d_correctness_isize_20_osize_11_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearest1d_launch_config_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearest1d_mode_nearest-exact_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearest1d_mode_nearest_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearest2d_correctness_memory_format0_isize_10_osize_15_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearest2d_correctness_memory_format0_isize_20_osize_11_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearest2d_correctness_memory_format1_isize_10_osize_15_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearest2d_correctness_memory_format1_isize_20_osize_11_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearest2d_launch_config_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearest2d_launch_fail_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearest2d_launch_rocm_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearest2d_memory_format0_mode_nearest-exact_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearest2d_memory_format0_mode_nearest_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearest2d_memory_format1_mode_nearest-exact_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearest2d_memory_format1_mode_nearest_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearest3d_correctness_memory_format0_isize_10_osize_15_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearest3d_correctness_memory_format0_isize_20_osize_11_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearest3d_correctness_memory_format1_isize_10_osize_15_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearest3d_correctness_memory_format1_isize_20_osize_11_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearest3d_launch_config_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearest3d_memory_format0_mode_nearest-exact_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearest3d_memory_format0_mode_nearest_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearest3d_memory_format1_mode_nearest-exact_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearest3d_memory_format1_mode_nearest_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearestExact1d_correctness_isize_10_osize_15_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearestExact1d_correctness_isize_20_osize_11_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearestExact1d_rescale_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearestExact2d_correctness_memory_format0_isize_10_osize_15_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearestExact2d_correctness_memory_format0_isize_20_osize_11_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearestExact2d_correctness_memory_format1_isize_10_osize_15_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearestExact2d_correctness_memory_format1_isize_20_osize_11_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearestExact3d_correctness_memory_format0_isize_10_osize_15_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearestExact3d_correctness_memory_format0_isize_20_osize_11_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearestExact3d_correctness_memory_format1_isize_10_osize_15_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingNearestExact3d_correctness_memory_format1_isize_20_osize_11_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingTrilinear3d_align_corners_False_memory_format0_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingTrilinear3d_align_corners_False_memory_format1_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingTrilinear3d_align_corners_True_memory_format0_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingTrilinear3d_align_corners_True_memory_format1_cuda, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsampling_64bit_indexing_channels_last_cuda_bfloat16, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsampling_64bit_indexing_channels_last_cuda_float16, test/test_nn.py::TestNNDeviceTypeCUDA::test_upsamplingnearest2d_backward_64bit_indexing_cuda_float16, test/test_nn.py::TestNNDeviceTypeCUDA::test_variable_sequence_cuda_float16, test/test_nn.py::TestNNDeviceTypeCUDA::test_variable_sequence_cuda_float32, test/test_nn.py::TestNNDeviceTypeCUDA::test_variable_sequence_cuda_float64, test/test_nn.py::TestNNDeviceTypeCUDA::test_warp_softmax_64bit_indexing_cuda_float16, test/test_nn.py::TestNNDeviceTypeCUDA::test_warp_softmax_64bit_indexing_cuda_float32 2025-07-17T08:51:36.1566078Z 2025-07-17T08:51:36.1566300Z Running distributions/test_distributions 1/1 ... [2025-07-17 08:51:36.023917] 2025-07-17T08:51:36.1566696Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T08:51:36.1567410Z Executing ['/opt/conda/envs/py_3.12/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 08:51:36.024359] 2025-07-17T08:52:15.9745872Z 2025-07-17T08:52:15.9746748Z distributions/test_distributions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_distributions_1.1_1986cc0b9f80c8f3_.log 2025-07-17T08:52:15.9796907Z 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 2025-07-17T08:52:15.9845662Z 2025-07-17T08:52:15.9845796Z Running test_spectral_ops 1/1 ... [2025-07-17 08:52:15.974952] 2025-07-17T08:52:15.9846067Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T08:52:15.9846686Z Executing ['/opt/conda/envs/py_3.12/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 08:52:15.975250] 2025-07-17T08:54:29.9371497Z 2025-07-17T08:54:29.9372363Z test_spectral_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_spectral_ops_1.1_6a0074acdd1decbf_.log 2025-07-17T08:54:29.9451145Z Running 347 items in this shard: test/test_spectral_ops.py::TestFFTCUDA::test_batch_istft_cuda, test/test_spectral_ops.py::TestFFTCUDA::test_complex_istft_real_equiv_cuda_complex128, test/test_spectral_ops.py::TestFFTCUDA::test_complex_stft_definition_cuda_complex128, test/test_spectral_ops.py::TestFFTCUDA::test_complex_stft_onesided_cuda, test/test_spectral_ops.py::TestFFTCUDA::test_complex_stft_real_equiv_cuda_complex128, test/test_spectral_ops.py::TestFFTCUDA::test_complex_stft_roundtrip_cuda_complex128, test/test_spectral_ops.py::TestFFTCUDA::test_complex_stft_roundtrip_cuda_float64, test/test_spectral_ops.py::TestFFTCUDA::test_cufft_context_cuda_complex128, 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test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error__refs_fft_hfft_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error__refs_fft_hfftn_cuda_complex32, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error__refs_fft_hfftn_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error__refs_fft_ifft2_cuda_complex32, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error__refs_fft_ifft2_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error__refs_fft_ifft_cuda_complex32, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error__refs_fft_ifft_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error__refs_fft_ifftn_cuda_complex32, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error__refs_fft_ifftn_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error__refs_fft_ihfft2_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error__refs_fft_ihfft_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error__refs_fft_ihfftn_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error__refs_fft_irfft2_cuda_complex32, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error__refs_fft_irfft2_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error__refs_fft_irfft_cuda_complex32, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error__refs_fft_irfft_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error__refs_fft_irfftn_cuda_complex32, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error__refs_fft_irfftn_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error__refs_fft_rfft2_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error__refs_fft_rfft_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error__refs_fft_rfftn_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error_fft_fft2_cuda_complex32, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error_fft_fft2_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error_fft_fft_cuda_complex32, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error_fft_fft_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error_fft_fftn_cuda_complex32, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error_fft_fftn_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error_fft_hfft2_cuda_complex32, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error_fft_hfft2_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error_fft_hfft_cuda_complex32, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error_fft_hfft_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error_fft_hfftn_cuda_complex32, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error_fft_hfftn_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error_fft_ifft2_cuda_complex32, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error_fft_ifft2_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error_fft_ifft_cuda_complex32, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error_fft_ifft_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error_fft_ifftn_cuda_complex32, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error_fft_ifftn_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error_fft_ihfft2_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error_fft_ihfft_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error_fft_ihfftn_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error_fft_irfft2_cuda_complex32, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error_fft_irfft2_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error_fft_irfft_cuda_complex32, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error_fft_irfft_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error_fft_irfftn_cuda_complex32, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error_fft_irfftn_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error_fft_rfft2_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error_fft_rfft_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_half_and_chalf_not_power_of_two_error_fft_rfftn_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_ifft_rfft_irfft_cuda_float64, test/test_spectral_ops.py::TestFFTCUDA::test_fft_input_modification_cuda, test/test_spectral_ops.py::TestFFTCUDA::test_fft_invalid_dtypes_cuda, test/test_spectral_ops.py::TestFFTCUDA::test_fft_plan_repeatable_cuda, test/test_spectral_ops.py::TestFFTCUDA::test_fft_round_trip_cuda_complex128, test/test_spectral_ops.py::TestFFTCUDA::test_fft_round_trip_cuda_complex32, test/test_spectral_ops.py::TestFFTCUDA::test_fft_round_trip_cuda_complex64, test/test_spectral_ops.py::TestFFTCUDA::test_fft_round_trip_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_round_trip_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_fft_round_trip_cuda_float64, test/test_spectral_ops.py::TestFFTCUDA::test_fft_type_promotion_cuda_complex128, test/test_spectral_ops.py::TestFFTCUDA::test_fft_type_promotion_cuda_complex32, test/test_spectral_ops.py::TestFFTCUDA::test_fft_type_promotion_cuda_complex64, test/test_spectral_ops.py::TestFFTCUDA::test_fft_type_promotion_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fft_type_promotion_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_fft_type_promotion_cuda_float64, test/test_spectral_ops.py::TestFFTCUDA::test_fft_type_promotion_cuda_int8, test/test_spectral_ops.py::TestFFTCUDA::test_fftfreq_numpy_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_fftfreq_numpy_cuda_float64, test/test_spectral_ops.py::TestFFTCUDA::test_fftfreq_out_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_fftfreq_out_cuda_float64, test/test_spectral_ops.py::TestFFTCUDA::test_fftn_invalid__refs_fft_fftn_cuda_complex64, test/test_spectral_ops.py::TestFFTCUDA::test_fftn_invalid__refs_fft_fftn_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_fftn_invalid__refs_fft_hfftn_cuda_complex64, test/test_spectral_ops.py::TestFFTCUDA::test_fftn_invalid__refs_fft_hfftn_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_fftn_invalid__refs_fft_ifftn_cuda_complex64, test/test_spectral_ops.py::TestFFTCUDA::test_fftn_invalid__refs_fft_ifftn_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_fftn_invalid__refs_fft_ihfftn_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_fftn_invalid__refs_fft_irfftn_cuda_complex64, test/test_spectral_ops.py::TestFFTCUDA::test_fftn_invalid__refs_fft_irfftn_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_fftn_invalid__refs_fft_rfftn_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_fftn_invalid_fft_fftn_cuda_complex64, test/test_spectral_ops.py::TestFFTCUDA::test_fftn_invalid_fft_fftn_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_fftn_invalid_fft_hfftn_cuda_complex64, test/test_spectral_ops.py::TestFFTCUDA::test_fftn_invalid_fft_hfftn_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_fftn_invalid_fft_ifftn_cuda_complex64, test/test_spectral_ops.py::TestFFTCUDA::test_fftn_invalid_fft_ifftn_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_fftn_invalid_fft_ihfftn_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_fftn_invalid_fft_irfftn_cuda_complex64, test/test_spectral_ops.py::TestFFTCUDA::test_fftn_invalid_fft_irfftn_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_fftn_invalid_fft_rfftn_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_fftn_noop_transform_cuda_complex128, test/test_spectral_ops.py::TestFFTCUDA::test_fftn_noop_transform_cuda_complex64, test/test_spectral_ops.py::TestFFTCUDA::test_fftn_noop_transform_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fftn_noop_transform_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_fftn_noop_transform_cuda_float64, test/test_spectral_ops.py::TestFFTCUDA::test_fftn_round_trip_cuda_complex128, test/test_spectral_ops.py::TestFFTCUDA::test_fftn_round_trip_cuda_complex32, test/test_spectral_ops.py::TestFFTCUDA::test_fftn_round_trip_cuda_complex64, test/test_spectral_ops.py::TestFFTCUDA::test_fftn_round_trip_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_fftn_round_trip_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_fftn_round_trip_cuda_float64, test/test_spectral_ops.py::TestFFTCUDA::test_fftshift_frequencies_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_fftshift_frequencies_cuda_float64, test/test_spectral_ops.py::TestFFTCUDA::test_fftshift_numpy_cuda_complex128, test/test_spectral_ops.py::TestFFTCUDA::test_fftshift_numpy_cuda_complex64, test/test_spectral_ops.py::TestFFTCUDA::test_fftshift_numpy_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_fftshift_numpy_cuda_float64, test/test_spectral_ops.py::TestFFTCUDA::test_hfftn_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_hfftn_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_hfftn_cuda_float64, test/test_spectral_ops.py::TestFFTCUDA::test_ihfftn_cuda_float16, test/test_spectral_ops.py::TestFFTCUDA::test_ihfftn_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_ihfftn_cuda_float64, test/test_spectral_ops.py::TestFFTCUDA::test_istft_against_librosa_cuda_float64, test/test_spectral_ops.py::TestFFTCUDA::test_istft_linearity_cuda_float64, test/test_spectral_ops.py::TestFFTCUDA::test_istft_of_sine_cuda_float64, test/test_spectral_ops.py::TestFFTCUDA::test_istft_requires_window_cuda, test/test_spectral_ops.py::TestFFTCUDA::test_istft_round_trip_simple_cases_cuda_float64, test/test_spectral_ops.py::TestFFTCUDA::test_istft_round_trip_various_params_cuda_float64, test/test_spectral_ops.py::TestFFTCUDA::test_istft_round_trip_with_padding_cuda_float64, test/test_spectral_ops.py::TestFFTCUDA::test_istft_throws_cuda, test/test_spectral_ops.py::TestFFTCUDA::test_reference_1d__refs_fft_fft_cuda_complex64, test/test_spectral_ops.py::TestFFTCUDA::test_reference_1d__refs_fft_fft_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_reference_1d__refs_fft_hfft_cuda_complex64, test/test_spectral_ops.py::TestFFTCUDA::test_reference_1d__refs_fft_hfft_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_reference_1d__refs_fft_ifft_cuda_complex64, test/test_spectral_ops.py::TestFFTCUDA::test_reference_1d__refs_fft_ifft_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_reference_1d__refs_fft_ihfft_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_reference_1d__refs_fft_irfft_cuda_complex64, test/test_spectral_ops.py::TestFFTCUDA::test_reference_1d__refs_fft_irfft_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_reference_1d__refs_fft_rfft_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_reference_1d_fft_fft_cuda_complex64, test/test_spectral_ops.py::TestFFTCUDA::test_reference_1d_fft_fft_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_reference_1d_fft_hfft_cuda_complex64, test/test_spectral_ops.py::TestFFTCUDA::test_reference_1d_fft_hfft_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_reference_1d_fft_ifft_cuda_complex64, test/test_spectral_ops.py::TestFFTCUDA::test_reference_1d_fft_ifft_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_reference_1d_fft_ihfft_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_reference_1d_fft_irfft_cuda_complex64, test/test_spectral_ops.py::TestFFTCUDA::test_reference_1d_fft_irfft_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_reference_1d_fft_rfft_cuda_float32, test/test_spectral_ops.py::TestFFTCUDA::test_reference_nd__refs_fft_fftn_cuda_complex128, test/test_spectral_ops.py::TestFFTCUDA::test_reference_nd__refs_fft_fftn_cuda_complex64, test/test_spectral_ops.py::TestFFTCUDA::test_reference_nd__refs_fft_hfftn_cuda_complex128, test/test_spectral_ops.py::TestFFTCUDA::test_reference_nd__refs_fft_hfftn_cuda_complex64, test/test_spectral_ops.py::TestFFTCUDA::test_reference_nd__refs_fft_ifftn_cuda_complex128, test/test_spectral_ops.py::TestFFTCUDA::test_reference_nd__refs_fft_ifftn_cuda_complex64, test/test_spectral_ops.py::TestFFTCUDA::test_reference_nd__refs_fft_irfftn_cuda_complex128, test/test_spectral_ops.py::TestFFTCUDA::test_reference_nd__refs_fft_irfftn_cuda_complex64, test/test_spectral_ops.py::TestFFTCUDA::test_reference_nd_fft_fftn_cuda_complex128, test/test_spectral_ops.py::TestFFTCUDA::test_reference_nd_fft_fftn_cuda_complex64, test/test_spectral_ops.py::TestFFTCUDA::test_reference_nd_fft_hfftn_cuda_complex128, test/test_spectral_ops.py::TestFFTCUDA::test_reference_nd_fft_hfftn_cuda_complex64, test/test_spectral_ops.py::TestFFTCUDA::test_reference_nd_fft_ifftn_cuda_complex128, test/test_spectral_ops.py::TestFFTCUDA::test_reference_nd_fft_ifftn_cuda_complex64, test/test_spectral_ops.py::TestFFTCUDA::test_reference_nd_fft_irfftn_cuda_complex128, test/test_spectral_ops.py::TestFFTCUDA::test_reference_nd_fft_irfftn_cuda_complex64, test/test_spectral_ops.py::TestFFTCUDA::test_stft_align_to_window_only_requires_non_center_cuda, test/test_spectral_ops.py::TestFFTCUDA::test_stft_cuda_float64, test/test_spectral_ops.py::TestFFTCUDA::test_stft_requires_complex_cuda, test/test_spectral_ops.py::TestFFTCUDA::test_stft_requires_window_cuda, test/test_spectral_ops.py::TestFFTCUDA::test_stft_roundtrip_complex_window_cuda_complex128, test/test_spectral_ops.py::TestFFTCUDA::test_stft_roundtrip_complex_window_cuda_float64, test/test_spectral_ops.py::TestFFTCUDA::test_stft_window_device_cuda 2025-07-17T08:54:29.9514838Z 2025-07-17T08:54:29.9515171Z Running inductor/test_max_autotune 1/2 ... [2025-07-17 08:54:29.937502] 2025-07-17T08:54:29.9515462Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T08:54:29.9516098Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_max_autotune.py', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:54:29.937812] 2025-07-17T09:02:18.6023401Z 2025-07-17T09:02:18.6024278Z PRINTING LOG FILE of inductor/test_max_autotune 1/2 (test/test-reports/inductor.test_max_autotune_1.2_c5c210428b66da3e_.log) 2025-07-17T09:02:18.6030131Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T09:02:18.6031106Z import pkg_resources 2025-07-17T09:02:18.6032723Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py:1597: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/Context.cpp:78.) 2025-07-17T09:02:18.6034272Z _C._set_float32_matmul_precision(precision) 2025-07-17T09:02:18.6034707Z Test results will be stored in test-reports/python-pytest/inductor.test_max_autotune/inductor.test_max_autotune-6e62b0eb4fd2a2c4.xml 2025-07-17T09:02:18.6035156Z ============================= test session starts ============================== 2025-07-17T09:02:18.6035521Z platform linux -- Python 3.12.11, pytest-7.3.2, pluggy-1.6.0 -- /opt/conda/envs/py_3.12/bin/python 2025-07-17T09:02:18.6035830Z cachedir: .pytest_cache 2025-07-17T09:02:18.6036204Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2025-07-17T09:02:18.6036605Z rootdir: /var/lib/jenkins/pytorch 2025-07-17T09:02:18.6036813Z configfile: pytest.ini 2025-07-17T09:02:18.6037202Z plugins: rerunfailures-14.0, subtests-0.13.1, flakefinder-1.1.0, xdist-3.3.1, xdoctest-1.1.0, hypothesis-5.35.1, cpp-2.3.0, typeguard-4.3.0 2025-07-17T09:02:18.6037613Z collecting ... collected 132 items 2025-07-17T09:02:18.6037908Z stepcurrent: Cannot find last run test, not skipping 2025-07-17T09:02:18.6055995Z Running 72 items in this shard: test/inductor/test_max_autotune.py::TestMaxAutotune::test_autotune_device_guard, test/inductor/test_max_autotune.py::TestMaxAutotune::test_cat_max_autotune_extern, test/inductor/test_max_autotune.py::TestMaxAutotune::test_conv1x1_with_free_symbols, test/inductor/test_max_autotune.py::TestMaxAutotune::test_conv3d, test/inductor/test_max_autotune.py::TestMaxAutotune::test_conv_backend, test/inductor/test_max_autotune.py::TestMaxAutotune::test_honor_sm_carveout_with_triton_tma_carveout0_op_mm, test/inductor/test_max_autotune.py::TestMaxAutotune::test_honor_sm_carveout_with_triton_tma_carveout_27_op_mm, test/inductor/test_max_autotune.py::TestMaxAutotune::test_honor_sm_carveout_with_triton_tma_carveout_27_op_scaled_mm, test/inductor/test_max_autotune.py::TestMaxAutotune::test_jit_fusion_matches_aot_fusion, test/inductor/test_max_autotune.py::TestMaxAutotune::test_linear_and_cel, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_addmm_persistent_tma_a_transposed_False_b_transposed_False_dynamic_False, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_addmm_persistent_tma_a_transposed_False_b_transposed_True_dynamic_True, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_addmm_persistent_tma_a_transposed_True_b_transposed_False_dynamic_True, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_addmm_persistent_tma_a_transposed_True_b_transposed_True_dynamic_True, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_addmm_persistent_tma_illegal_alignment_dynamic_True, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_addmm_tma_dynamic_outer_dim, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_addmm_zero_size_input_dynamic_True, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_decompose_k_dynamic_False_bfloat16_sizes1, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_decompose_k_dynamic_False_bfloat16_sizes2, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_decompose_k_dynamic_False_float16_sizes0, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_decompose_k_dynamic_False_float16_sizes1, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_decompose_k_dynamic_True_bfloat16_sizes0, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_decompose_k_dynamic_True_bfloat16_sizes1, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_decompose_k_dynamic_True_bfloat16_sizes2, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_decompose_k_dynamic_True_float16_sizes0, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_decompose_k_dynamic_True_float16_sizes2, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_decompose_k_dynamic_input, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_decompose_k_dynamic_input_bwd, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_exhaustive, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_mm_plus_mm_zero_size_input_dynamic_True, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_regular_mm_persistent_tma_a_transposed_False_b_transposed_False_dynamic_False, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_regular_mm_persistent_tma_a_transposed_False_b_transposed_True_dynamic_False, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_regular_mm_persistent_tma_illegal_alignment_dynamic_False, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_regular_mm_persistent_tma_illegal_alignment_dynamic_True, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_regular_mm_tma_dynamic_outer_dim, test/inductor/test_max_autotune.py::TestMaxAutotune::test_non_contiguous_input_bmm, test/inductor/test_max_autotune.py::TestMaxAutotune::test_non_contiguous_input_mm, test/inductor/test_max_autotune.py::TestMaxAutotune::test_non_contiguous_input_mm_plus_mm, test/inductor/test_max_autotune.py::TestMaxAutotune::test_triton_template_generated_code_caching_bmm, test/inductor/test_max_autotune.py::TestMaxAutotune::test_triton_template_generated_code_caching_mm_plus_mm, test/inductor/test_max_autotune.py::TestMaxAutotunePrecompile::test_precompilation_threads, test/inductor/test_max_autotune.py::TestMaxAutotunePrecompile::test_precompilations, test/inductor/test_max_autotune.py::TestMaxAutotuneSubproc::test_benchmark_choice_fail_in_subproc, test/inductor/test_max_autotune.py::TestMaxAutotuneSubproc::test_max_autotune_mm_plus_mm_autotune_in_subproc_True_autotune_multi_device_False, test/inductor/test_max_autotune.py::TestMaxAutotuneSubproc::test_max_autotune_mm_plus_mm_autotune_in_subproc_True_autotune_multi_device_True, test/inductor/test_max_autotune.py::TestMaxAutotuneSubproc::test_max_autotune_regular_mm_dynamic_False, test/inductor/test_max_autotune.py::TestMaxAutotuneSubproc::test_triton_template_with_epilogues_and_dynamic_shape, test/inductor/test_max_autotune.py::TestMaxAutotuneRemoteCache::test_max_autotune_remote_caching_dynamic_False, test/inductor/test_max_autotune.py::TestMaxAutotuneRemoteCache::test_max_autotune_remote_caching_dynamic_True, test/inductor/test_max_autotune.py::TestTuningProcess::test_tuning_subproc_crash, test/inductor/test_max_autotune.py::TestTuningProcessPool::test_tuning_pool_crash, test/inductor/test_max_autotune.py::TestTuningProcessPool::test_tuning_pool_multiple_devices, test/inductor/test_max_autotune.py::TestPrologueFusion::test_broadcast_x_K_63, test/inductor/test_max_autotune.py::TestPrologueFusion::test_broadcast_x_K_64, test/inductor/test_max_autotune.py::TestPrologueFusion::test_broadcast_y, test/inductor/test_max_autotune.py::TestPrologueFusion::test_gather_fusion, test/inductor/test_max_autotune.py::TestPrologueFusion::test_multiple_fusions_sizes2, test/inductor/test_max_autotune.py::TestPrologueFusion::test_multiple_inputs_sizes0, test/inductor/test_max_autotune.py::TestPrologueFusion::test_multiple_inputs_sizes1, test/inductor/test_max_autotune.py::TestPrologueFusion::test_multiple_inputs_sizes2, test/inductor/test_max_autotune.py::TestPrologueFusion::test_pending_fusion_pro_and_epi, test/inductor/test_max_autotune.py::TestPrologueFusion::test_pending_fusions_multiple, test/inductor/test_max_autotune.py::TestPrologueFusion::test_preserves_zero_analysis, test/inductor/test_max_autotune.py::TestPrologueFusion::test_prologue_masked_load_sizes1, test/inductor/test_max_autotune.py::TestPrologueFusion::test_prologue_masked_load_sizes2, test/inductor/test_max_autotune.py::TestPrologueFusion::test_prologue_multiple_nodes_sizes0, test/inductor/test_max_autotune.py::TestPrologueFusion::test_prologue_multiple_nodes_sizes1, test/inductor/test_max_autotune.py::TestPrologueFusion::test_prologue_multiple_nodes_sizes2, test/inductor/test_max_autotune.py::TestPrologueFusion::test_prologue_read_into_both_inputs_benchmark_fusion_False, test/inductor/test_max_autotune.py::TestPrologueFusion::test_prologue_read_into_both_inputs_benchmark_fusion_True, test/inductor/test_max_autotune.py::TestPrologueFusion::test_storage_offset_prologue, test/inductor/test_max_autotune.py::TestPrologueFusion::test_upcast_sizes0 2025-07-17T09:02:18.6072921Z 2025-07-17T09:02:18.6073227Z inductor/test_max_autotune.py::TestMaxAutotune::test_autotune_device_guard SKIPPED [0.0004s] (Need at least 2 devices for this test) [ 1%] 2025-07-17T09:02:18.6073787Z inductor/test_max_autotune.py::TestMaxAutotune::test_cat_max_autotune_extern PASSED [11.9436s] [ 2%] 2025-07-17T09:02:18.6074253Z inductor/test_max_autotune.py::TestMaxAutotune::test_conv1x1_with_free_symbols PASSED [8.4506s] [ 4%] 2025-07-17T09:02:18.6074678Z inductor/test_max_autotune.py::TestMaxAutotune::test_conv3d PASSED [0.0908s] [ 5%] 2025-07-17T09:02:18.6075083Z inductor/test_max_autotune.py::TestMaxAutotune::test_conv_backend PASSED [0.0760s] [ 6%] 2025-07-17T09:02:18.6075658Z inductor/test_max_autotune.py::TestMaxAutotune::test_honor_sm_carveout_with_triton_tma_carveout0_op_mm SKIPPED [0.0002s] (ROCm doesn't support sm carveout) [ 8%] 2025-07-17T09:02:18.6076367Z inductor/test_max_autotune.py::TestMaxAutotune::test_honor_sm_carveout_with_triton_tma_carveout_27_op_mm SKIPPED [0.0001s] (ROCm doesn't support sm carveout) [ 9%] 2025-07-17T09:02:18.6077078Z inductor/test_max_autotune.py::TestMaxAutotune::test_honor_sm_carveout_with_triton_tma_carveout_27_op_scaled_mm SKIPPED [0.0001s] (ROCm doesn't support sm carveout) [ 11%] 2025-07-17T09:02:18.6077843Z inductor/test_max_autotune.py::TestMaxAutotune::test_jit_fusion_matches_aot_fusion W0717 08:54:59.266000 42767 site-packages/torch/_export/__init__.py:70] +============================+ 2025-07-17T09:02:18.6078442Z W0717 08:54:59.266000 42767 site-packages/torch/_export/__init__.py:71] | !!! WARNING !!! | 2025-07-17T09:02:18.6079016Z W0717 08:54:59.267000 42767 site-packages/torch/_export/__init__.py:72] +============================+ 2025-07-17T09:02:18.6079971Z W0717 08:54:59.267000 42767 site-packages/torch/_export/__init__.py:73] torch._export.aot_compile()/torch._export.aot_load() is being deprecated, please switch to directly calling torch._inductor.aoti_compile_and_package(torch.export.export())/torch._inductor.aoti_load_package() instead. 2025-07-17T09:02:18.6080866Z E0717 08:55:03.497000 42767 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Runtime error during autotuning: 2025-07-17T09:02:18.6081662Z E0717 08:55:03.497000 42767 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] No valid triton configs. OutOfResources: out of resource: shared memory, Required: 98304, Hardware limit: 65536. Reducing block sizes or `num_stages` may help.. 2025-07-17T09:02:18.6082407Z E0717 08:55:03.497000 42767 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Ignoring this choice. 2025-07-17T09:02:18.6082904Z E0717 08:55:04.101000 42767 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Runtime error during autotuning: 2025-07-17T09:02:18.6083663Z E0717 08:55:04.101000 42767 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] No valid triton configs. OutOfResources: out of resource: shared memory, Required: 98304, Hardware limit: 65536. Reducing block sizes or `num_stages` may help.. 2025-07-17T09:02:18.6084391Z E0717 08:55:04.101000 42767 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Ignoring this choice. 2025-07-17T09:02:18.6084875Z E0717 08:55:04.829000 42767 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Runtime error during autotuning: 2025-07-17T09:02:18.6085634Z E0717 08:55:04.829000 42767 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] No valid triton configs. OutOfResources: out of resource: shared memory, Required: 131072, Hardware limit: 65536. Reducing block sizes or `num_stages` may help.. 2025-07-17T09:02:18.6086581Z E0717 08:55:04.829000 42767 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Ignoring this choice. 2025-07-17T09:02:18.6087080Z E0717 08:55:05.074000 42767 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Runtime error during autotuning: 2025-07-17T09:02:18.6087848Z E0717 08:55:05.074000 42767 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] No valid triton configs. OutOfResources: out of resource: shared memory, Required: 98304, Hardware limit: 65536. Reducing block sizes or `num_stages` may help.. 2025-07-17T09:02:18.6088588Z E0717 08:55:05.074000 42767 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Ignoring this choice. 2025-07-17T09:02:18.6089077Z E0717 08:55:05.197000 42767 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Runtime error during autotuning: 2025-07-17T09:02:18.6089835Z E0717 08:55:05.197000 42767 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] No valid triton configs. OutOfResources: out of resource: shared memory, Required: 81920, Hardware limit: 65536. Reducing block sizes or `num_stages` may help.. 2025-07-17T09:02:18.6090567Z E0717 08:55:05.197000 42767 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Ignoring this choice. 2025-07-17T09:02:18.6091057Z E0717 08:55:05.554000 42767 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Runtime error during autotuning: 2025-07-17T09:02:18.6091831Z E0717 08:55:05.554000 42767 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] No valid triton configs. OutOfResources: out of resource: shared memory, Required: 98304, Hardware limit: 65536. Reducing block sizes or `num_stages` may help.. 2025-07-17T09:02:18.6092553Z E0717 08:55:05.554000 42767 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Ignoring this choice. 2025-07-17T09:02:18.6093030Z E0717 08:55:05.673000 42767 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Runtime error during autotuning: 2025-07-17T09:02:18.6093947Z E0717 08:55:05.673000 42767 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] No valid triton configs. OutOfResources: out of resource: shared memory, Required: 131072, Hardware limit: 65536. Reducing block sizes or `num_stages` may help.. 2025-07-17T09:02:18.6094678Z E0717 08:55:05.673000 42767 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Ignoring this choice. 2025-07-17T09:02:18.6095146Z PASSED [14.4855s] [ 12%] 2025-07-17T09:02:18.6095637Z inductor/test_max_autotune.py::TestMaxAutotune::test_linear_and_cel E0717 08:55:30.093000 42767 site-packages/torch/_dynamo/utils.py:3063] Accuracy failed: allclose not within tol=0.01 2025-07-17T09:02:18.6096143Z ('RERUN', {'yellow': True}) [16.4919s] [ 13%] 2025-07-17T09:02:18.6096633Z inductor/test_max_autotune.py::TestMaxAutotune::test_linear_and_cel E0717 08:55:36.048000 42767 site-packages/torch/_dynamo/utils.py:3063] Accuracy failed: allclose not within tol=0.01 2025-07-17T09:02:18.6097128Z ('RERUN', {'yellow': True}) [5.8667s] [ 13%] 2025-07-17T09:02:18.6097621Z inductor/test_max_autotune.py::TestMaxAutotune::test_linear_and_cel E0717 08:55:42.259000 42767 site-packages/torch/_dynamo/utils.py:3063] Accuracy failed: allclose not within tol=0.01 2025-07-17T09:02:18.6098106Z FAILED [6.2102s] [ 13%] 2025-07-17T09:02:18.6098209Z 2025-07-17T09:02:18.6098316Z ==================================== RERUNS ==================================== 2025-07-17T09:02:18.6098610Z _____________________ TestMaxAutotune.test_linear_and_cel ______________________ 2025-07-17T09:02:18.6098887Z Traceback (most recent call last): 2025-07-17T09:02:18.6099248Z File "/var/lib/jenkins/pytorch/test/inductor/test_max_autotune.py", line 954, in test_linear_and_cel 2025-07-17T09:02:18.6099675Z assert same(expect, actual, tol=1e-2), f"ref:\n{expect}\nact:\n{actual}" 2025-07-17T09:02:18.6099954Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2025-07-17T09:02:18.6100158Z AssertionError: ref: 2025-07-17T09:02:18.6100565Z (tensor(11., device='cuda:0', dtype=torch.bfloat16, grad_fn=), tensor([[-4.9546e-07, 4.1677e-08, -5.2527e-07, ..., 4.9546e-07, 2025-07-17T09:02:18.6100968Z -9.3132e-07, -3.4273e-07], 2025-07-17T09:02:18.6101204Z [ 6.4448e-07, -7.0035e-07, -4.6939e-07, ..., 4.3772e-08, 2025-07-17T09:02:18.6101432Z 6.0722e-07, 1.0058e-06], 2025-07-17T09:02:18.6101653Z [-4.9826e-08, 1.0133e-06, 3.4086e-07, ..., 7.3016e-07, 2025-07-17T09:02:18.6101875Z -4.3027e-07, 8.6427e-07], 2025-07-17T09:02:18.6102058Z ..., 2025-07-17T09:02:18.6102239Z [-6.4448e-07, -8.2329e-07, -6.8545e-07, ..., -9.0152e-07, 2025-07-17T09:02:18.6102451Z 3.0361e-07, -8.9034e-07], 2025-07-17T09:02:18.6102654Z [-9.7603e-07, -6.3702e-07, 2.5705e-07, ..., 8.9034e-07, 2025-07-17T09:02:18.6102879Z 8.6427e-07, 1.9837e-07], 2025-07-17T09:02:18.6103113Z [ 5.0990e-08, -1.0654e-06, -2.1420e-07, ..., 8.7917e-07, 2025-07-17T09:02:18.6103511Z -1.6391e-07, -9.2015e-07]], device='cuda:0', dtype=torch.bfloat16), tensor([[ 8.1956e-08, 2.2054e-05, -2.2769e-05, ..., -1.5125e-06, 2025-07-17T09:02:18.6103867Z 7.4387e-05, -7.9632e-05], 2025-07-17T09:02:18.6104089Z [-4.8399e-05, -9.5367e-05, -4.7207e-05, ..., 5.1975e-05, 2025-07-17T09:02:18.6104312Z -1.0300e-04, -5.3406e-05], 2025-07-17T09:02:18.6104536Z [-1.0431e-05, -2.4438e-05, -1.1623e-05, ..., -1.9968e-06, 2025-07-17T09:02:18.6104763Z -2.7299e-05, -2.1458e-05], 2025-07-17T09:02:18.6104952Z ..., 2025-07-17T09:02:18.6105142Z [ 8.1658e-06, 4.2200e-05, 2.0862e-05, ..., -3.1471e-05, 2025-07-17T09:02:18.6105426Z -3.0547e-06, -8.4043e-06], 2025-07-17T09:02:18.6105639Z [ 3.3140e-05, 2.1577e-05, 8.1062e-06, ..., 1.8358e-05, 2025-07-17T09:02:18.6105881Z -5.0783e-05, -2.2888e-05], 2025-07-17T09:02:18.6106082Z [ 9.0338e-08, 3.3714e-07, 1.3132e-07, ..., 5.7369e-07, 2025-07-17T09:02:18.6118060Z -5.5507e-07, 9.2667e-08]], device='cuda:0', dtype=torch.bfloat16), tensor([-4.1485e-05, -4.0770e-05, -1.0431e-05, ..., -1.0550e-05, 2025-07-17T09:02:18.6118462Z -1.0848e-05, 1.9670e-05], device='cuda:0', dtype=torch.bfloat16)) 2025-07-17T09:02:18.6118705Z act: 2025-07-17T09:02:18.6118895Z (tensor(6.3750, device='cuda:0', dtype=torch.bfloat16, 2025-07-17T09:02:18.6119401Z grad_fn=), tensor([[ 2.6673e-06, -1.6975e-04, -2.5940e-04, ..., 1.6332e-05, 2025-07-17T09:02:18.6119729Z 1.3292e-05, 5.2214e-05], 2025-07-17T09:02:18.6119956Z [ 9.2387e-07, -1.6880e-04, -2.5749e-04, ..., 1.6928e-05, 2025-07-17T09:02:18.6120172Z 1.0967e-05, 5.1498e-05], 2025-07-17T09:02:18.6120381Z [ 1.6317e-06, -1.6975e-04, -2.5940e-04, ..., 1.7047e-05, 2025-07-17T09:02:18.6120593Z 1.2994e-05, 5.1260e-05], 2025-07-17T09:02:18.6120768Z ..., 2025-07-17T09:02:18.6120946Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6121169Z 0.0000e+00, 0.0000e+00], 2025-07-17T09:02:18.6121375Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6121585Z 0.0000e+00, 0.0000e+00], 2025-07-17T09:02:18.6121790Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6122162Z 0.0000e+00, 0.0000e+00]], device='cuda:0', dtype=torch.bfloat16), tensor([[-0.0015, -0.0039, -0.0004, ..., -0.0033, 0.0006, -0.0012], 2025-07-17T09:02:18.6122545Z [-0.0015, -0.0039, -0.0004, ..., -0.0034, 0.0007, -0.0012], 2025-07-17T09:02:18.6122786Z [-0.0015, -0.0039, -0.0004, ..., -0.0033, 0.0006, -0.0012], 2025-07-17T09:02:18.6122998Z ..., 2025-07-17T09:02:18.6123172Z [-0.0015, -0.0039, -0.0005, ..., -0.0033, 0.0006, -0.0013], 2025-07-17T09:02:18.6123411Z [-0.0015, -0.0039, -0.0005, ..., -0.0033, 0.0006, -0.0013], 2025-07-17T09:02:18.6123657Z [-0.0015, -0.0039, -0.0005, ..., -0.0033, 0.0006, -0.0013]], 2025-07-17T09:02:18.6124003Z device='cuda:0', dtype=torch.bfloat16), tensor([-0.3262, -0.3262, -0.3262, ..., -0.3262, -0.3262, -0.3262], 2025-07-17T09:02:18.6124341Z device='cuda:0', dtype=torch.bfloat16)) 2025-07-17T09:02:18.6124494Z 2025-07-17T09:02:18.6124620Z To execute this test, run the following from the base repo dir: 2025-07-17T09:02:18.6125031Z PYTORCH_TEST_WITH_ROCM=1 python test/inductor/test_max_autotune.py TestMaxAutotune.test_linear_and_cel 2025-07-17T09:02:18.6125312Z 2025-07-17T09:02:18.6125471Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2025-07-17T09:02:18.6125810Z ----------------------------- Captured stdout call ----------------------------- 2025-07-17T09:02:18.6126081Z frames [('total', 5), ('ok', 5)] 2025-07-17T09:02:18.6126272Z inline_call [] 2025-07-17T09:02:18.6126429Z unimplemented [] 2025-07-17T09:02:18.6127262Z graph_break [('Unsupported Tensor.backward() call\n Explanation: Dynamo currently does not support tracing `Tensor.backward()`.\n Hint: This graph break is fundamental - it is unlikely that Dynamo will ever be able to trace through your code. Consider finding a workaround.\n\n Developer debug context: call_method TensorVariable() backward () {}\n', 1)] 2025-07-17T09:02:18.6128130Z stats [('calls_captured', 2), ('unique_graphs', 1)] 2025-07-17T09:02:18.6128460Z aot_autograd [('total', 1), ('autograd_cache_miss', 1), ('ok', 1), ('autograd_cache_saved', 1)] 2025-07-17T09:02:18.6129521Z inductor [('triton_bundler_save_kernel', 480), ('benchmarking.InductorBenchmarker.benchmark_gpu', 61), ('async_compile_cache_miss', 40), ('async_compile_cache_hit', 20), ('pattern_matcher_nodes', 12), ('pattern_matcher_count', 7), ('extern_calls', 3), ('fxgraph_cache_miss', 2), ('benchmarking.InductorBenchmarker.benchmark', 2), ('select_algorithm_precompile', 1), ('select_algorithm_autotune', 1), ('inplace_padding', 1)] 2025-07-17T09:02:18.6130557Z aten_mm_info [('aten.addmm_32768_50264_768', 1), ('aten.mm_32768_768_50264', 1), ('aten.mm_50264_768_32768', 1)] 2025-07-17T09:02:18.6131048Z ----------------------------- Captured stderr call ----------------------------- 2025-07-17T09:02:18.6131330Z AUTOTUNE addmm(32768x50264, 32768x768, 768x50264) 2025-07-17T09:02:18.6131548Z strides: [0, 1], [768, 1], [1, 768] 2025-07-17T09:02:18.6131776Z dtypes: torch.bfloat16, torch.bfloat16, torch.bfloat16 2025-07-17T09:02:18.6132018Z bias_addmm 4.9959 ms 100.0% 2025-07-17T09:02:18.6132317Z addmm 8.0962 ms 61.7% 2025-07-17T09:02:18.6132660Z SingleProcess AUTOTUNE benchmarking takes 0.1381 seconds and 0.0002 seconds precompiling for 2 choices 2025-07-17T09:02:18.6133071Z _____________________ TestMaxAutotune.test_linear_and_cel ______________________ 2025-07-17T09:02:18.6133343Z Traceback (most recent call last): 2025-07-17T09:02:18.6133694Z File "/var/lib/jenkins/pytorch/test/inductor/test_max_autotune.py", line 954, in test_linear_and_cel 2025-07-17T09:02:18.6134108Z assert same(expect, actual, tol=1e-2), f"ref:\n{expect}\nact:\n{actual}" 2025-07-17T09:02:18.6134389Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2025-07-17T09:02:18.6134592Z AssertionError: ref: 2025-07-17T09:02:18.6134966Z (tensor(11., device='cuda:0', dtype=torch.bfloat16, grad_fn=), tensor([[-4.9546e-07, 4.1677e-08, -5.2527e-07, ..., 4.9546e-07, 2025-07-17T09:02:18.6135360Z -9.3132e-07, -3.4273e-07], 2025-07-17T09:02:18.6135586Z [ 6.4448e-07, -7.0035e-07, -4.6939e-07, ..., 4.3772e-08, 2025-07-17T09:02:18.6135802Z 6.0722e-07, 1.0058e-06], 2025-07-17T09:02:18.6136011Z [-4.9826e-08, 1.0133e-06, 3.4086e-07, ..., 7.3016e-07, 2025-07-17T09:02:18.6136221Z -4.3027e-07, 8.6427e-07], 2025-07-17T09:02:18.6136402Z ..., 2025-07-17T09:02:18.6136579Z [-6.4448e-07, -8.2329e-07, -6.8545e-07, ..., -9.0152e-07, 2025-07-17T09:02:18.6136795Z 3.0361e-07, -8.9034e-07], 2025-07-17T09:02:18.6137003Z [-9.7603e-07, -6.3702e-07, 2.5705e-07, ..., 8.9034e-07, 2025-07-17T09:02:18.6137223Z 8.6427e-07, 1.9837e-07], 2025-07-17T09:02:18.6137428Z [ 5.0990e-08, -1.0654e-06, -2.1420e-07, ..., 8.7917e-07, 2025-07-17T09:02:18.6137799Z -1.6391e-07, -9.2015e-07]], device='cuda:0', dtype=torch.bfloat16), tensor([[ 8.1956e-08, 2.2054e-05, -2.2769e-05, ..., -1.5125e-06, 2025-07-17T09:02:18.6138143Z 7.4387e-05, -7.9632e-05], 2025-07-17T09:02:18.6138346Z [-4.8399e-05, -9.5367e-05, -4.7207e-05, ..., 5.1975e-05, 2025-07-17T09:02:18.6138564Z -1.0300e-04, -5.3406e-05], 2025-07-17T09:02:18.6138760Z [-1.0431e-05, -2.4438e-05, -1.1623e-05, ..., -1.9968e-06, 2025-07-17T09:02:18.6138974Z -2.7299e-05, -2.1458e-05], 2025-07-17T09:02:18.6139153Z ..., 2025-07-17T09:02:18.6139324Z [ 8.1658e-06, 4.2200e-05, 2.0862e-05, ..., -3.1471e-05, 2025-07-17T09:02:18.6139535Z -3.0547e-06, -8.4043e-06], 2025-07-17T09:02:18.6139737Z [ 3.3140e-05, 2.1577e-05, 8.1062e-06, ..., 1.8358e-05, 2025-07-17T09:02:18.6139953Z -5.0783e-05, -2.2888e-05], 2025-07-17T09:02:18.6140152Z [ 9.0338e-08, 3.3714e-07, 1.3132e-07, ..., 5.7369e-07, 2025-07-17T09:02:18.6140517Z -5.5507e-07, 9.2667e-08]], device='cuda:0', dtype=torch.bfloat16), tensor([-4.1485e-05, -4.0770e-05, -1.0431e-05, ..., -1.0550e-05, 2025-07-17T09:02:18.6140909Z -1.0848e-05, 1.9670e-05], device='cuda:0', dtype=torch.bfloat16)) 2025-07-17T09:02:18.6141148Z act: 2025-07-17T09:02:18.6141337Z (tensor(6.3750, device='cuda:0', dtype=torch.bfloat16, 2025-07-17T09:02:18.6141698Z grad_fn=), tensor([[ 2.6673e-06, -1.6975e-04, -2.5940e-04, ..., 1.6332e-05, 2025-07-17T09:02:18.6142012Z 1.3292e-05, 5.2214e-05], 2025-07-17T09:02:18.6142220Z [ 9.2387e-07, -1.6880e-04, -2.5749e-04, ..., 1.6928e-05, 2025-07-17T09:02:18.6142430Z 1.0967e-05, 5.1498e-05], 2025-07-17T09:02:18.6142635Z [ 1.6317e-06, -1.6975e-04, -2.5940e-04, ..., 1.7047e-05, 2025-07-17T09:02:18.6142990Z 1.2994e-05, 5.1260e-05], 2025-07-17T09:02:18.6143162Z ..., 2025-07-17T09:02:18.6143338Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6143548Z 0.0000e+00, 0.0000e+00], 2025-07-17T09:02:18.6143751Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6143962Z 0.0000e+00, 0.0000e+00], 2025-07-17T09:02:18.6144266Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6144641Z 0.0000e+00, 0.0000e+00]], device='cuda:0', dtype=torch.bfloat16), tensor([[-0.0015, -0.0039, -0.0004, ..., -0.0033, 0.0006, -0.0012], 2025-07-17T09:02:18.6145022Z [-0.0015, -0.0039, -0.0004, ..., -0.0034, 0.0007, -0.0012], 2025-07-17T09:02:18.6145321Z [-0.0015, -0.0039, -0.0004, ..., -0.0033, 0.0006, -0.0012], 2025-07-17T09:02:18.6145541Z ..., 2025-07-17T09:02:18.6145725Z [-0.0015, -0.0039, -0.0005, ..., -0.0033, 0.0006, -0.0013], 2025-07-17T09:02:18.6145968Z [-0.0015, -0.0040, -0.0004, ..., -0.0033, 0.0007, -0.0013], 2025-07-17T09:02:18.6146213Z [-0.0015, -0.0039, -0.0005, ..., -0.0033, 0.0006, -0.0013]], 2025-07-17T09:02:18.6146554Z device='cuda:0', dtype=torch.bfloat16), tensor([-0.3262, -0.3262, -0.3262, ..., -0.3262, -0.3262, -0.3262], 2025-07-17T09:02:18.6146888Z device='cuda:0', dtype=torch.bfloat16)) 2025-07-17T09:02:18.6147035Z 2025-07-17T09:02:18.6147163Z To execute this test, run the following from the base repo dir: 2025-07-17T09:02:18.6147566Z PYTORCH_TEST_WITH_ROCM=1 python test/inductor/test_max_autotune.py TestMaxAutotune.test_linear_and_cel 2025-07-17T09:02:18.6147847Z 2025-07-17T09:02:18.6147994Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2025-07-17T09:02:18.6148331Z ----------------------------- Captured stdout call ----------------------------- 2025-07-17T09:02:18.6148603Z frames [('total', 5), ('ok', 5)] 2025-07-17T09:02:18.6148798Z inline_call [] 2025-07-17T09:02:18.6148965Z unimplemented [] 2025-07-17T09:02:18.6149792Z graph_break [('Unsupported Tensor.backward() call\n Explanation: Dynamo currently does not support tracing `Tensor.backward()`.\n Hint: This graph break is fundamental - it is unlikely that Dynamo will ever be able to trace through your code. Consider finding a workaround.\n\n Developer debug context: call_method TensorVariable() backward () {}\n', 1)] 2025-07-17T09:02:18.6150666Z stats [('calls_captured', 2), ('unique_graphs', 1)] 2025-07-17T09:02:18.6150987Z aot_autograd [('total', 1), ('autograd_cache_miss', 1), ('ok', 1), ('autograd_cache_saved', 1)] 2025-07-17T09:02:18.6152040Z inductor [('triton_bundler_save_kernel', 480), ('benchmarking.InductorBenchmarker.benchmark_gpu', 61), ('async_compile_cache_miss', 40), ('async_compile_cache_hit', 20), ('pattern_matcher_nodes', 12), ('pattern_matcher_count', 7), ('extern_calls', 3), ('fxgraph_cache_miss', 2), ('benchmarking.InductorBenchmarker.benchmark', 2), ('select_algorithm_precompile', 1), ('select_algorithm_autotune', 1), ('inplace_padding', 1)] 2025-07-17T09:02:18.6153094Z aten_mm_info [('aten.addmm_32768_50264_768', 1), ('aten.mm_32768_768_50264', 1), ('aten.mm_50264_768_32768', 1)] 2025-07-17T09:02:18.6153460Z ----------------------------- Captured stderr call ----------------------------- 2025-07-17T09:02:18.6153739Z AUTOTUNE addmm(32768x50264, 32768x768, 768x50264) 2025-07-17T09:02:18.6153958Z strides: [0, 1], [768, 1], [1, 768] 2025-07-17T09:02:18.6154182Z dtypes: torch.bfloat16, torch.bfloat16, torch.bfloat16 2025-07-17T09:02:18.6154425Z bias_addmm 4.9959 ms 100.0% 2025-07-17T09:02:18.6154612Z addmm 8.0962 ms 61.7% 2025-07-17T09:02:18.6154939Z SingleProcess AUTOTUNE benchmarking takes 0.1381 seconds and 0.0002 seconds precompiling for 2 choices 2025-07-17T09:02:18.6155326Z ----------------------------- Captured stdout call ----------------------------- 2025-07-17T09:02:18.6155577Z frames [('total', 5), ('ok', 5)] 2025-07-17T09:02:18.6155764Z inline_call [] 2025-07-17T09:02:18.6156081Z unimplemented [] 2025-07-17T09:02:18.6156884Z graph_break [('Unsupported Tensor.backward() call\n Explanation: Dynamo currently does not support tracing `Tensor.backward()`.\n Hint: This graph break is fundamental - it is unlikely that Dynamo will ever be able to trace through your code. Consider finding a workaround.\n\n Developer debug context: call_method TensorVariable() backward () {}\n', 1)] 2025-07-17T09:02:18.6157852Z stats [('calls_captured', 2), ('unique_graphs', 1)] 2025-07-17T09:02:18.6158177Z aot_autograd [('total', 1), ('autograd_cache_miss', 1), ('ok', 1), ('autograd_cache_saved', 1)] 2025-07-17T09:02:18.6159220Z inductor [('triton_bundler_save_kernel', 480), ('benchmarking.InductorBenchmarker.benchmark_gpu', 61), ('async_compile_cache_miss', 40), ('async_compile_cache_hit', 20), ('pattern_matcher_nodes', 12), ('pattern_matcher_count', 7), ('extern_calls', 3), ('fxgraph_cache_miss', 2), ('benchmarking.InductorBenchmarker.benchmark', 2), ('select_algorithm_precompile', 1), ('select_algorithm_autotune', 1), ('inplace_padding', 1)] 2025-07-17T09:02:18.6160259Z aten_mm_info [('aten.addmm_32768_50264_768', 1), ('aten.mm_32768_768_50264', 1), ('aten.mm_50264_768_32768', 1)] 2025-07-17T09:02:18.6160624Z ----------------------------- Captured stderr call ----------------------------- 2025-07-17T09:02:18.6160899Z AUTOTUNE addmm(32768x50264, 32768x768, 768x50264) 2025-07-17T09:02:18.6161118Z strides: [0, 1], [768, 1], [1, 768] 2025-07-17T09:02:18.6161357Z dtypes: torch.bfloat16, torch.bfloat16, torch.bfloat16 2025-07-17T09:02:18.6161601Z bias_addmm 5.0080 ms 100.0% 2025-07-17T09:02:18.6161787Z addmm 8.2660 ms 60.6% 2025-07-17T09:02:18.6162098Z SingleProcess AUTOTUNE benchmarking takes 0.1391 seconds and 0.0002 seconds precompiling for 2 choices 2025-07-17T09:02:18.6162465Z =================================== FAILURES =================================== 2025-07-17T09:02:18.6162751Z _____________________ TestMaxAutotune.test_linear_and_cel ______________________ 2025-07-17T09:02:18.6163024Z Traceback (most recent call last): 2025-07-17T09:02:18.6163382Z File "/var/lib/jenkins/pytorch/test/inductor/test_max_autotune.py", line 954, in test_linear_and_cel 2025-07-17T09:02:18.6163819Z assert same(expect, actual, tol=1e-2), f"ref:\n{expect}\nact:\n{actual}" 2025-07-17T09:02:18.6164090Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2025-07-17T09:02:18.6164292Z AssertionError: ref: 2025-07-17T09:02:18.6164672Z (tensor(11., device='cuda:0', dtype=torch.bfloat16, grad_fn=), tensor([[-4.9546e-07, 4.1677e-08, -5.2527e-07, ..., 4.9546e-07, 2025-07-17T09:02:18.6165063Z -9.3132e-07, -3.4273e-07], 2025-07-17T09:02:18.6165287Z [ 6.4448e-07, -7.0035e-07, -4.6939e-07, ..., 4.3772e-08, 2025-07-17T09:02:18.6165512Z 6.0722e-07, 1.0058e-06], 2025-07-17T09:02:18.6165715Z [-4.9826e-08, 1.0133e-06, 3.4086e-07, ..., 7.3016e-07, 2025-07-17T09:02:18.6165925Z -4.3027e-07, 8.6427e-07], 2025-07-17T09:02:18.6166096Z ..., 2025-07-17T09:02:18.6166280Z [-6.4448e-07, -8.2329e-07, -6.8545e-07, ..., -9.0152e-07, 2025-07-17T09:02:18.6166494Z 3.0361e-07, -8.9034e-07], 2025-07-17T09:02:18.6166704Z [-9.7603e-07, -6.3702e-07, 2.5705e-07, ..., 8.9034e-07, 2025-07-17T09:02:18.6166928Z 8.6427e-07, 1.9837e-07], 2025-07-17T09:02:18.6167137Z [ 5.0990e-08, -1.0654e-06, -2.1420e-07, ..., 8.7917e-07, 2025-07-17T09:02:18.6167514Z -1.6391e-07, -9.2015e-07]], device='cuda:0', dtype=torch.bfloat16), tensor([[ 8.1956e-08, 2.2054e-05, -2.2769e-05, ..., -1.5125e-06, 2025-07-17T09:02:18.6167860Z 7.4387e-05, -7.9632e-05], 2025-07-17T09:02:18.6168078Z [-4.8399e-05, -9.5367e-05, -4.7207e-05, ..., 5.1975e-05, 2025-07-17T09:02:18.6168294Z -1.0300e-04, -5.3406e-05], 2025-07-17T09:02:18.6168498Z [-1.0431e-05, -2.4438e-05, -1.1623e-05, ..., -1.9968e-06, 2025-07-17T09:02:18.6168716Z -2.7299e-05, -2.1458e-05], 2025-07-17T09:02:18.6168896Z ..., 2025-07-17T09:02:18.6169201Z [ 8.1658e-06, 4.2200e-05, 2.0862e-05, ..., -3.1471e-05, 2025-07-17T09:02:18.6169415Z -3.0547e-06, -8.4043e-06], 2025-07-17T09:02:18.6169629Z [ 3.3140e-05, 2.1577e-05, 8.1062e-06, ..., 1.8358e-05, 2025-07-17T09:02:18.6169849Z -5.0783e-05, -2.2888e-05], 2025-07-17T09:02:18.6170044Z [ 9.0338e-08, 3.3714e-07, 1.3132e-07, ..., 5.7369e-07, 2025-07-17T09:02:18.6170525Z -5.5507e-07, 9.2667e-08]], device='cuda:0', dtype=torch.bfloat16), tensor([-4.1485e-05, -4.0770e-05, -1.0431e-05, ..., -1.0550e-05, 2025-07-17T09:02:18.6170930Z -1.0848e-05, 1.9670e-05], device='cuda:0', dtype=torch.bfloat16)) 2025-07-17T09:02:18.6171165Z act: 2025-07-17T09:02:18.6171345Z (tensor(6.3750, device='cuda:0', dtype=torch.bfloat16, 2025-07-17T09:02:18.6171697Z grad_fn=), tensor([[ 2.6673e-06, -1.6975e-04, -2.5940e-04, ..., 1.6332e-05, 2025-07-17T09:02:18.6172021Z 1.3292e-05, 5.2214e-05], 2025-07-17T09:02:18.6172227Z [ 9.2387e-07, -1.6880e-04, -2.5749e-04, ..., 1.6928e-05, 2025-07-17T09:02:18.6172460Z 1.0967e-05, 5.1498e-05], 2025-07-17T09:02:18.6172656Z [ 1.6317e-06, -1.6975e-04, -2.5940e-04, ..., 1.7047e-05, 2025-07-17T09:02:18.6172867Z 1.2994e-05, 5.1260e-05], 2025-07-17T09:02:18.6173044Z ..., 2025-07-17T09:02:18.6173213Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6173436Z 0.0000e+00, 0.0000e+00], 2025-07-17T09:02:18.6173636Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6173859Z 0.0000e+00, 0.0000e+00], 2025-07-17T09:02:18.6174064Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6174433Z 0.0000e+00, 0.0000e+00]], device='cuda:0', dtype=torch.bfloat16), tensor([[-0.0015, -0.0039, -0.0004, ..., -0.0033, 0.0006, -0.0012], 2025-07-17T09:02:18.6174819Z [-0.0015, -0.0039, -0.0004, ..., -0.0034, 0.0007, -0.0012], 2025-07-17T09:02:18.6175080Z [-0.0015, -0.0039, -0.0004, ..., -0.0033, 0.0006, -0.0012], 2025-07-17T09:02:18.6175292Z ..., 2025-07-17T09:02:18.6175473Z [-0.0015, -0.0039, -0.0005, ..., -0.0033, 0.0006, -0.0013], 2025-07-17T09:02:18.6175709Z [-0.0015, -0.0040, -0.0004, ..., -0.0033, 0.0007, -0.0013], 2025-07-17T09:02:18.6175964Z [-0.0015, -0.0039, -0.0005, ..., -0.0033, 0.0006, -0.0013]], 2025-07-17T09:02:18.6176318Z device='cuda:0', dtype=torch.bfloat16), tensor([-0.3262, -0.3262, -0.3262, ..., -0.3262, -0.3262, -0.3262], 2025-07-17T09:02:18.6176646Z device='cuda:0', dtype=torch.bfloat16)) 2025-07-17T09:02:18.6176794Z 2025-07-17T09:02:18.6176917Z To execute this test, run the following from the base repo dir: 2025-07-17T09:02:18.6177313Z PYTORCH_TEST_WITH_ROCM=1 python test/inductor/test_max_autotune.py TestMaxAutotune.test_linear_and_cel 2025-07-17T09:02:18.6177593Z 2025-07-17T09:02:18.6177739Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2025-07-17T09:02:18.6178074Z ----------------------------- Captured stdout call ----------------------------- 2025-07-17T09:02:18.6178331Z frames [('total', 5), ('ok', 5)] 2025-07-17T09:02:18.6178520Z inline_call [] 2025-07-17T09:02:18.6178682Z unimplemented [] 2025-07-17T09:02:18.6179513Z graph_break [('Unsupported Tensor.backward() call\n Explanation: Dynamo currently does not support tracing `Tensor.backward()`.\n Hint: This graph break is fundamental - it is unlikely that Dynamo will ever be able to trace through your code. Consider finding a workaround.\n\n Developer debug context: call_method TensorVariable() backward () {}\n', 1)] 2025-07-17T09:02:18.6180372Z stats [('calls_captured', 2), ('unique_graphs', 1)] 2025-07-17T09:02:18.6180696Z aot_autograd [('total', 1), ('autograd_cache_miss', 1), ('ok', 1), ('autograd_cache_saved', 1)] 2025-07-17T09:02:18.6181743Z inductor [('triton_bundler_save_kernel', 480), ('benchmarking.InductorBenchmarker.benchmark_gpu', 61), ('async_compile_cache_miss', 40), ('async_compile_cache_hit', 20), ('pattern_matcher_nodes', 12), ('pattern_matcher_count', 7), ('extern_calls', 3), ('fxgraph_cache_miss', 2), ('benchmarking.InductorBenchmarker.benchmark', 2), ('select_algorithm_precompile', 1), ('select_algorithm_autotune', 1), ('inplace_padding', 1)] 2025-07-17T09:02:18.6182926Z aten_mm_info [('aten.addmm_32768_50264_768', 1), ('aten.mm_32768_768_50264', 1), ('aten.mm_50264_768_32768', 1)] 2025-07-17T09:02:18.6183394Z ----------------------------- Captured stderr call ----------------------------- 2025-07-17T09:02:18.6183674Z AUTOTUNE addmm(32768x50264, 32768x768, 768x50264) 2025-07-17T09:02:18.6183892Z strides: [0, 1], [768, 1], [1, 768] 2025-07-17T09:02:18.6184122Z dtypes: torch.bfloat16, torch.bfloat16, torch.bfloat16 2025-07-17T09:02:18.6184361Z bias_addmm 4.9959 ms 100.0% 2025-07-17T09:02:18.6184552Z addmm 8.0962 ms 61.7% 2025-07-17T09:02:18.6184871Z SingleProcess AUTOTUNE benchmarking takes 0.1381 seconds and 0.0002 seconds precompiling for 2 choices 2025-07-17T09:02:18.6185328Z ----------------------------- Captured stdout call ----------------------------- 2025-07-17T09:02:18.6185590Z frames [('total', 5), ('ok', 5)] 2025-07-17T09:02:18.6185780Z inline_call [] 2025-07-17T09:02:18.6185947Z unimplemented [] 2025-07-17T09:02:18.6186760Z graph_break [('Unsupported Tensor.backward() call\n Explanation: Dynamo currently does not support tracing `Tensor.backward()`.\n Hint: This graph break is fundamental - it is unlikely that Dynamo will ever be able to trace through your code. Consider finding a workaround.\n\n Developer debug context: call_method TensorVariable() backward () {}\n', 1)] 2025-07-17T09:02:18.6187608Z stats [('calls_captured', 2), ('unique_graphs', 1)] 2025-07-17T09:02:18.6187917Z aot_autograd [('total', 1), ('autograd_cache_miss', 1), ('ok', 1), ('autograd_cache_saved', 1)] 2025-07-17T09:02:18.6188951Z inductor [('triton_bundler_save_kernel', 480), ('benchmarking.InductorBenchmarker.benchmark_gpu', 61), ('async_compile_cache_miss', 40), ('async_compile_cache_hit', 20), ('pattern_matcher_nodes', 12), ('pattern_matcher_count', 7), ('extern_calls', 3), ('fxgraph_cache_miss', 2), ('benchmarking.InductorBenchmarker.benchmark', 2), ('select_algorithm_precompile', 1), ('select_algorithm_autotune', 1), ('inplace_padding', 1)] 2025-07-17T09:02:18.6189990Z aten_mm_info [('aten.addmm_32768_50264_768', 1), ('aten.mm_32768_768_50264', 1), ('aten.mm_50264_768_32768', 1)] 2025-07-17T09:02:18.6190357Z ----------------------------- Captured stderr call ----------------------------- 2025-07-17T09:02:18.6190628Z AUTOTUNE addmm(32768x50264, 32768x768, 768x50264) 2025-07-17T09:02:18.6190854Z strides: [0, 1], [768, 1], [1, 768] 2025-07-17T09:02:18.6191081Z dtypes: torch.bfloat16, torch.bfloat16, torch.bfloat16 2025-07-17T09:02:18.6280792Z bias_addmm 5.0080 ms 100.0% 2025-07-17T09:02:18.6281165Z addmm 8.2660 ms 60.6% 2025-07-17T09:02:18.6281625Z SingleProcess AUTOTUNE benchmarking takes 0.1391 seconds and 0.0002 seconds precompiling for 2 choices 2025-07-17T09:02:18.6282082Z ----------------------------- Captured stdout call ----------------------------- 2025-07-17T09:02:18.6282383Z frames [('total', 5), ('ok', 5)] 2025-07-17T09:02:18.6282594Z inline_call [] 2025-07-17T09:02:18.6282758Z unimplemented [] 2025-07-17T09:02:18.6283616Z graph_break [('Unsupported Tensor.backward() call\n Explanation: Dynamo currently does not support tracing `Tensor.backward()`.\n Hint: This graph break is fundamental - it is unlikely that Dynamo will ever be able to trace through your code. Consider finding a workaround.\n\n Developer debug context: call_method TensorVariable() backward () {}\n', 1)] 2025-07-17T09:02:18.6284502Z stats [('calls_captured', 2), ('unique_graphs', 1)] 2025-07-17T09:02:18.6284839Z aot_autograd [('total', 1), ('autograd_cache_miss', 1), ('ok', 1), ('autograd_cache_saved', 1)] 2025-07-17T09:02:18.6285907Z inductor [('triton_bundler_save_kernel', 480), ('benchmarking.InductorBenchmarker.benchmark_gpu', 61), ('async_compile_cache_miss', 40), ('async_compile_cache_hit', 20), ('pattern_matcher_nodes', 12), ('pattern_matcher_count', 7), ('extern_calls', 3), ('fxgraph_cache_miss', 2), ('benchmarking.InductorBenchmarker.benchmark', 2), ('select_algorithm_precompile', 1), ('select_algorithm_autotune', 1), ('inplace_padding', 1)] 2025-07-17T09:02:18.6287180Z aten_mm_info [('aten.addmm_32768_50264_768', 1), ('aten.mm_32768_768_50264', 1), ('aten.mm_50264_768_32768', 1)] 2025-07-17T09:02:18.6287689Z ----------------------------- Captured stderr call ----------------------------- 2025-07-17T09:02:18.6287986Z AUTOTUNE addmm(32768x50264, 32768x768, 768x50264) 2025-07-17T09:02:18.6288224Z strides: [0, 1], [768, 1], [1, 768] 2025-07-17T09:02:18.6288469Z dtypes: torch.bfloat16, torch.bfloat16, torch.bfloat16 2025-07-17T09:02:18.6288722Z bias_addmm 5.0475 ms 100.0% 2025-07-17T09:02:18.6288920Z addmm 8.3290 ms 60.6% 2025-07-17T09:02:18.6289251Z SingleProcess AUTOTUNE benchmarking takes 0.1308 seconds and 0.0002 seconds precompiling for 2 choices 2025-07-17T09:02:18.6289921Z - generated xml file: /var/lib/jenkins/pytorch/test/test-reports/python-pytest/inductor.test_max_autotune/inductor.test_max_autotune-6e62b0eb4fd2a2c4.xml - 2025-07-17T09:02:18.6290460Z =========================== short test summary info ============================ 2025-07-17T09:02:18.6290867Z FAILED [6.2102s] inductor/test_max_autotune.py::TestMaxAutotune::test_linear_and_cel - AssertionError: ref: 2025-07-17T09:02:18.6291433Z (tensor(11., device='cuda:0', dtype=torch.bfloat16, grad_fn=), tensor([[-4.9546e-07, 4.1677e-08, -5.2527e-07, ..., 4.9546e-07, 2025-07-17T09:02:18.6291835Z -9.3132e-07, -3.4273e-07], 2025-07-17T09:02:18.6292075Z [ 6.4448e-07, -7.0035e-07, -4.6939e-07, ..., 4.3772e-08, 2025-07-17T09:02:18.6292312Z 6.0722e-07, 1.0058e-06], 2025-07-17T09:02:18.6292538Z [-4.9826e-08, 1.0133e-06, 3.4086e-07, ..., 7.3016e-07, 2025-07-17T09:02:18.6292781Z -4.3027e-07, 8.6427e-07], 2025-07-17T09:02:18.6292975Z ..., 2025-07-17T09:02:18.6293179Z [-6.4448e-07, -8.2329e-07, -6.8545e-07, ..., -9.0152e-07, 2025-07-17T09:02:18.6293406Z 3.0361e-07, -8.9034e-07], 2025-07-17T09:02:18.6293619Z [-9.7603e-07, -6.3702e-07, 2.5705e-07, ..., 8.9034e-07, 2025-07-17T09:02:18.6293839Z 8.6427e-07, 1.9837e-07], 2025-07-17T09:02:18.6294049Z [ 5.0990e-08, -1.0654e-06, -2.1420e-07, ..., 8.7917e-07, 2025-07-17T09:02:18.6294438Z -1.6391e-07, -9.2015e-07]], device='cuda:0', dtype=torch.bfloat16), tensor([[ 8.1956e-08, 2.2054e-05, -2.2769e-05, ..., -1.5125e-06, 2025-07-17T09:02:18.6294784Z 7.4387e-05, -7.9632e-05], 2025-07-17T09:02:18.6294999Z [-4.8399e-05, -9.5367e-05, -4.7207e-05, ..., 5.1975e-05, 2025-07-17T09:02:18.6295221Z -1.0300e-04, -5.3406e-05], 2025-07-17T09:02:18.6295431Z [-1.0431e-05, -2.4438e-05, -1.1623e-05, ..., -1.9968e-06, 2025-07-17T09:02:18.6295650Z -2.7299e-05, -2.1458e-05], 2025-07-17T09:02:18.6295831Z ..., 2025-07-17T09:02:18.6296014Z [ 8.1658e-06, 4.2200e-05, 2.0862e-05, ..., -3.1471e-05, 2025-07-17T09:02:18.6296236Z -3.0547e-06, -8.4043e-06], 2025-07-17T09:02:18.6296441Z [ 3.3140e-05, 2.1577e-05, 8.1062e-06, ..., 1.8358e-05, 2025-07-17T09:02:18.6296656Z -5.0783e-05, -2.2888e-05], 2025-07-17T09:02:18.6296865Z [ 9.0338e-08, 3.3714e-07, 1.3132e-07, ..., 5.7369e-07, 2025-07-17T09:02:18.6297231Z -5.5507e-07, 9.2667e-08]], device='cuda:0', dtype=torch.bfloat16), tensor([-4.1485e-05, -4.0770e-05, -1.0431e-05, ..., -1.0550e-05, 2025-07-17T09:02:18.6297632Z -1.0848e-05, 1.9670e-05], device='cuda:0', dtype=torch.bfloat16)) 2025-07-17T09:02:18.6297879Z act: 2025-07-17T09:02:18.6298082Z (tensor(6.3750, device='cuda:0', dtype=torch.bfloat16, 2025-07-17T09:02:18.6298450Z grad_fn=), tensor([[ 2.6673e-06, -1.6975e-04, -2.5940e-04, ..., 1.6332e-05, 2025-07-17T09:02:18.6298771Z 1.3292e-05, 5.2214e-05], 2025-07-17T09:02:18.6299132Z [ 9.2387e-07, -1.6880e-04, -2.5749e-04, ..., 1.6928e-05, 2025-07-17T09:02:18.6299352Z 1.0967e-05, 5.1498e-05], 2025-07-17T09:02:18.6299564Z [ 1.6317e-06, -1.6975e-04, -2.5940e-04, ..., 1.7047e-05, 2025-07-17T09:02:18.6299787Z 1.2994e-05, 5.1260e-05], 2025-07-17T09:02:18.6299971Z ..., 2025-07-17T09:02:18.6300156Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6300493Z 0.0000e+00, 0.0000e+00], 2025-07-17T09:02:18.6300701Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6300919Z 0.0000e+00, 0.0000e+00], 2025-07-17T09:02:18.6301116Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6301497Z 0.0000e+00, 0.0000e+00]], device='cuda:0', dtype=torch.bfloat16), tensor([[-0.0015, -0.0039, -0.0004, ..., -0.0033, 0.0006, -0.0012], 2025-07-17T09:02:18.6301891Z [-0.0015, -0.0039, -0.0004, ..., -0.0034, 0.0007, -0.0012], 2025-07-17T09:02:18.6302155Z [-0.0015, -0.0039, -0.0004, ..., -0.0033, 0.0006, -0.0012], 2025-07-17T09:02:18.6302376Z ..., 2025-07-17T09:02:18.6302556Z [-0.0015, -0.0039, -0.0005, ..., -0.0033, 0.0006, -0.0013], 2025-07-17T09:02:18.6302800Z [-0.0015, -0.0040, -0.0004, ..., -0.0033, 0.0007, -0.0013], 2025-07-17T09:02:18.6303069Z [-0.0015, -0.0039, -0.0005, ..., -0.0033, 0.0006, -0.0013]], 2025-07-17T09:02:18.6303422Z device='cuda:0', dtype=torch.bfloat16), tensor([-0.3262, -0.3262, -0.3262, ..., -0.3262, -0.3262, -0.3262], 2025-07-17T09:02:18.6303767Z device='cuda:0', dtype=torch.bfloat16)) 2025-07-17T09:02:18.6303911Z 2025-07-17T09:02:18.6304049Z To execute this test, run the following from the base repo dir: 2025-07-17T09:02:18.6304455Z PYTORCH_TEST_WITH_ROCM=1 python test/inductor/test_max_autotune.py TestMaxAutotune.test_linear_and_cel 2025-07-17T09:02:18.6304728Z 2025-07-17T09:02:18.6304894Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2025-07-17T09:02:18.6305219Z !!!!!!!!!!!!!!!!!!!!!!!!!! stopping after 1 failures !!!!!!!!!!!!!!!!!!!!!!!!!!! 2025-07-17T09:02:18.6305626Z ========== 1 failed, 5 passed, 4 skipped, 2 rerun in 63.65s (0:01:03) ========== 2025-07-17T09:02:18.6305892Z Got exit code 1 2025-07-17T09:02:18.6306058Z Retrying single test... 2025-07-17T09:02:18.6306910Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T09:02:18.6307736Z import pkg_resources 2025-07-17T09:02:18.6309242Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py:1597: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/Context.cpp:78.) 2025-07-17T09:02:18.6310741Z _C._set_float32_matmul_precision(precision) 2025-07-17T09:02:18.6311196Z Test results will be stored in test-reports/python-pytest/inductor.test_max_autotune/inductor.test_max_autotune-54e0e28eb9bfb2ae.xml 2025-07-17T09:02:18.6311643Z ============================= test session starts ============================== 2025-07-17T09:02:18.6311994Z platform linux -- Python 3.12.11, pytest-7.3.2, pluggy-1.6.0 -- /opt/conda/envs/py_3.12/bin/python 2025-07-17T09:02:18.6312302Z cachedir: .pytest_cache 2025-07-17T09:02:18.6312682Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2025-07-17T09:02:18.6313353Z rootdir: /var/lib/jenkins/pytorch 2025-07-17T09:02:18.6313561Z configfile: pytest.ini 2025-07-17T09:02:18.6313948Z plugins: rerunfailures-14.0, subtests-0.13.1, flakefinder-1.1.0, xdist-3.3.1, xdoctest-1.1.0, hypothesis-5.35.1, cpp-2.3.0, typeguard-4.3.0 2025-07-17T09:02:18.6314407Z collecting ... collected 132 items / 71 deselected / 61 selected 2025-07-17T09:02:18.6315010Z stepcurrent: skipping 9 already run items. Running only test/inductor/test_max_autotune.py::TestMaxAutotune::test_linear_and_cel 2025-07-17T09:02:18.6315418Z Running 1 items in this shard 2025-07-17T09:02:18.6315557Z 2025-07-17T09:02:18.6318543Z inductor/test_max_autotune.py::TestMaxAutotune::test_linear_and_cel [W717 08:56:05.650908731 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6319046Z 2025-07-17T09:02:18.6319321Z [W717 08:56:06.352086277 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6319671Z 2025-07-17T09:02:18.6319922Z [W717 08:56:06.354532576 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6320241Z 2025-07-17T09:02:18.6320490Z [W717 08:56:06.354641778 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6320802Z 2025-07-17T09:02:18.6321041Z [W717 08:56:06.355055522 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6321352Z 2025-07-17T09:02:18.6321590Z [W717 08:56:06.355151815 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6321898Z 2025-07-17T09:02:18.6322132Z [W717 08:56:06.355444419 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6322449Z 2025-07-17T09:02:18.6322685Z [W717 08:56:06.355539590 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6322999Z 2025-07-17T09:02:18.6323235Z [W717 08:56:06.355816161 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6323546Z 2025-07-17T09:02:18.6323784Z [W717 08:56:06.355911602 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6324089Z 2025-07-17T09:02:18.6324348Z E0717 08:56:12.732000 70486 site-packages/torch/_dynamo/utils.py:3063] Accuracy failed: allclose not within tol=0.01 2025-07-17T09:02:18.6324738Z ('RERUN', {'yellow': True}) [20.2677s] [100%] 2025-07-17T09:02:18.6325363Z inductor/test_max_autotune.py::TestMaxAutotune::test_linear_and_cel [W717 08:56:13.714685547 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6325836Z 2025-07-17T09:02:18.6326090Z [W717 08:56:13.714875078 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6326408Z 2025-07-17T09:02:18.6326655Z [W717 08:56:13.715233810 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6326967Z 2025-07-17T09:02:18.6327205Z [W717 08:56:13.715330203 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6327515Z 2025-07-17T09:02:18.6327752Z [W717 08:56:13.715617339 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6328054Z 2025-07-17T09:02:18.6328289Z [W717 08:56:13.715710207 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6328821Z 2025-07-17T09:02:18.6329061Z [W717 08:56:13.715949452 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6329376Z 2025-07-17T09:02:18.6329724Z [W717 08:56:13.716060526 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6330050Z 2025-07-17T09:02:18.6330304Z [W717 08:56:13.716294454 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6330616Z 2025-07-17T09:02:18.6330857Z [W717 08:56:13.716383215 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6331171Z 2025-07-17T09:02:18.6331411Z E0717 08:56:18.998000 70486 site-packages/torch/_dynamo/utils.py:3063] Accuracy failed: allclose not within tol=0.01 2025-07-17T09:02:18.6331779Z ('RERUN', {'yellow': True}) [6.1331s] [100%] 2025-07-17T09:02:18.6332326Z inductor/test_max_autotune.py::TestMaxAutotune::test_linear_and_cel [W717 08:56:19.864838615 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6332788Z 2025-07-17T09:02:18.6333043Z [W717 08:56:19.865034495 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6333356Z 2025-07-17T09:02:18.6333595Z [W717 08:56:19.865409462 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6333905Z 2025-07-17T09:02:18.6334154Z [W717 08:56:19.865533976 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6334455Z 2025-07-17T09:02:18.6334703Z [W717 08:56:19.865805629 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6335001Z 2025-07-17T09:02:18.6335250Z [W717 08:56:19.865901481 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6335546Z 2025-07-17T09:02:18.6335804Z [W717 08:56:19.866142770 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6336112Z 2025-07-17T09:02:18.6336363Z [W717 08:56:19.866246423 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6336662Z 2025-07-17T09:02:18.6336907Z [W717 08:56:19.866482373 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6337202Z 2025-07-17T09:02:18.6337450Z [W717 08:56:19.866573408 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6337754Z 2025-07-17T09:02:18.6337983Z E0717 08:56:25.201000 70486 site-packages/torch/_dynamo/utils.py:3063] Accuracy failed: allclose not within tol=0.01 2025-07-17T09:02:18.6338334Z FAILED [6.1974s] [100%] 2025-07-17T09:02:18.6338442Z 2025-07-17T09:02:18.6338549Z ==================================== RERUNS ==================================== 2025-07-17T09:02:18.6338861Z _____________________ TestMaxAutotune.test_linear_and_cel ______________________ 2025-07-17T09:02:18.6339130Z Traceback (most recent call last): 2025-07-17T09:02:18.6339504Z File "/var/lib/jenkins/pytorch/test/inductor/test_max_autotune.py", line 954, in test_linear_and_cel 2025-07-17T09:02:18.6339946Z assert same(expect, actual, tol=1e-2), f"ref:\n{expect}\nact:\n{actual}" 2025-07-17T09:02:18.6340225Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2025-07-17T09:02:18.6340433Z AssertionError: ref: 2025-07-17T09:02:18.6340818Z (tensor(11., device='cuda:0', dtype=torch.bfloat16, grad_fn=), tensor([[-4.9546e-07, 4.1677e-08, -5.2527e-07, ..., 4.9546e-07, 2025-07-17T09:02:18.6341362Z -9.3132e-07, -3.4273e-07], 2025-07-17T09:02:18.6341600Z [ 6.4448e-07, -7.0035e-07, -4.6939e-07, ..., 4.3772e-08, 2025-07-17T09:02:18.6341820Z 6.0722e-07, 1.0058e-06], 2025-07-17T09:02:18.6342025Z [-4.9826e-08, 1.0133e-06, 3.4086e-07, ..., 7.3016e-07, 2025-07-17T09:02:18.6342358Z -4.3027e-07, 8.6427e-07], 2025-07-17T09:02:18.6342542Z ..., 2025-07-17T09:02:18.6342720Z [-6.4448e-07, -8.2329e-07, -6.8545e-07, ..., -9.0152e-07, 2025-07-17T09:02:18.6342929Z 3.0361e-07, -8.9034e-07], 2025-07-17T09:02:18.6343125Z [-9.7603e-07, -6.3702e-07, 2.5705e-07, ..., 8.9034e-07, 2025-07-17T09:02:18.6343339Z 8.6427e-07, 1.9837e-07], 2025-07-17T09:02:18.6343542Z [ 5.0990e-08, -1.0654e-06, -2.1420e-07, ..., 8.7917e-07, 2025-07-17T09:02:18.6343911Z -1.6391e-07, -9.2015e-07]], device='cuda:0', dtype=torch.bfloat16), tensor([[ 8.1956e-08, 2.2054e-05, -2.2769e-05, ..., -1.5125e-06, 2025-07-17T09:02:18.6344252Z 7.4387e-05, -7.9632e-05], 2025-07-17T09:02:18.6344446Z [-4.8399e-05, -9.5367e-05, -4.7207e-05, ..., 5.1975e-05, 2025-07-17T09:02:18.6344654Z -1.0300e-04, -5.3406e-05], 2025-07-17T09:02:18.6344846Z [-1.0431e-05, -2.4438e-05, -1.1623e-05, ..., -1.9968e-06, 2025-07-17T09:02:18.6345058Z -2.7299e-05, -2.1458e-05], 2025-07-17T09:02:18.6345227Z ..., 2025-07-17T09:02:18.6345486Z [ 8.1658e-06, 4.2200e-05, 2.0862e-05, ..., -3.1471e-05, 2025-07-17T09:02:18.6345699Z -3.0547e-06, -8.4043e-06], 2025-07-17T09:02:18.6345902Z [ 3.3140e-05, 2.1577e-05, 8.1062e-06, ..., 1.8358e-05, 2025-07-17T09:02:18.6346101Z -5.0783e-05, -2.2888e-05], 2025-07-17T09:02:18.6346301Z [ 9.0338e-08, 3.3714e-07, 1.3132e-07, ..., 5.7369e-07, 2025-07-17T09:02:18.6346654Z -5.5507e-07, 9.2667e-08]], device='cuda:0', dtype=torch.bfloat16), tensor([-4.1485e-05, -4.0770e-05, -1.0431e-05, ..., -1.0550e-05, 2025-07-17T09:02:18.6347048Z -1.0848e-05, 1.9670e-05], device='cuda:0', dtype=torch.bfloat16)) 2025-07-17T09:02:18.6347282Z act: 2025-07-17T09:02:18.6347469Z (tensor(6.3750, device='cuda:0', dtype=torch.bfloat16, 2025-07-17T09:02:18.6347860Z grad_fn=), tensor([[ 2.6673e-06, -1.6975e-04, -2.5940e-04, ..., 1.6332e-05, 2025-07-17T09:02:18.6348178Z 1.3292e-05, 5.2214e-05], 2025-07-17T09:02:18.6348399Z [ 9.2387e-07, -1.6880e-04, -2.5749e-04, ..., 1.6928e-05, 2025-07-17T09:02:18.6348622Z 1.0967e-05, 5.1498e-05], 2025-07-17T09:02:18.6348842Z [ 1.6317e-06, -1.6975e-04, -2.5940e-04, ..., 1.7047e-05, 2025-07-17T09:02:18.6349050Z 1.2994e-05, 5.1260e-05], 2025-07-17T09:02:18.6349221Z ..., 2025-07-17T09:02:18.6349407Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6349637Z 0.0000e+00, 0.0000e+00], 2025-07-17T09:02:18.6349840Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6350047Z 0.0000e+00, 0.0000e+00], 2025-07-17T09:02:18.6350240Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6350610Z 0.0000e+00, 0.0000e+00]], device='cuda:0', dtype=torch.bfloat16), tensor([[-0.0015, -0.0039, -0.0004, ..., -0.0033, 0.0006, -0.0012], 2025-07-17T09:02:18.6350977Z [-0.0015, -0.0039, -0.0004, ..., -0.0034, 0.0007, -0.0012], 2025-07-17T09:02:18.6351218Z [-0.0015, -0.0039, -0.0004, ..., -0.0033, 0.0006, -0.0012], 2025-07-17T09:02:18.6351427Z ..., 2025-07-17T09:02:18.6351602Z [-0.0015, -0.0039, -0.0005, ..., -0.0033, 0.0006, -0.0013], 2025-07-17T09:02:18.6351825Z [-0.0015, -0.0039, -0.0005, ..., -0.0033, 0.0006, -0.0013], 2025-07-17T09:02:18.6352055Z [-0.0015, -0.0039, -0.0005, ..., -0.0033, 0.0006, -0.0013]], 2025-07-17T09:02:18.6352556Z device='cuda:0', dtype=torch.bfloat16), tensor([-0.3262, -0.3262, -0.3262, ..., -0.3262, -0.3262, -0.3262], 2025-07-17T09:02:18.6352885Z device='cuda:0', dtype=torch.bfloat16)) 2025-07-17T09:02:18.6353026Z 2025-07-17T09:02:18.6353151Z To execute this test, run the following from the base repo dir: 2025-07-17T09:02:18.6353680Z PYTORCH_TEST_WITH_ROCM=1 python test/inductor/test_max_autotune.py TestMaxAutotune.test_linear_and_cel 2025-07-17T09:02:18.6353951Z 2025-07-17T09:02:18.6354096Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2025-07-17T09:02:18.6354423Z ----------------------------- Captured stdout call ----------------------------- 2025-07-17T09:02:18.6354679Z frames [('total', 5), ('ok', 5)] 2025-07-17T09:02:18.6354850Z inline_call [] 2025-07-17T09:02:18.6354997Z unimplemented [] 2025-07-17T09:02:18.6355818Z graph_break [('Unsupported Tensor.backward() call\n Explanation: Dynamo currently does not support tracing `Tensor.backward()`.\n Hint: This graph break is fundamental - it is unlikely that Dynamo will ever be able to trace through your code. Consider finding a workaround.\n\n Developer debug context: call_method TensorVariable() backward () {}\n', 1)] 2025-07-17T09:02:18.6356695Z stats [('calls_captured', 2), ('unique_graphs', 1)] 2025-07-17T09:02:18.6357671Z inductor [('triton_bundler_save_kernel', 480), ('benchmarking.InductorBenchmarker.benchmark_gpu', 61), ('async_compile_cache_miss', 30), ('pattern_matcher_nodes', 12), ('async_compile_cache_hit', 10), ('pattern_matcher_count', 7), ('extern_calls', 3), ('fxgraph_cache_miss', 2), ('benchmarking.InductorBenchmarker.benchmark', 2), ('select_algorithm_precompile', 1), ('select_algorithm_autotune', 1), ('inplace_padding', 1)] 2025-07-17T09:02:18.6358688Z aot_autograd [('total', 1), ('autograd_cache_miss', 1), ('ok', 1), ('autograd_cache_saved', 1)] 2025-07-17T09:02:18.6359095Z aten_mm_info [('aten.addmm_32768_50264_768', 1), ('aten.mm_32768_768_50264', 1), ('aten.mm_50264_768_32768', 1)] 2025-07-17T09:02:18.6359459Z ----------------------------- Captured stderr call ----------------------------- 2025-07-17T09:02:18.6359731Z AUTOTUNE addmm(32768x50264, 32768x768, 768x50264) 2025-07-17T09:02:18.6359942Z strides: [0, 1], [768, 1], [1, 768] 2025-07-17T09:02:18.6360167Z dtypes: torch.bfloat16, torch.bfloat16, torch.bfloat16 2025-07-17T09:02:18.6360401Z bias_addmm 4.9707 ms 100.0% 2025-07-17T09:02:18.6360579Z addmm 8.3943 ms 59.2% 2025-07-17T09:02:18.6360894Z SingleProcess AUTOTUNE benchmarking takes 0.1456 seconds and 0.0002 seconds precompiling for 2 choices 2025-07-17T09:02:18.6361291Z _____________________ TestMaxAutotune.test_linear_and_cel ______________________ 2025-07-17T09:02:18.6361561Z Traceback (most recent call last): 2025-07-17T09:02:18.6361896Z File "/var/lib/jenkins/pytorch/test/inductor/test_max_autotune.py", line 954, in test_linear_and_cel 2025-07-17T09:02:18.6362304Z assert same(expect, actual, tol=1e-2), f"ref:\n{expect}\nact:\n{actual}" 2025-07-17T09:02:18.6362565Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2025-07-17T09:02:18.6362760Z AssertionError: ref: 2025-07-17T09:02:18.6363120Z (tensor(11., device='cuda:0', dtype=torch.bfloat16, grad_fn=), tensor([[-4.9546e-07, 4.1677e-08, -5.2527e-07, ..., 4.9546e-07, 2025-07-17T09:02:18.6363497Z -9.3132e-07, -3.4273e-07], 2025-07-17T09:02:18.6363716Z [ 6.4448e-07, -7.0035e-07, -4.6939e-07, ..., 4.3772e-08, 2025-07-17T09:02:18.6363927Z 6.0722e-07, 1.0058e-06], 2025-07-17T09:02:18.6364119Z [-4.9826e-08, 1.0133e-06, 3.4086e-07, ..., 7.3016e-07, 2025-07-17T09:02:18.6364331Z -4.3027e-07, 8.6427e-07], 2025-07-17T09:02:18.6364503Z ..., 2025-07-17T09:02:18.6364676Z [-6.4448e-07, -8.2329e-07, -6.8545e-07, ..., -9.0152e-07, 2025-07-17T09:02:18.6364887Z 3.0361e-07, -8.9034e-07], 2025-07-17T09:02:18.6365083Z [-9.7603e-07, -6.3702e-07, 2.5705e-07, ..., 8.9034e-07, 2025-07-17T09:02:18.6365295Z 8.6427e-07, 1.9837e-07], 2025-07-17T09:02:18.6365655Z [ 5.0990e-08, -1.0654e-06, -2.1420e-07, ..., 8.7917e-07, 2025-07-17T09:02:18.6366015Z -1.6391e-07, -9.2015e-07]], device='cuda:0', dtype=torch.bfloat16), tensor([[ 8.1956e-08, 2.2054e-05, -2.2769e-05, ..., -1.5125e-06, 2025-07-17T09:02:18.6366358Z 7.4387e-05, -7.9632e-05], 2025-07-17T09:02:18.6366566Z [-4.8399e-05, -9.5367e-05, -4.7207e-05, ..., 5.1975e-05, 2025-07-17T09:02:18.6366887Z -1.0300e-04, -5.3406e-05], 2025-07-17T09:02:18.6367099Z [-1.0431e-05, -2.4438e-05, -1.1623e-05, ..., -1.9968e-06, 2025-07-17T09:02:18.6367319Z -2.7299e-05, -2.1458e-05], 2025-07-17T09:02:18.6367496Z ..., 2025-07-17T09:02:18.6367674Z [ 8.1658e-06, 4.2200e-05, 2.0862e-05, ..., -3.1471e-05, 2025-07-17T09:02:18.6367890Z -3.0547e-06, -8.4043e-06], 2025-07-17T09:02:18.6368083Z [ 3.3140e-05, 2.1577e-05, 8.1062e-06, ..., 1.8358e-05, 2025-07-17T09:02:18.6368294Z -5.0783e-05, -2.2888e-05], 2025-07-17T09:02:18.6368504Z [ 9.0338e-08, 3.3714e-07, 1.3132e-07, ..., 5.7369e-07, 2025-07-17T09:02:18.6368863Z -5.5507e-07, 9.2667e-08]], device='cuda:0', dtype=torch.bfloat16), tensor([-4.1485e-05, -4.0770e-05, -1.0431e-05, ..., -1.0550e-05, 2025-07-17T09:02:18.6369255Z -1.0848e-05, 1.9670e-05], device='cuda:0', dtype=torch.bfloat16)) 2025-07-17T09:02:18.6369499Z act: 2025-07-17T09:02:18.6369694Z (tensor(6.3750, device='cuda:0', dtype=torch.bfloat16, 2025-07-17T09:02:18.6370046Z grad_fn=), tensor([[ 2.6673e-06, -1.6975e-04, -2.5940e-04, ..., 1.6332e-05, 2025-07-17T09:02:18.6370366Z 1.3292e-05, 5.2214e-05], 2025-07-17T09:02:18.6370575Z [ 9.2387e-07, -1.6880e-04, -2.5749e-04, ..., 1.6928e-05, 2025-07-17T09:02:18.6370793Z 1.0967e-05, 5.1498e-05], 2025-07-17T09:02:18.6371004Z [ 1.6317e-06, -1.6975e-04, -2.5940e-04, ..., 1.7047e-05, 2025-07-17T09:02:18.6371229Z 1.2994e-05, 5.1260e-05], 2025-07-17T09:02:18.6371413Z ..., 2025-07-17T09:02:18.6371580Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6371791Z 0.0000e+00, 0.0000e+00], 2025-07-17T09:02:18.6371991Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6372205Z 0.0000e+00, 0.0000e+00], 2025-07-17T09:02:18.6372406Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6372789Z 0.0000e+00, 0.0000e+00]], device='cuda:0', dtype=torch.bfloat16), tensor([[-0.0015, -0.0039, -0.0004, ..., -0.0033, 0.0006, -0.0012], 2025-07-17T09:02:18.6373170Z [-0.0015, -0.0039, -0.0004, ..., -0.0034, 0.0007, -0.0012], 2025-07-17T09:02:18.6373426Z [-0.0015, -0.0039, -0.0004, ..., -0.0033, 0.0006, -0.0012], 2025-07-17T09:02:18.6373645Z ..., 2025-07-17T09:02:18.6373818Z [-0.0015, -0.0039, -0.0005, ..., -0.0033, 0.0006, -0.0013], 2025-07-17T09:02:18.6374060Z [-0.0015, -0.0040, -0.0004, ..., -0.0033, 0.0007, -0.0013], 2025-07-17T09:02:18.6374309Z [-0.0015, -0.0039, -0.0005, ..., -0.0033, 0.0006, -0.0013]], 2025-07-17T09:02:18.6374649Z device='cuda:0', dtype=torch.bfloat16), tensor([-0.3262, -0.3262, -0.3262, ..., -0.3262, -0.3262, -0.3262], 2025-07-17T09:02:18.6374977Z device='cuda:0', dtype=torch.bfloat16)) 2025-07-17T09:02:18.6375115Z 2025-07-17T09:02:18.6375247Z To execute this test, run the following from the base repo dir: 2025-07-17T09:02:18.6375644Z PYTORCH_TEST_WITH_ROCM=1 python test/inductor/test_max_autotune.py TestMaxAutotune.test_linear_and_cel 2025-07-17T09:02:18.6375923Z 2025-07-17T09:02:18.6376068Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2025-07-17T09:02:18.6376406Z ----------------------------- Captured stdout call ----------------------------- 2025-07-17T09:02:18.6376666Z frames [('total', 5), ('ok', 5)] 2025-07-17T09:02:18.6376857Z inline_call [] 2025-07-17T09:02:18.6377011Z unimplemented [] 2025-07-17T09:02:18.6377986Z graph_break [('Unsupported Tensor.backward() call\n Explanation: Dynamo currently does not support tracing `Tensor.backward()`.\n Hint: This graph break is fundamental - it is unlikely that Dynamo will ever be able to trace through your code. Consider finding a workaround.\n\n Developer debug context: call_method TensorVariable() backward () {}\n', 1)] 2025-07-17T09:02:18.6378848Z stats [('calls_captured', 2), ('unique_graphs', 1)] 2025-07-17T09:02:18.6379929Z inductor [('triton_bundler_save_kernel', 480), ('benchmarking.InductorBenchmarker.benchmark_gpu', 61), ('async_compile_cache_miss', 30), ('pattern_matcher_nodes', 12), ('async_compile_cache_hit', 10), ('pattern_matcher_count', 7), ('extern_calls', 3), ('fxgraph_cache_miss', 2), ('benchmarking.InductorBenchmarker.benchmark', 2), ('select_algorithm_precompile', 1), ('select_algorithm_autotune', 1), ('inplace_padding', 1)] 2025-07-17T09:02:18.6380961Z aot_autograd [('total', 1), ('autograd_cache_miss', 1), ('ok', 1), ('autograd_cache_saved', 1)] 2025-07-17T09:02:18.6381367Z aten_mm_info [('aten.addmm_32768_50264_768', 1), ('aten.mm_32768_768_50264', 1), ('aten.mm_50264_768_32768', 1)] 2025-07-17T09:02:18.6381737Z ----------------------------- Captured stderr call ----------------------------- 2025-07-17T09:02:18.6382017Z AUTOTUNE addmm(32768x50264, 32768x768, 768x50264) 2025-07-17T09:02:18.6382241Z strides: [0, 1], [768, 1], [1, 768] 2025-07-17T09:02:18.6382482Z dtypes: torch.bfloat16, torch.bfloat16, torch.bfloat16 2025-07-17T09:02:18.6382731Z bias_addmm 4.9707 ms 100.0% 2025-07-17T09:02:18.6382926Z addmm 8.3943 ms 59.2% 2025-07-17T09:02:18.6383249Z SingleProcess AUTOTUNE benchmarking takes 0.1456 seconds and 0.0002 seconds precompiling for 2 choices 2025-07-17T09:02:18.6383648Z ----------------------------- Captured stdout call ----------------------------- 2025-07-17T09:02:18.6383908Z frames [('total', 5), ('ok', 5)] 2025-07-17T09:02:18.6384087Z inline_call [] 2025-07-17T09:02:18.6384248Z unimplemented [] 2025-07-17T09:02:18.6385067Z graph_break [('Unsupported Tensor.backward() call\n Explanation: Dynamo currently does not support tracing `Tensor.backward()`.\n Hint: This graph break is fundamental - it is unlikely that Dynamo will ever be able to trace through your code. Consider finding a workaround.\n\n Developer debug context: call_method TensorVariable() backward () {}\n', 1)] 2025-07-17T09:02:18.6385995Z stats [('calls_captured', 2), ('unique_graphs', 1)] 2025-07-17T09:02:18.6386321Z aot_autograd [('total', 1), ('autograd_cache_miss', 1), ('ok', 1), ('autograd_cache_saved', 1)] 2025-07-17T09:02:18.6387355Z inductor [('triton_bundler_save_kernel', 480), ('benchmarking.InductorBenchmarker.benchmark_gpu', 61), ('async_compile_cache_miss', 40), ('async_compile_cache_hit', 20), ('pattern_matcher_nodes', 12), ('pattern_matcher_count', 7), ('extern_calls', 3), ('fxgraph_cache_miss', 2), ('benchmarking.InductorBenchmarker.benchmark', 2), ('select_algorithm_precompile', 1), ('select_algorithm_autotune', 1), ('inplace_padding', 1)] 2025-07-17T09:02:18.6388386Z aten_mm_info [('aten.addmm_32768_50264_768', 1), ('aten.mm_32768_768_50264', 1), ('aten.mm_50264_768_32768', 1)] 2025-07-17T09:02:18.6388753Z ----------------------------- Captured stderr call ----------------------------- 2025-07-17T09:02:18.6389045Z AUTOTUNE addmm(32768x50264, 32768x768, 768x50264) 2025-07-17T09:02:18.6389268Z strides: [0, 1], [768, 1], [1, 768] 2025-07-17T09:02:18.6389504Z dtypes: torch.bfloat16, torch.bfloat16, torch.bfloat16 2025-07-17T09:02:18.6389748Z bias_addmm 5.0420 ms 100.0% 2025-07-17T09:02:18.6389938Z addmm 8.1349 ms 62.0% 2025-07-17T09:02:18.6390258Z SingleProcess AUTOTUNE benchmarking takes 0.1303 seconds and 0.0002 seconds precompiling for 2 choices 2025-07-17T09:02:18.6390616Z =================================== FAILURES =================================== 2025-07-17T09:02:18.6390901Z _____________________ TestMaxAutotune.test_linear_and_cel ______________________ 2025-07-17T09:02:18.6391174Z Traceback (most recent call last): 2025-07-17T09:02:18.6391687Z File "/var/lib/jenkins/pytorch/test/inductor/test_max_autotune.py", line 954, in test_linear_and_cel 2025-07-17T09:02:18.6392101Z assert same(expect, actual, tol=1e-2), f"ref:\n{expect}\nact:\n{actual}" 2025-07-17T09:02:18.6392371Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2025-07-17T09:02:18.6392566Z AssertionError: ref: 2025-07-17T09:02:18.6393084Z (tensor(11., device='cuda:0', dtype=torch.bfloat16, grad_fn=), tensor([[-4.9546e-07, 4.1677e-08, -5.2527e-07, ..., 4.9546e-07, 2025-07-17T09:02:18.6393485Z -9.3132e-07, -3.4273e-07], 2025-07-17T09:02:18.6393708Z [ 6.4448e-07, -7.0035e-07, -4.6939e-07, ..., 4.3772e-08, 2025-07-17T09:02:18.6393935Z 6.0722e-07, 1.0058e-06], 2025-07-17T09:02:18.6394149Z [-4.9826e-08, 1.0133e-06, 3.4086e-07, ..., 7.3016e-07, 2025-07-17T09:02:18.6394365Z -4.3027e-07, 8.6427e-07], 2025-07-17T09:02:18.6394543Z ..., 2025-07-17T09:02:18.6394728Z [-6.4448e-07, -8.2329e-07, -6.8545e-07, ..., -9.0152e-07, 2025-07-17T09:02:18.6394950Z 3.0361e-07, -8.9034e-07], 2025-07-17T09:02:18.6395152Z [-9.7603e-07, -6.3702e-07, 2.5705e-07, ..., 8.9034e-07, 2025-07-17T09:02:18.6395364Z 8.6427e-07, 1.9837e-07], 2025-07-17T09:02:18.6395561Z [ 5.0990e-08, -1.0654e-06, -2.1420e-07, ..., 8.7917e-07, 2025-07-17T09:02:18.6395934Z -1.6391e-07, -9.2015e-07]], device='cuda:0', dtype=torch.bfloat16), tensor([[ 8.1956e-08, 2.2054e-05, -2.2769e-05, ..., -1.5125e-06, 2025-07-17T09:02:18.6396279Z 7.4387e-05, -7.9632e-05], 2025-07-17T09:02:18.6396489Z [-4.8399e-05, -9.5367e-05, -4.7207e-05, ..., 5.1975e-05, 2025-07-17T09:02:18.6396704Z -1.0300e-04, -5.3406e-05], 2025-07-17T09:02:18.6396916Z [-1.0431e-05, -2.4438e-05, -1.1623e-05, ..., -1.9968e-06, 2025-07-17T09:02:18.6397130Z -2.7299e-05, -2.1458e-05], 2025-07-17T09:02:18.6397311Z ..., 2025-07-17T09:02:18.6397481Z [ 8.1658e-06, 4.2200e-05, 2.0862e-05, ..., -3.1471e-05, 2025-07-17T09:02:18.6397701Z -3.0547e-06, -8.4043e-06], 2025-07-17T09:02:18.6397903Z [ 3.3140e-05, 2.1577e-05, 8.1062e-06, ..., 1.8358e-05, 2025-07-17T09:02:18.6398117Z -5.0783e-05, -2.2888e-05], 2025-07-17T09:02:18.6398322Z [ 9.0338e-08, 3.3714e-07, 1.3132e-07, ..., 5.7369e-07, 2025-07-17T09:02:18.6398680Z -5.5507e-07, 9.2667e-08]], device='cuda:0', dtype=torch.bfloat16), tensor([-4.1485e-05, -4.0770e-05, -1.0431e-05, ..., -1.0550e-05, 2025-07-17T09:02:18.6399078Z -1.0848e-05, 1.9670e-05], device='cuda:0', dtype=torch.bfloat16)) 2025-07-17T09:02:18.6399317Z act: 2025-07-17T09:02:18.6399504Z (tensor(6.3750, device='cuda:0', dtype=torch.bfloat16, 2025-07-17T09:02:18.6399860Z grad_fn=), tensor([[ 2.6673e-06, -1.6975e-04, -2.5940e-04, ..., 1.6332e-05, 2025-07-17T09:02:18.6400179Z 1.3292e-05, 5.2214e-05], 2025-07-17T09:02:18.6400380Z [ 9.2387e-07, -1.6880e-04, -2.5749e-04, ..., 1.6928e-05, 2025-07-17T09:02:18.6400600Z 1.0967e-05, 5.1498e-05], 2025-07-17T09:02:18.6400803Z [ 1.6317e-06, -1.6975e-04, -2.5940e-04, ..., 1.7047e-05, 2025-07-17T09:02:18.6401018Z 1.2994e-05, 5.1260e-05], 2025-07-17T09:02:18.6401197Z ..., 2025-07-17T09:02:18.6401372Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6401585Z 0.0000e+00, 0.0000e+00], 2025-07-17T09:02:18.6401779Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6401998Z 0.0000e+00, 0.0000e+00], 2025-07-17T09:02:18.6402197Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6402572Z 0.0000e+00, 0.0000e+00]], device='cuda:0', dtype=torch.bfloat16), tensor([[-0.0015, -0.0039, -0.0004, ..., -0.0033, 0.0006, -0.0012], 2025-07-17T09:02:18.6402954Z [-0.0015, -0.0039, -0.0004, ..., -0.0034, 0.0007, -0.0012], 2025-07-17T09:02:18.6403209Z [-0.0015, -0.0039, -0.0004, ..., -0.0033, 0.0006, -0.0012], 2025-07-17T09:02:18.6403574Z ..., 2025-07-17T09:02:18.6403748Z [-0.0015, -0.0039, -0.0005, ..., -0.0033, 0.0006, -0.0013], 2025-07-17T09:02:18.6403991Z [-0.0015, -0.0040, -0.0004, ..., -0.0033, 0.0007, -0.0013], 2025-07-17T09:02:18.6404230Z [-0.0015, -0.0039, -0.0005, ..., -0.0033, 0.0006, -0.0013]], 2025-07-17T09:02:18.6404669Z device='cuda:0', dtype=torch.bfloat16), tensor([-0.3262, -0.3262, -0.3262, ..., -0.3262, -0.3262, -0.3262], 2025-07-17T09:02:18.6405002Z device='cuda:0', dtype=torch.bfloat16)) 2025-07-17T09:02:18.6405140Z 2025-07-17T09:02:18.6405269Z To execute this test, run the following from the base repo dir: 2025-07-17T09:02:18.6405660Z PYTORCH_TEST_WITH_ROCM=1 python test/inductor/test_max_autotune.py TestMaxAutotune.test_linear_and_cel 2025-07-17T09:02:18.6405927Z 2025-07-17T09:02:18.6406077Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2025-07-17T09:02:18.6406404Z ----------------------------- Captured stdout call ----------------------------- 2025-07-17T09:02:18.6406656Z frames [('total', 5), ('ok', 5)] 2025-07-17T09:02:18.6406828Z inline_call [] 2025-07-17T09:02:18.6406975Z unimplemented [] 2025-07-17T09:02:18.6407793Z graph_break [('Unsupported Tensor.backward() call\n Explanation: Dynamo currently does not support tracing `Tensor.backward()`.\n Hint: This graph break is fundamental - it is unlikely that Dynamo will ever be able to trace through your code. Consider finding a workaround.\n\n Developer debug context: call_method TensorVariable() backward () {}\n', 1)] 2025-07-17T09:02:18.6408647Z stats [('calls_captured', 2), ('unique_graphs', 1)] 2025-07-17T09:02:18.6409610Z inductor [('triton_bundler_save_kernel', 480), ('benchmarking.InductorBenchmarker.benchmark_gpu', 61), ('async_compile_cache_miss', 30), ('pattern_matcher_nodes', 12), ('async_compile_cache_hit', 10), ('pattern_matcher_count', 7), ('extern_calls', 3), ('fxgraph_cache_miss', 2), ('benchmarking.InductorBenchmarker.benchmark', 2), ('select_algorithm_precompile', 1), ('select_algorithm_autotune', 1), ('inplace_padding', 1)] 2025-07-17T09:02:18.6410628Z aot_autograd [('total', 1), ('autograd_cache_miss', 1), ('ok', 1), ('autograd_cache_saved', 1)] 2025-07-17T09:02:18.6411023Z aten_mm_info [('aten.addmm_32768_50264_768', 1), ('aten.mm_32768_768_50264', 1), ('aten.mm_50264_768_32768', 1)] 2025-07-17T09:02:18.6411383Z ----------------------------- Captured stderr call ----------------------------- 2025-07-17T09:02:18.6411647Z AUTOTUNE addmm(32768x50264, 32768x768, 768x50264) 2025-07-17T09:02:18.6411858Z strides: [0, 1], [768, 1], [1, 768] 2025-07-17T09:02:18.6412081Z dtypes: torch.bfloat16, torch.bfloat16, torch.bfloat16 2025-07-17T09:02:18.6412310Z bias_addmm 4.9707 ms 100.0% 2025-07-17T09:02:18.6412493Z addmm 8.3943 ms 59.2% 2025-07-17T09:02:18.6412806Z SingleProcess AUTOTUNE benchmarking takes 0.1456 seconds and 0.0002 seconds precompiling for 2 choices 2025-07-17T09:02:18.6413195Z ----------------------------- Captured stdout call ----------------------------- 2025-07-17T09:02:18.6413443Z frames [('total', 5), ('ok', 5)] 2025-07-17T09:02:18.6413623Z inline_call [] 2025-07-17T09:02:18.6413777Z unimplemented [] 2025-07-17T09:02:18.6414574Z graph_break [('Unsupported Tensor.backward() call\n Explanation: Dynamo currently does not support tracing `Tensor.backward()`.\n Hint: This graph break is fundamental - it is unlikely that Dynamo will ever be able to trace through your code. Consider finding a workaround.\n\n Developer debug context: call_method TensorVariable() backward () {}\n', 1)] 2025-07-17T09:02:18.6415427Z stats [('calls_captured', 2), ('unique_graphs', 1)] 2025-07-17T09:02:18.6415746Z aot_autograd [('total', 1), ('autograd_cache_miss', 1), ('ok', 1), ('autograd_cache_saved', 1)] 2025-07-17T09:02:18.6416773Z inductor [('triton_bundler_save_kernel', 480), ('benchmarking.InductorBenchmarker.benchmark_gpu', 61), ('async_compile_cache_miss', 40), ('async_compile_cache_hit', 20), ('pattern_matcher_nodes', 12), ('pattern_matcher_count', 7), ('extern_calls', 3), ('fxgraph_cache_miss', 2), ('benchmarking.InductorBenchmarker.benchmark', 2), ('select_algorithm_precompile', 1), ('select_algorithm_autotune', 1), ('inplace_padding', 1)] 2025-07-17T09:02:18.6417950Z aten_mm_info [('aten.addmm_32768_50264_768', 1), ('aten.mm_32768_768_50264', 1), ('aten.mm_50264_768_32768', 1)] 2025-07-17T09:02:18.6418425Z ----------------------------- Captured stderr call ----------------------------- 2025-07-17T09:02:18.6418701Z AUTOTUNE addmm(32768x50264, 32768x768, 768x50264) 2025-07-17T09:02:18.6418911Z strides: [0, 1], [768, 1], [1, 768] 2025-07-17T09:02:18.6419133Z dtypes: torch.bfloat16, torch.bfloat16, torch.bfloat16 2025-07-17T09:02:18.6419363Z bias_addmm 5.0420 ms 100.0% 2025-07-17T09:02:18.6419542Z addmm 8.1349 ms 62.0% 2025-07-17T09:02:18.6419853Z SingleProcess AUTOTUNE benchmarking takes 0.1303 seconds and 0.0002 seconds precompiling for 2 choices 2025-07-17T09:02:18.6420239Z ----------------------------- Captured stdout call ----------------------------- 2025-07-17T09:02:18.6420496Z frames [('total', 5), ('ok', 5)] 2025-07-17T09:02:18.6420672Z inline_call [] 2025-07-17T09:02:18.6420823Z unimplemented [] 2025-07-17T09:02:18.6421627Z graph_break [('Unsupported Tensor.backward() call\n Explanation: Dynamo currently does not support tracing `Tensor.backward()`.\n Hint: This graph break is fundamental - it is unlikely that Dynamo will ever be able to trace through your code. Consider finding a workaround.\n\n Developer debug context: call_method TensorVariable() backward () {}\n', 1)] 2025-07-17T09:02:18.6422476Z stats [('calls_captured', 2), ('unique_graphs', 1)] 2025-07-17T09:02:18.6422788Z aot_autograd [('total', 1), ('autograd_cache_miss', 1), ('ok', 1), ('autograd_cache_saved', 1)] 2025-07-17T09:02:18.6423811Z inductor [('triton_bundler_save_kernel', 480), ('benchmarking.InductorBenchmarker.benchmark_gpu', 61), ('async_compile_cache_miss', 40), ('async_compile_cache_hit', 20), ('pattern_matcher_nodes', 12), ('pattern_matcher_count', 7), ('extern_calls', 3), ('fxgraph_cache_miss', 2), ('benchmarking.InductorBenchmarker.benchmark', 2), ('select_algorithm_precompile', 1), ('select_algorithm_autotune', 1), ('inplace_padding', 1)] 2025-07-17T09:02:18.6424840Z aten_mm_info [('aten.addmm_32768_50264_768', 1), ('aten.mm_32768_768_50264', 1), ('aten.mm_50264_768_32768', 1)] 2025-07-17T09:02:18.6425195Z ----------------------------- Captured stderr call ----------------------------- 2025-07-17T09:02:18.6425555Z AUTOTUNE addmm(32768x50264, 32768x768, 768x50264) 2025-07-17T09:02:18.6425776Z strides: [0, 1], [768, 1], [1, 768] 2025-07-17T09:02:18.6426001Z dtypes: torch.bfloat16, torch.bfloat16, torch.bfloat16 2025-07-17T09:02:18.6426231Z bias_addmm 5.0194 ms 100.0% 2025-07-17T09:02:18.6426401Z addmm 8.1180 ms 61.8% 2025-07-17T09:02:18.6426718Z SingleProcess AUTOTUNE benchmarking takes 0.1298 seconds and 0.0002 seconds precompiling for 2 choices 2025-07-17T09:02:18.6427356Z - generated xml file: /var/lib/jenkins/pytorch/test/test-reports/python-pytest/inductor.test_max_autotune/inductor.test_max_autotune-54e0e28eb9bfb2ae.xml - 2025-07-17T09:02:18.6427872Z =========================== short test summary info ============================ 2025-07-17T09:02:18.6428262Z FAILED [6.1974s] inductor/test_max_autotune.py::TestMaxAutotune::test_linear_and_cel - AssertionError: ref: 2025-07-17T09:02:18.6428802Z (tensor(11., device='cuda:0', dtype=torch.bfloat16, grad_fn=), tensor([[-4.9546e-07, 4.1677e-08, -5.2527e-07, ..., 4.9546e-07, 2025-07-17T09:02:18.6429181Z -9.3132e-07, -3.4273e-07], 2025-07-17T09:02:18.6429398Z [ 6.4448e-07, -7.0035e-07, -4.6939e-07, ..., 4.3772e-08, 2025-07-17T09:02:18.6429619Z 6.0722e-07, 1.0058e-06], 2025-07-17T09:02:18.6429824Z [-4.9826e-08, 1.0133e-06, 3.4086e-07, ..., 7.3016e-07, 2025-07-17T09:02:18.6430034Z -4.3027e-07, 8.6427e-07], 2025-07-17T09:02:18.6430214Z ..., 2025-07-17T09:02:18.6430386Z [-6.4448e-07, -8.2329e-07, -6.8545e-07, ..., -9.0152e-07, 2025-07-17T09:02:18.6430821Z 3.0361e-07, -8.9034e-07], 2025-07-17T09:02:18.6431034Z [-9.7603e-07, -6.3702e-07, 2.5705e-07, ..., 8.9034e-07, 2025-07-17T09:02:18.6431237Z 8.6427e-07, 1.9837e-07], 2025-07-17T09:02:18.6431439Z [ 5.0990e-08, -1.0654e-06, -2.1420e-07, ..., 8.7917e-07, 2025-07-17T09:02:18.6431926Z -1.6391e-07, -9.2015e-07]], device='cuda:0', dtype=torch.bfloat16), tensor([[ 8.1956e-08, 2.2054e-05, -2.2769e-05, ..., -1.5125e-06, 2025-07-17T09:02:18.6432271Z 7.4387e-05, -7.9632e-05], 2025-07-17T09:02:18.6432477Z [-4.8399e-05, -9.5367e-05, -4.7207e-05, ..., 5.1975e-05, 2025-07-17T09:02:18.6432696Z -1.0300e-04, -5.3406e-05], 2025-07-17T09:02:18.6432893Z [-1.0431e-05, -2.4438e-05, -1.1623e-05, ..., -1.9968e-06, 2025-07-17T09:02:18.6433101Z -2.7299e-05, -2.1458e-05], 2025-07-17T09:02:18.6433273Z ..., 2025-07-17T09:02:18.6433446Z [ 8.1658e-06, 4.2200e-05, 2.0862e-05, ..., -3.1471e-05, 2025-07-17T09:02:18.6433662Z -3.0547e-06, -8.4043e-06], 2025-07-17T09:02:18.6433864Z [ 3.3140e-05, 2.1577e-05, 8.1062e-06, ..., 1.8358e-05, 2025-07-17T09:02:18.6434074Z -5.0783e-05, -2.2888e-05], 2025-07-17T09:02:18.6434262Z [ 9.0338e-08, 3.3714e-07, 1.3132e-07, ..., 5.7369e-07, 2025-07-17T09:02:18.6434620Z -5.5507e-07, 9.2667e-08]], device='cuda:0', dtype=torch.bfloat16), tensor([-4.1485e-05, -4.0770e-05, -1.0431e-05, ..., -1.0550e-05, 2025-07-17T09:02:18.6435004Z -1.0848e-05, 1.9670e-05], device='cuda:0', dtype=torch.bfloat16)) 2025-07-17T09:02:18.6435244Z act: 2025-07-17T09:02:18.6435429Z (tensor(6.3750, device='cuda:0', dtype=torch.bfloat16, 2025-07-17T09:02:18.6435775Z grad_fn=), tensor([[ 2.6673e-06, -1.6975e-04, -2.5940e-04, ..., 1.6332e-05, 2025-07-17T09:02:18.6436091Z 1.3292e-05, 5.2214e-05], 2025-07-17T09:02:18.6436296Z [ 9.2387e-07, -1.6880e-04, -2.5749e-04, ..., 1.6928e-05, 2025-07-17T09:02:18.6436514Z 1.0967e-05, 5.1498e-05], 2025-07-17T09:02:18.6436716Z [ 1.6317e-06, -1.6975e-04, -2.5940e-04, ..., 1.7047e-05, 2025-07-17T09:02:18.6436921Z 1.2994e-05, 5.1260e-05], 2025-07-17T09:02:18.6437088Z ..., 2025-07-17T09:02:18.6437261Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6437461Z 0.0000e+00, 0.0000e+00], 2025-07-17T09:02:18.6437653Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6437859Z 0.0000e+00, 0.0000e+00], 2025-07-17T09:02:18.6438055Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6438415Z 0.0000e+00, 0.0000e+00]], device='cuda:0', dtype=torch.bfloat16), tensor([[-0.0015, -0.0039, -0.0004, ..., -0.0033, 0.0006, -0.0012], 2025-07-17T09:02:18.6438787Z [-0.0015, -0.0039, -0.0004, ..., -0.0034, 0.0007, -0.0012], 2025-07-17T09:02:18.6439033Z [-0.0015, -0.0039, -0.0004, ..., -0.0033, 0.0006, -0.0012], 2025-07-17T09:02:18.6439238Z ..., 2025-07-17T09:02:18.6439407Z [-0.0015, -0.0039, -0.0005, ..., -0.0033, 0.0006, -0.0013], 2025-07-17T09:02:18.6439641Z [-0.0015, -0.0040, -0.0004, ..., -0.0033, 0.0007, -0.0013], 2025-07-17T09:02:18.6439884Z [-0.0015, -0.0039, -0.0005, ..., -0.0033, 0.0006, -0.0013]], 2025-07-17T09:02:18.6440215Z device='cuda:0', dtype=torch.bfloat16), tensor([-0.3262, -0.3262, -0.3262, ..., -0.3262, -0.3262, -0.3262], 2025-07-17T09:02:18.6440536Z device='cuda:0', dtype=torch.bfloat16)) 2025-07-17T09:02:18.6440673Z 2025-07-17T09:02:18.6440801Z To execute this test, run the following from the base repo dir: 2025-07-17T09:02:18.6441190Z PYTORCH_TEST_WITH_ROCM=1 python test/inductor/test_max_autotune.py TestMaxAutotune.test_linear_and_cel 2025-07-17T09:02:18.6441460Z 2025-07-17T09:02:18.6441613Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2025-07-17T09:02:18.6442041Z !!!!!!!!!!!!!!!!!!!!!!!!!! stopping after 1 failures !!!!!!!!!!!!!!!!!!!!!!!!!!! 2025-07-17T09:02:18.6442316Z ================== 1 failed, 71 deselected, 2 rerun in 32.62s ================== 2025-07-17T09:02:18.6442550Z Got exit code 1 2025-07-17T09:02:18.6442714Z Retrying single test... 2025-07-17T09:02:18.6443631Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T09:02:18.6444430Z import pkg_resources 2025-07-17T09:02:18.6445918Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py:1597: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/Context.cpp:78.) 2025-07-17T09:02:18.6447400Z _C._set_float32_matmul_precision(precision) 2025-07-17T09:02:18.6447839Z Test results will be stored in test-reports/python-pytest/inductor.test_max_autotune/inductor.test_max_autotune-aef4a534f50e5f1e.xml 2025-07-17T09:02:18.6448270Z ============================= test session starts ============================== 2025-07-17T09:02:18.6448620Z platform linux -- Python 3.12.11, pytest-7.3.2, pluggy-1.6.0 -- /opt/conda/envs/py_3.12/bin/python 2025-07-17T09:02:18.6448920Z cachedir: .pytest_cache 2025-07-17T09:02:18.6449290Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2025-07-17T09:02:18.6449685Z rootdir: /var/lib/jenkins/pytorch 2025-07-17T09:02:18.6449879Z configfile: pytest.ini 2025-07-17T09:02:18.6450257Z plugins: rerunfailures-14.0, subtests-0.13.1, flakefinder-1.1.0, xdist-3.3.1, xdoctest-1.1.0, hypothesis-5.35.1, cpp-2.3.0, typeguard-4.3.0 2025-07-17T09:02:18.6450710Z collecting ... collected 132 items / 71 deselected / 61 selected 2025-07-17T09:02:18.6451161Z stepcurrent: skipping 9 already run items. Running only test/inductor/test_max_autotune.py::TestMaxAutotune::test_linear_and_cel 2025-07-17T09:02:18.6451554Z Running 1 items in this shard 2025-07-17T09:02:18.6451669Z 2025-07-17T09:02:18.6452088Z inductor/test_max_autotune.py::TestMaxAutotune::test_linear_and_cel [W717 08:56:47.940645142 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6452540Z 2025-07-17T09:02:18.6452795Z [W717 08:56:48.544828452 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6453100Z 2025-07-17T09:02:18.6453341Z [W717 08:56:48.547292948 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6453637Z 2025-07-17T09:02:18.6453877Z [W717 08:56:48.547401970 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6454172Z 2025-07-17T09:02:18.6454412Z [W717 08:56:48.547815724 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6454708Z 2025-07-17T09:02:18.6454947Z [W717 08:56:48.547912387 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6455246Z 2025-07-17T09:02:18.6455480Z [W717 08:56:48.548215467 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6455904Z 2025-07-17T09:02:18.6456142Z [W717 08:56:48.548309366 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6456442Z 2025-07-17T09:02:18.6456678Z [W717 08:56:48.548590884 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6456972Z 2025-07-17T09:02:18.6457308Z [W717 08:56:48.548684282 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6457614Z 2025-07-17T09:02:18.6457839Z E0717 08:56:54.913000 85202 site-packages/torch/_dynamo/utils.py:3063] Accuracy failed: allclose not within tol=0.01 2025-07-17T09:02:18.6458197Z ('RERUN', {'yellow': True}) [20.4982s] [100%] 2025-07-17T09:02:18.6458729Z inductor/test_max_autotune.py::TestMaxAutotune::test_linear_and_cel [W717 08:56:55.892927339 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6459180Z 2025-07-17T09:02:18.6459418Z [W717 08:56:55.893115078 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6459721Z 2025-07-17T09:02:18.6459960Z [W717 08:56:55.893475503 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6460257Z 2025-07-17T09:02:18.6460490Z [W717 08:56:55.893572977 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6460788Z 2025-07-17T09:02:18.6461022Z [W717 08:56:55.893865101 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6461323Z 2025-07-17T09:02:18.6461556Z [W717 08:56:55.893958609 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6461857Z 2025-07-17T09:02:18.6462092Z [W717 08:56:55.894196082 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6462393Z 2025-07-17T09:02:18.6462629Z [W717 08:56:55.894302539 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6462940Z 2025-07-17T09:02:18.6463175Z [W717 08:56:55.894534884 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6463473Z 2025-07-17T09:02:18.6463706Z [W717 08:56:55.894624377 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6464008Z 2025-07-17T09:02:18.6464215Z E0717 08:57:01.164000 85202 site-packages/torch/_dynamo/utils.py:3063] Accuracy failed: allclose not within tol=0.01 2025-07-17T09:02:18.6464566Z ('RERUN', {'yellow': True}) [6.1149s] [100%] 2025-07-17T09:02:18.6465091Z inductor/test_max_autotune.py::TestMaxAutotune::test_linear_and_cel [W717 08:57:01.016248962 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6465604Z 2025-07-17T09:02:18.6465844Z [W717 08:57:01.016442068 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6466145Z 2025-07-17T09:02:18.6466381Z [W717 08:57:01.016799468 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6466676Z 2025-07-17T09:02:18.6466912Z [W717 08:57:01.016925816 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6467212Z 2025-07-17T09:02:18.6467447Z [W717 08:57:01.017198941 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6467903Z 2025-07-17T09:02:18.6468140Z [W717 08:57:01.017294442 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6468439Z 2025-07-17T09:02:18.6468813Z [W717 08:57:01.017540998 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6469128Z 2025-07-17T09:02:18.6469371Z [W717 08:57:01.017649749 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6469671Z 2025-07-17T09:02:18.6469906Z [W717 08:57:01.017884768 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6470207Z 2025-07-17T09:02:18.6470442Z [W717 08:57:01.017977626 Module.cpp:191] symbolizing C++ stack trace for exception; if this hangs, rerun with TORCH_DISABLE_ADDR2LINE=1... 2025-07-17T09:02:18.6470754Z 2025-07-17T09:02:18.6470962Z E0717 08:57:07.131000 85202 site-packages/torch/_dynamo/utils.py:3063] Accuracy failed: allclose not within tol=0.01 2025-07-17T09:02:18.6471301Z FAILED [5.9643s] [100%] 2025-07-17T09:02:18.6471409Z 2025-07-17T09:02:18.6471496Z ==================================== RERUNS ==================================== 2025-07-17T09:02:18.6471788Z _____________________ TestMaxAutotune.test_linear_and_cel ______________________ 2025-07-17T09:02:18.6472055Z Traceback (most recent call last): 2025-07-17T09:02:18.6472405Z File "/var/lib/jenkins/pytorch/test/inductor/test_max_autotune.py", line 954, in test_linear_and_cel 2025-07-17T09:02:18.6472821Z assert same(expect, actual, tol=1e-2), f"ref:\n{expect}\nact:\n{actual}" 2025-07-17T09:02:18.6473083Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2025-07-17T09:02:18.6473283Z AssertionError: ref: 2025-07-17T09:02:18.6473666Z (tensor(11., device='cuda:0', dtype=torch.bfloat16, grad_fn=), tensor([[-4.9546e-07, 4.1677e-08, -5.2527e-07, ..., 4.9546e-07, 2025-07-17T09:02:18.6474066Z -9.3132e-07, -3.4273e-07], 2025-07-17T09:02:18.6474292Z [ 6.4448e-07, -7.0035e-07, -4.6939e-07, ..., 4.3772e-08, 2025-07-17T09:02:18.6474523Z 6.0722e-07, 1.0058e-06], 2025-07-17T09:02:18.6474739Z [-4.9826e-08, 1.0133e-06, 3.4086e-07, ..., 7.3016e-07, 2025-07-17T09:02:18.6474960Z -4.3027e-07, 8.6427e-07], 2025-07-17T09:02:18.6475135Z ..., 2025-07-17T09:02:18.6475317Z [-6.4448e-07, -8.2329e-07, -6.8545e-07, ..., -9.0152e-07, 2025-07-17T09:02:18.6475537Z 3.0361e-07, -8.9034e-07], 2025-07-17T09:02:18.6475745Z [-9.7603e-07, -6.3702e-07, 2.5705e-07, ..., 8.9034e-07, 2025-07-17T09:02:18.6475966Z 8.6427e-07, 1.9837e-07], 2025-07-17T09:02:18.6476176Z [ 5.0990e-08, -1.0654e-06, -2.1420e-07, ..., 8.7917e-07, 2025-07-17T09:02:18.6476551Z -1.6391e-07, -9.2015e-07]], device='cuda:0', dtype=torch.bfloat16), tensor([[ 8.1956e-08, 2.2054e-05, -2.2769e-05, ..., -1.5125e-06, 2025-07-17T09:02:18.6476896Z 7.4387e-05, -7.9632e-05], 2025-07-17T09:02:18.6477105Z [-4.8399e-05, -9.5367e-05, -4.7207e-05, ..., 5.1975e-05, 2025-07-17T09:02:18.6477317Z -1.0300e-04, -5.3406e-05], 2025-07-17T09:02:18.6477524Z [-1.0431e-05, -2.4438e-05, -1.1623e-05, ..., -1.9968e-06, 2025-07-17T09:02:18.6477736Z -2.7299e-05, -2.1458e-05], 2025-07-17T09:02:18.6477904Z ..., 2025-07-17T09:02:18.6478076Z [ 8.1658e-06, 4.2200e-05, 2.0862e-05, ..., -3.1471e-05, 2025-07-17T09:02:18.6478286Z -3.0547e-06, -8.4043e-06], 2025-07-17T09:02:18.6478489Z [ 3.3140e-05, 2.1577e-05, 8.1062e-06, ..., 1.8358e-05, 2025-07-17T09:02:18.6478700Z -5.0783e-05, -2.2888e-05], 2025-07-17T09:02:18.6478901Z [ 9.0338e-08, 3.3714e-07, 1.3132e-07, ..., 5.7369e-07, 2025-07-17T09:02:18.6479264Z -5.5507e-07, 9.2667e-08]], device='cuda:0', dtype=torch.bfloat16), tensor([-4.1485e-05, -4.0770e-05, -1.0431e-05, ..., -1.0550e-05, 2025-07-17T09:02:18.6479812Z -1.0848e-05, 1.9670e-05], device='cuda:0', dtype=torch.bfloat16)) 2025-07-17T09:02:18.6480055Z act: 2025-07-17T09:02:18.6480247Z (tensor(6.3750, device='cuda:0', dtype=torch.bfloat16, 2025-07-17T09:02:18.6480608Z grad_fn=), tensor([[ 2.6673e-06, -1.6975e-04, -2.5940e-04, ..., 1.6332e-05, 2025-07-17T09:02:18.6481063Z 1.3292e-05, 5.2214e-05], 2025-07-17T09:02:18.6481277Z [ 9.2387e-07, -1.6880e-04, -2.5749e-04, ..., 1.6928e-05, 2025-07-17T09:02:18.6481493Z 1.0967e-05, 5.1498e-05], 2025-07-17T09:02:18.6481694Z [ 1.6317e-06, -1.6975e-04, -2.5940e-04, ..., 1.7047e-05, 2025-07-17T09:02:18.6481913Z 1.2994e-05, 5.1260e-05], 2025-07-17T09:02:18.6482087Z ..., 2025-07-17T09:02:18.6482262Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6482477Z 0.0000e+00, 0.0000e+00], 2025-07-17T09:02:18.6482697Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6482916Z 0.0000e+00, 0.0000e+00], 2025-07-17T09:02:18.6483120Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6483487Z 0.0000e+00, 0.0000e+00]], device='cuda:0', dtype=torch.bfloat16), tensor([[-0.0015, -0.0039, -0.0004, ..., -0.0033, 0.0006, -0.0012], 2025-07-17T09:02:18.6483871Z [-0.0015, -0.0039, -0.0004, ..., -0.0034, 0.0007, -0.0012], 2025-07-17T09:02:18.6484121Z [-0.0015, -0.0039, -0.0004, ..., -0.0033, 0.0006, -0.0012], 2025-07-17T09:02:18.6484340Z ..., 2025-07-17T09:02:18.6484518Z [-0.0015, -0.0039, -0.0005, ..., -0.0033, 0.0006, -0.0013], 2025-07-17T09:02:18.6484750Z [-0.0015, -0.0039, -0.0005, ..., -0.0033, 0.0006, -0.0013], 2025-07-17T09:02:18.6485021Z [-0.0015, -0.0039, -0.0005, ..., -0.0033, 0.0006, -0.0013]], 2025-07-17T09:02:18.6485378Z device='cuda:0', dtype=torch.bfloat16), tensor([-0.3262, -0.3262, -0.3262, ..., -0.3262, -0.3262, -0.3262], 2025-07-17T09:02:18.6485716Z device='cuda:0', dtype=torch.bfloat16)) 2025-07-17T09:02:18.6485867Z 2025-07-17T09:02:18.6485993Z To execute this test, run the following from the base repo dir: 2025-07-17T09:02:18.6486403Z PYTORCH_TEST_WITH_ROCM=1 python test/inductor/test_max_autotune.py TestMaxAutotune.test_linear_and_cel 2025-07-17T09:02:18.6486687Z 2025-07-17T09:02:18.6486837Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2025-07-17T09:02:18.6487177Z ----------------------------- Captured stdout call ----------------------------- 2025-07-17T09:02:18.6487458Z frames [('total', 5), ('ok', 5)] 2025-07-17T09:02:18.6487653Z inline_call [] 2025-07-17T09:02:18.6487818Z unimplemented [] 2025-07-17T09:02:18.6488642Z graph_break [('Unsupported Tensor.backward() call\n Explanation: Dynamo currently does not support tracing `Tensor.backward()`.\n Hint: This graph break is fundamental - it is unlikely that Dynamo will ever be able to trace through your code. Consider finding a workaround.\n\n Developer debug context: call_method TensorVariable() backward () {}\n', 1)] 2025-07-17T09:02:18.6489530Z stats [('calls_captured', 2), ('unique_graphs', 1)] 2025-07-17T09:02:18.6490512Z inductor [('triton_bundler_save_kernel', 480), ('benchmarking.InductorBenchmarker.benchmark_gpu', 61), ('async_compile_cache_miss', 30), ('pattern_matcher_nodes', 12), ('async_compile_cache_hit', 10), ('pattern_matcher_count', 7), ('extern_calls', 3), ('fxgraph_cache_miss', 2), ('benchmarking.InductorBenchmarker.benchmark', 2), ('select_algorithm_precompile', 1), ('select_algorithm_autotune', 1), ('inplace_padding', 1)] 2025-07-17T09:02:18.6491540Z aot_autograd [('total', 1), ('autograd_cache_miss', 1), ('ok', 1), ('autograd_cache_saved', 1)] 2025-07-17T09:02:18.6491947Z aten_mm_info [('aten.addmm_32768_50264_768', 1), ('aten.mm_32768_768_50264', 1), ('aten.mm_50264_768_32768', 1)] 2025-07-17T09:02:18.6492493Z ----------------------------- Captured stderr call ----------------------------- 2025-07-17T09:02:18.6492788Z AUTOTUNE addmm(32768x50264, 32768x768, 768x50264) 2025-07-17T09:02:18.6493017Z strides: [0, 1], [768, 1], [1, 768] 2025-07-17T09:02:18.6493255Z dtypes: torch.bfloat16, torch.bfloat16, torch.bfloat16 2025-07-17T09:02:18.6493501Z bias_addmm 5.0388 ms 100.0% 2025-07-17T09:02:18.6493683Z addmm 8.3373 ms 60.4% 2025-07-17T09:02:18.6494113Z SingleProcess AUTOTUNE benchmarking takes 0.1406 seconds and 0.0002 seconds precompiling for 2 choices 2025-07-17T09:02:18.6494533Z _____________________ TestMaxAutotune.test_linear_and_cel ______________________ 2025-07-17T09:02:18.6494806Z Traceback (most recent call last): 2025-07-17T09:02:18.6495161Z File "/var/lib/jenkins/pytorch/test/inductor/test_max_autotune.py", line 954, in test_linear_and_cel 2025-07-17T09:02:18.6495589Z assert same(expect, actual, tol=1e-2), f"ref:\n{expect}\nact:\n{actual}" 2025-07-17T09:02:18.6495866Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2025-07-17T09:02:18.6496074Z AssertionError: ref: 2025-07-17T09:02:18.6496453Z (tensor(11., device='cuda:0', dtype=torch.bfloat16, grad_fn=), tensor([[-4.9546e-07, 4.1677e-08, -5.2527e-07, ..., 4.9546e-07, 2025-07-17T09:02:18.6496847Z -9.3132e-07, -3.4273e-07], 2025-07-17T09:02:18.6497075Z [ 6.4448e-07, -7.0035e-07, -4.6939e-07, ..., 4.3772e-08, 2025-07-17T09:02:18.6497307Z 6.0722e-07, 1.0058e-06], 2025-07-17T09:02:18.6497512Z [-4.9826e-08, 1.0133e-06, 3.4086e-07, ..., 7.3016e-07, 2025-07-17T09:02:18.6497736Z -4.3027e-07, 8.6427e-07], 2025-07-17T09:02:18.6497908Z ..., 2025-07-17T09:02:18.6498097Z [-6.4448e-07, -8.2329e-07, -6.8545e-07, ..., -9.0152e-07, 2025-07-17T09:02:18.6498316Z 3.0361e-07, -8.9034e-07], 2025-07-17T09:02:18.6498526Z [-9.7603e-07, -6.3702e-07, 2.5705e-07, ..., 8.9034e-07, 2025-07-17T09:02:18.6498740Z 8.6427e-07, 1.9837e-07], 2025-07-17T09:02:18.6498950Z [ 5.0990e-08, -1.0654e-06, -2.1420e-07, ..., 8.7917e-07, 2025-07-17T09:02:18.6499317Z -1.6391e-07, -9.2015e-07]], device='cuda:0', dtype=torch.bfloat16), tensor([[ 8.1956e-08, 2.2054e-05, -2.2769e-05, ..., -1.5125e-06, 2025-07-17T09:02:18.6499666Z 7.4387e-05, -7.9632e-05], 2025-07-17T09:02:18.6499877Z [-4.8399e-05, -9.5367e-05, -4.7207e-05, ..., 5.1975e-05, 2025-07-17T09:02:18.6500097Z -1.0300e-04, -5.3406e-05], 2025-07-17T09:02:18.6500311Z [-1.0431e-05, -2.4438e-05, -1.1623e-05, ..., -1.9968e-06, 2025-07-17T09:02:18.6500527Z -2.7299e-05, -2.1458e-05], 2025-07-17T09:02:18.6500711Z ..., 2025-07-17T09:02:18.6500871Z [ 8.1658e-06, 4.2200e-05, 2.0862e-05, ..., -3.1471e-05, 2025-07-17T09:02:18.6501084Z -3.0547e-06, -8.4043e-06], 2025-07-17T09:02:18.6501301Z [ 3.3140e-05, 2.1577e-05, 8.1062e-06, ..., 1.8358e-05, 2025-07-17T09:02:18.6501516Z -5.0783e-05, -2.2888e-05], 2025-07-17T09:02:18.6501767Z [ 9.0338e-08, 3.3714e-07, 1.3132e-07, ..., 5.7369e-07, 2025-07-17T09:02:18.6502132Z -5.5507e-07, 9.2667e-08]], device='cuda:0', dtype=torch.bfloat16), tensor([-4.1485e-05, -4.0770e-05, -1.0431e-05, ..., -1.0550e-05, 2025-07-17T09:02:18.6502526Z -1.0848e-05, 1.9670e-05], device='cuda:0', dtype=torch.bfloat16)) 2025-07-17T09:02:18.6502770Z act: 2025-07-17T09:02:18.6502964Z (tensor(6.3750, device='cuda:0', dtype=torch.bfloat16, 2025-07-17T09:02:18.6503330Z grad_fn=), tensor([[ 2.6673e-06, -1.6975e-04, -2.5940e-04, ..., 1.6332e-05, 2025-07-17T09:02:18.6503649Z 1.3292e-05, 5.2214e-05], 2025-07-17T09:02:18.6503854Z [ 9.2387e-07, -1.6880e-04, -2.5749e-04, ..., 1.6928e-05, 2025-07-17T09:02:18.6504070Z 1.0967e-05, 5.1498e-05], 2025-07-17T09:02:18.6504265Z [ 1.6317e-06, -1.6975e-04, -2.5940e-04, ..., 1.7047e-05, 2025-07-17T09:02:18.6504486Z 1.2994e-05, 5.1260e-05], 2025-07-17T09:02:18.6504791Z ..., 2025-07-17T09:02:18.6504967Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6505182Z 0.0000e+00, 0.0000e+00], 2025-07-17T09:02:18.6505457Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6505666Z 0.0000e+00, 0.0000e+00], 2025-07-17T09:02:18.6505872Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6506379Z 0.0000e+00, 0.0000e+00]], device='cuda:0', dtype=torch.bfloat16), tensor([[-0.0015, -0.0039, -0.0004, ..., -0.0033, 0.0006, -0.0012], 2025-07-17T09:02:18.6506799Z [-0.0015, -0.0039, -0.0004, ..., -0.0034, 0.0007, -0.0012], 2025-07-17T09:02:18.6507051Z [-0.0015, -0.0039, -0.0004, ..., -0.0033, 0.0006, -0.0012], 2025-07-17T09:02:18.6507266Z ..., 2025-07-17T09:02:18.6507447Z [-0.0015, -0.0039, -0.0005, ..., -0.0033, 0.0006, -0.0013], 2025-07-17T09:02:18.6507688Z [-0.0015, -0.0040, -0.0004, ..., -0.0033, 0.0007, -0.0013], 2025-07-17T09:02:18.6507943Z [-0.0015, -0.0039, -0.0005, ..., -0.0033, 0.0006, -0.0013]], 2025-07-17T09:02:18.6508284Z device='cuda:0', dtype=torch.bfloat16), tensor([-0.3262, -0.3262, -0.3262, ..., -0.3262, -0.3262, -0.3262], 2025-07-17T09:02:18.6508626Z device='cuda:0', dtype=torch.bfloat16)) 2025-07-17T09:02:18.6508779Z 2025-07-17T09:02:18.6508900Z To execute this test, run the following from the base repo dir: 2025-07-17T09:02:18.6509304Z PYTORCH_TEST_WITH_ROCM=1 python test/inductor/test_max_autotune.py TestMaxAutotune.test_linear_and_cel 2025-07-17T09:02:18.6509588Z 2025-07-17T09:02:18.6509740Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2025-07-17T09:02:18.6510081Z ----------------------------- Captured stdout call ----------------------------- 2025-07-17T09:02:18.6510348Z frames [('total', 5), ('ok', 5)] 2025-07-17T09:02:18.6510532Z inline_call [] 2025-07-17T09:02:18.6510700Z unimplemented [] 2025-07-17T09:02:18.6511535Z graph_break [('Unsupported Tensor.backward() call\n Explanation: Dynamo currently does not support tracing `Tensor.backward()`.\n Hint: This graph break is fundamental - it is unlikely that Dynamo will ever be able to trace through your code. Consider finding a workaround.\n\n Developer debug context: call_method TensorVariable() backward () {}\n', 1)] 2025-07-17T09:02:18.6512403Z stats [('calls_captured', 2), ('unique_graphs', 1)] 2025-07-17T09:02:18.6513382Z inductor [('triton_bundler_save_kernel', 480), ('benchmarking.InductorBenchmarker.benchmark_gpu', 61), ('async_compile_cache_miss', 30), ('pattern_matcher_nodes', 12), ('async_compile_cache_hit', 10), ('pattern_matcher_count', 7), ('extern_calls', 3), ('fxgraph_cache_miss', 2), ('benchmarking.InductorBenchmarker.benchmark', 2), ('select_algorithm_precompile', 1), ('select_algorithm_autotune', 1), ('inplace_padding', 1)] 2025-07-17T09:02:18.6514402Z aot_autograd [('total', 1), ('autograd_cache_miss', 1), ('ok', 1), ('autograd_cache_saved', 1)] 2025-07-17T09:02:18.6514807Z aten_mm_info [('aten.addmm_32768_50264_768', 1), ('aten.mm_32768_768_50264', 1), ('aten.mm_50264_768_32768', 1)] 2025-07-17T09:02:18.6515167Z ----------------------------- Captured stderr call ----------------------------- 2025-07-17T09:02:18.6515460Z AUTOTUNE addmm(32768x50264, 32768x768, 768x50264) 2025-07-17T09:02:18.6515680Z strides: [0, 1], [768, 1], [1, 768] 2025-07-17T09:02:18.6515917Z dtypes: torch.bfloat16, torch.bfloat16, torch.bfloat16 2025-07-17T09:02:18.6516162Z bias_addmm 5.0388 ms 100.0% 2025-07-17T09:02:18.6516355Z addmm 8.3373 ms 60.4% 2025-07-17T09:02:18.6516674Z SingleProcess AUTOTUNE benchmarking takes 0.1406 seconds and 0.0002 seconds precompiling for 2 choices 2025-07-17T09:02:18.6517069Z ----------------------------- Captured stdout call ----------------------------- 2025-07-17T09:02:18.6517324Z frames [('total', 5), ('ok', 5)] 2025-07-17T09:02:18.6517515Z inline_call [] 2025-07-17T09:02:18.6517677Z unimplemented [] 2025-07-17T09:02:18.6518482Z graph_break [('Unsupported Tensor.backward() call\n Explanation: Dynamo currently does not support tracing `Tensor.backward()`.\n Hint: This graph break is fundamental - it is unlikely that Dynamo will ever be able to trace through your code. Consider finding a workaround.\n\n Developer debug context: call_method TensorVariable() backward () {}\n', 1)] 2025-07-17T09:02:18.6519525Z stats [('calls_captured', 2), ('unique_graphs', 1)] 2025-07-17T09:02:18.6519948Z aot_autograd [('total', 1), ('autograd_cache_miss', 1), ('ok', 1), ('autograd_cache_saved', 1)] 2025-07-17T09:02:18.6520985Z inductor [('triton_bundler_save_kernel', 480), ('benchmarking.InductorBenchmarker.benchmark_gpu', 61), ('async_compile_cache_miss', 40), ('async_compile_cache_hit', 20), ('pattern_matcher_nodes', 12), ('pattern_matcher_count', 7), ('extern_calls', 3), ('fxgraph_cache_miss', 2), ('benchmarking.InductorBenchmarker.benchmark', 2), ('select_algorithm_precompile', 1), ('select_algorithm_autotune', 1), ('inplace_padding', 1)] 2025-07-17T09:02:18.6522054Z aten_mm_info [('aten.addmm_32768_50264_768', 1), ('aten.mm_32768_768_50264', 1), ('aten.mm_50264_768_32768', 1)] 2025-07-17T09:02:18.6522418Z ----------------------------- Captured stderr call ----------------------------- 2025-07-17T09:02:18.6522697Z AUTOTUNE addmm(32768x50264, 32768x768, 768x50264) 2025-07-17T09:02:18.6522915Z strides: [0, 1], [768, 1], [1, 768] 2025-07-17T09:02:18.6523150Z dtypes: torch.bfloat16, torch.bfloat16, torch.bfloat16 2025-07-17T09:02:18.6523398Z bias_addmm 5.0277 ms 100.0% 2025-07-17T09:02:18.6523586Z addmm 8.1979 ms 61.3% 2025-07-17T09:02:18.6523906Z SingleProcess AUTOTUNE benchmarking takes 0.1304 seconds and 0.0002 seconds precompiling for 2 choices 2025-07-17T09:02:18.6524273Z =================================== FAILURES =================================== 2025-07-17T09:02:18.6524565Z _____________________ TestMaxAutotune.test_linear_and_cel ______________________ 2025-07-17T09:02:18.6524827Z Traceback (most recent call last): 2025-07-17T09:02:18.6525188Z File "/var/lib/jenkins/pytorch/test/inductor/test_max_autotune.py", line 954, in test_linear_and_cel 2025-07-17T09:02:18.6525607Z assert same(expect, actual, tol=1e-2), f"ref:\n{expect}\nact:\n{actual}" 2025-07-17T09:02:18.6525884Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2025-07-17T09:02:18.6526087Z AssertionError: ref: 2025-07-17T09:02:18.6526470Z (tensor(11., device='cuda:0', dtype=torch.bfloat16, grad_fn=), tensor([[-4.9546e-07, 4.1677e-08, -5.2527e-07, ..., 4.9546e-07, 2025-07-17T09:02:18.6526872Z -9.3132e-07, -3.4273e-07], 2025-07-17T09:02:18.6527095Z [ 6.4448e-07, -7.0035e-07, -4.6939e-07, ..., 4.3772e-08, 2025-07-17T09:02:18.6527330Z 6.0722e-07, 1.0058e-06], 2025-07-17T09:02:18.6527553Z [-4.9826e-08, 1.0133e-06, 3.4086e-07, ..., 7.3016e-07, 2025-07-17T09:02:18.6527770Z -4.3027e-07, 8.6427e-07], 2025-07-17T09:02:18.6527950Z ..., 2025-07-17T09:02:18.6528132Z [-6.4448e-07, -8.2329e-07, -6.8545e-07, ..., -9.0152e-07, 2025-07-17T09:02:18.6528343Z 3.0361e-07, -8.9034e-07], 2025-07-17T09:02:18.6528818Z [-9.7603e-07, -6.3702e-07, 2.5705e-07, ..., 8.9034e-07, 2025-07-17T09:02:18.6536121Z 8.6427e-07, 1.9837e-07], 2025-07-17T09:02:18.6536360Z [ 5.0990e-08, -1.0654e-06, -2.1420e-07, ..., 8.7917e-07, 2025-07-17T09:02:18.6536760Z -1.6391e-07, -9.2015e-07]], device='cuda:0', dtype=torch.bfloat16), tensor([[ 8.1956e-08, 2.2054e-05, -2.2769e-05, ..., -1.5125e-06, 2025-07-17T09:02:18.6537114Z 7.4387e-05, -7.9632e-05], 2025-07-17T09:02:18.6537328Z [-4.8399e-05, -9.5367e-05, -4.7207e-05, ..., 5.1975e-05, 2025-07-17T09:02:18.6537546Z -1.0300e-04, -5.3406e-05], 2025-07-17T09:02:18.6537754Z [-1.0431e-05, -2.4438e-05, -1.1623e-05, ..., -1.9968e-06, 2025-07-17T09:02:18.6537967Z -2.7299e-05, -2.1458e-05], 2025-07-17T09:02:18.6538141Z ..., 2025-07-17T09:02:18.6538318Z [ 8.1658e-06, 4.2200e-05, 2.0862e-05, ..., -3.1471e-05, 2025-07-17T09:02:18.6538701Z -3.0547e-06, -8.4043e-06], 2025-07-17T09:02:18.6538892Z [ 3.3140e-05, 2.1577e-05, 8.1062e-06, ..., 1.8358e-05, 2025-07-17T09:02:18.6539101Z -5.0783e-05, -2.2888e-05], 2025-07-17T09:02:18.6539299Z [ 9.0338e-08, 3.3714e-07, 1.3132e-07, ..., 5.7369e-07, 2025-07-17T09:02:18.6539769Z -5.5507e-07, 9.2667e-08]], device='cuda:0', dtype=torch.bfloat16), tensor([-4.1485e-05, -4.0770e-05, -1.0431e-05, ..., -1.0550e-05, 2025-07-17T09:02:18.6540172Z -1.0848e-05, 1.9670e-05], device='cuda:0', dtype=torch.bfloat16)) 2025-07-17T09:02:18.6540422Z act: 2025-07-17T09:02:18.6540608Z (tensor(6.3750, device='cuda:0', dtype=torch.bfloat16, 2025-07-17T09:02:18.6540976Z grad_fn=), tensor([[ 2.6673e-06, -1.6975e-04, -2.5940e-04, ..., 1.6332e-05, 2025-07-17T09:02:18.6541299Z 1.3292e-05, 5.2214e-05], 2025-07-17T09:02:18.6541517Z [ 9.2387e-07, -1.6880e-04, -2.5749e-04, ..., 1.6928e-05, 2025-07-17T09:02:18.6541737Z 1.0967e-05, 5.1498e-05], 2025-07-17T09:02:18.6541939Z [ 1.6317e-06, -1.6975e-04, -2.5940e-04, ..., 1.7047e-05, 2025-07-17T09:02:18.6542001Z 1.2994e-05, 5.1260e-05], 2025-07-17T09:02:18.6542062Z ..., 2025-07-17T09:02:18.6542156Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6542226Z 0.0000e+00, 0.0000e+00], 2025-07-17T09:02:18.6542314Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6542375Z 0.0000e+00, 0.0000e+00], 2025-07-17T09:02:18.6542464Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6542696Z 0.0000e+00, 0.0000e+00]], device='cuda:0', dtype=torch.bfloat16), tensor([[-0.0015, -0.0039, -0.0004, ..., -0.0033, 0.0006, -0.0012], 2025-07-17T09:02:18.6542805Z [-0.0015, -0.0039, -0.0004, ..., -0.0034, 0.0007, -0.0012], 2025-07-17T09:02:18.6542895Z [-0.0015, -0.0039, -0.0004, ..., -0.0033, 0.0006, -0.0012], 2025-07-17T09:02:18.6542969Z ..., 2025-07-17T09:02:18.6543056Z [-0.0015, -0.0039, -0.0005, ..., -0.0033, 0.0006, -0.0013], 2025-07-17T09:02:18.6543153Z [-0.0015, -0.0040, -0.0004, ..., -0.0033, 0.0007, -0.0013], 2025-07-17T09:02:18.6543247Z [-0.0015, -0.0039, -0.0005, ..., -0.0033, 0.0006, -0.0013]], 2025-07-17T09:02:18.6543453Z device='cuda:0', dtype=torch.bfloat16), tensor([-0.3262, -0.3262, -0.3262, ..., -0.3262, -0.3262, -0.3262], 2025-07-17T09:02:18.6543542Z device='cuda:0', dtype=torch.bfloat16)) 2025-07-17T09:02:18.6543546Z 2025-07-17T09:02:18.6543679Z To execute this test, run the following from the base repo dir: 2025-07-17T09:02:18.6543904Z PYTORCH_TEST_WITH_ROCM=1 python test/inductor/test_max_autotune.py TestMaxAutotune.test_linear_and_cel 2025-07-17T09:02:18.6543907Z 2025-07-17T09:02:18.6544064Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2025-07-17T09:02:18.6544193Z ----------------------------- Captured stdout call ----------------------------- 2025-07-17T09:02:18.6544277Z frames [('total', 5), ('ok', 5)] 2025-07-17T09:02:18.6544343Z inline_call [] 2025-07-17T09:02:18.6544416Z unimplemented [] 2025-07-17T09:02:18.6545145Z graph_break [('Unsupported Tensor.backward() call\n Explanation: Dynamo currently does not support tracing `Tensor.backward()`.\n Hint: This graph break is fundamental - it is unlikely that Dynamo will ever be able to trace through your code. Consider finding a workaround.\n\n Developer debug context: call_method TensorVariable() backward () {}\n', 1)] 2025-07-17T09:02:18.6545251Z stats [('calls_captured', 2), ('unique_graphs', 1)] 2025-07-17T09:02:18.6546182Z inductor [('triton_bundler_save_kernel', 480), ('benchmarking.InductorBenchmarker.benchmark_gpu', 61), ('async_compile_cache_miss', 30), ('pattern_matcher_nodes', 12), ('async_compile_cache_hit', 10), ('pattern_matcher_count', 7), ('extern_calls', 3), ('fxgraph_cache_miss', 2), ('benchmarking.InductorBenchmarker.benchmark', 2), ('select_algorithm_precompile', 1), ('select_algorithm_autotune', 1), ('inplace_padding', 1)] 2025-07-17T09:02:18.6546504Z aot_autograd [('total', 1), ('autograd_cache_miss', 1), ('ok', 1), ('autograd_cache_saved', 1)] 2025-07-17T09:02:18.6546692Z aten_mm_info [('aten.addmm_32768_50264_768', 1), ('aten.mm_32768_768_50264', 1), ('aten.mm_50264_768_32768', 1)] 2025-07-17T09:02:18.6546941Z ----------------------------- Captured stderr call ----------------------------- 2025-07-17T09:02:18.6547039Z AUTOTUNE addmm(32768x50264, 32768x768, 768x50264) 2025-07-17T09:02:18.6547113Z strides: [0, 1], [768, 1], [1, 768] 2025-07-17T09:02:18.6547220Z dtypes: torch.bfloat16, torch.bfloat16, torch.bfloat16 2025-07-17T09:02:18.6547300Z bias_addmm 5.0388 ms 100.0% 2025-07-17T09:02:18.6547364Z addmm 8.3373 ms 60.4% 2025-07-17T09:02:18.6547579Z SingleProcess AUTOTUNE benchmarking takes 0.1406 seconds and 0.0002 seconds precompiling for 2 choices 2025-07-17T09:02:18.6547701Z ----------------------------- Captured stdout call ----------------------------- 2025-07-17T09:02:18.6547781Z frames [('total', 5), ('ok', 5)] 2025-07-17T09:02:18.6547840Z inline_call [] 2025-07-17T09:02:18.6547907Z unimplemented [] 2025-07-17T09:02:18.6548620Z graph_break [('Unsupported Tensor.backward() call\n Explanation: Dynamo currently does not support tracing `Tensor.backward()`.\n Hint: This graph break is fundamental - it is unlikely that Dynamo will ever be able to trace through your code. Consider finding a workaround.\n\n Developer debug context: call_method TensorVariable() backward () {}\n', 1)] 2025-07-17T09:02:18.6548718Z stats [('calls_captured', 2), ('unique_graphs', 1)] 2025-07-17T09:02:18.6548878Z aot_autograd [('total', 1), ('autograd_cache_miss', 1), ('ok', 1), ('autograd_cache_saved', 1)] 2025-07-17T09:02:18.6549703Z inductor [('triton_bundler_save_kernel', 480), ('benchmarking.InductorBenchmarker.benchmark_gpu', 61), ('async_compile_cache_miss', 40), ('async_compile_cache_hit', 20), ('pattern_matcher_nodes', 12), ('pattern_matcher_count', 7), ('extern_calls', 3), ('fxgraph_cache_miss', 2), ('benchmarking.InductorBenchmarker.benchmark', 2), ('select_algorithm_precompile', 1), ('select_algorithm_autotune', 1), ('inplace_padding', 1)] 2025-07-17T09:02:18.6549879Z aten_mm_info [('aten.addmm_32768_50264_768', 1), ('aten.mm_32768_768_50264', 1), ('aten.mm_50264_768_32768', 1)] 2025-07-17T09:02:18.6549998Z ----------------------------- Captured stderr call ----------------------------- 2025-07-17T09:02:18.6550079Z AUTOTUNE addmm(32768x50264, 32768x768, 768x50264) 2025-07-17T09:02:18.6550149Z strides: [0, 1], [768, 1], [1, 768] 2025-07-17T09:02:18.6550247Z dtypes: torch.bfloat16, torch.bfloat16, torch.bfloat16 2025-07-17T09:02:18.6550330Z bias_addmm 5.0277 ms 100.0% 2025-07-17T09:02:18.6550392Z addmm 8.1979 ms 61.3% 2025-07-17T09:02:18.6550605Z SingleProcess AUTOTUNE benchmarking takes 0.1304 seconds and 0.0002 seconds precompiling for 2 choices 2025-07-17T09:02:18.6550719Z ----------------------------- Captured stdout call ----------------------------- 2025-07-17T09:02:18.6550801Z frames [('total', 5), ('ok', 5)] 2025-07-17T09:02:18.6550863Z inline_call [] 2025-07-17T09:02:18.6550930Z unimplemented [] 2025-07-17T09:02:18.6551628Z graph_break [('Unsupported Tensor.backward() call\n Explanation: Dynamo currently does not support tracing `Tensor.backward()`.\n Hint: This graph break is fundamental - it is unlikely that Dynamo will ever be able to trace through your code. Consider finding a workaround.\n\n Developer debug context: call_method TensorVariable() backward () {}\n', 1)] 2025-07-17T09:02:18.6551720Z stats [('calls_captured', 2), ('unique_graphs', 1)] 2025-07-17T09:02:18.6551871Z aot_autograd [('total', 1), ('autograd_cache_miss', 1), ('ok', 1), ('autograd_cache_saved', 1)] 2025-07-17T09:02:18.6552700Z inductor [('triton_bundler_save_kernel', 480), ('benchmarking.InductorBenchmarker.benchmark_gpu', 61), ('async_compile_cache_miss', 40), ('async_compile_cache_hit', 20), ('pattern_matcher_nodes', 12), ('pattern_matcher_count', 7), ('extern_calls', 3), ('fxgraph_cache_miss', 2), ('benchmarking.InductorBenchmarker.benchmark', 2), ('select_algorithm_precompile', 1), ('select_algorithm_autotune', 1), ('inplace_padding', 1)] 2025-07-17T09:02:18.6553028Z aten_mm_info [('aten.addmm_32768_50264_768', 1), ('aten.mm_32768_768_50264', 1), ('aten.mm_50264_768_32768', 1)] 2025-07-17T09:02:18.6553145Z ----------------------------- Captured stderr call ----------------------------- 2025-07-17T09:02:18.6553321Z AUTOTUNE addmm(32768x50264, 32768x768, 768x50264) 2025-07-17T09:02:18.6553399Z strides: [0, 1], [768, 1], [1, 768] 2025-07-17T09:02:18.6553500Z dtypes: torch.bfloat16, torch.bfloat16, torch.bfloat16 2025-07-17T09:02:18.6553573Z bias_addmm 5.0184 ms 100.0% 2025-07-17T09:02:18.6553635Z addmm 8.3948 ms 59.8% 2025-07-17T09:02:18.6553845Z SingleProcess AUTOTUNE benchmarking takes 0.1324 seconds and 0.0002 seconds precompiling for 2 choices 2025-07-17T09:02:18.6554216Z - generated xml file: /var/lib/jenkins/pytorch/test/test-reports/python-pytest/inductor.test_max_autotune/inductor.test_max_autotune-aef4a534f50e5f1e.xml - 2025-07-17T09:02:18.6554326Z =========================== short test summary info ============================ 2025-07-17T09:02:18.6554554Z FAILED [5.9643s] inductor/test_max_autotune.py::TestMaxAutotune::test_linear_and_cel - AssertionError: ref: 2025-07-17T09:02:18.6554825Z (tensor(11., device='cuda:0', dtype=torch.bfloat16, grad_fn=), tensor([[-4.9546e-07, 4.1677e-08, -5.2527e-07, ..., 4.9546e-07, 2025-07-17T09:02:18.6554894Z -9.3132e-07, -3.4273e-07], 2025-07-17T09:02:18.6555006Z [ 6.4448e-07, -7.0035e-07, -4.6939e-07, ..., 4.3772e-08, 2025-07-17T09:02:18.6555070Z 6.0722e-07, 1.0058e-06], 2025-07-17T09:02:18.6555165Z [-4.9826e-08, 1.0133e-06, 3.4086e-07, ..., 7.3016e-07, 2025-07-17T09:02:18.6555230Z -4.3027e-07, 8.6427e-07], 2025-07-17T09:02:18.6555293Z ..., 2025-07-17T09:02:18.6555379Z [-6.4448e-07, -8.2329e-07, -6.8545e-07, ..., -9.0152e-07, 2025-07-17T09:02:18.6555449Z 3.0361e-07, -8.9034e-07], 2025-07-17T09:02:18.6555536Z [-9.7603e-07, -6.3702e-07, 2.5705e-07, ..., 8.9034e-07, 2025-07-17T09:02:18.6555605Z 8.6427e-07, 1.9837e-07], 2025-07-17T09:02:18.6555689Z [ 5.0990e-08, -1.0654e-06, -2.1420e-07, ..., 8.7917e-07, 2025-07-17T09:02:18.6555917Z -1.6391e-07, -9.2015e-07]], device='cuda:0', dtype=torch.bfloat16), tensor([[ 8.1956e-08, 2.2054e-05, -2.2769e-05, ..., -1.5125e-06, 2025-07-17T09:02:18.6555979Z 7.4387e-05, -7.9632e-05], 2025-07-17T09:02:18.6556072Z [-4.8399e-05, -9.5367e-05, -4.7207e-05, ..., 5.1975e-05, 2025-07-17T09:02:18.6556134Z -1.0300e-04, -5.3406e-05], 2025-07-17T09:02:18.6556222Z [-1.0431e-05, -2.4438e-05, -1.1623e-05, ..., -1.9968e-06, 2025-07-17T09:02:18.6556284Z -2.7299e-05, -2.1458e-05], 2025-07-17T09:02:18.6556338Z ..., 2025-07-17T09:02:18.6556425Z [ 8.1658e-06, 4.2200e-05, 2.0862e-05, ..., -3.1471e-05, 2025-07-17T09:02:18.6556494Z -3.0547e-06, -8.4043e-06], 2025-07-17T09:02:18.6556585Z [ 3.3140e-05, 2.1577e-05, 8.1062e-06, ..., 1.8358e-05, 2025-07-17T09:02:18.6556648Z -5.0783e-05, -2.2888e-05], 2025-07-17T09:02:18.6556741Z [ 9.0338e-08, 3.3714e-07, 1.3132e-07, ..., 5.7369e-07, 2025-07-17T09:02:18.6556955Z -5.5507e-07, 9.2667e-08]], device='cuda:0', dtype=torch.bfloat16), tensor([-4.1485e-05, -4.0770e-05, -1.0431e-05, ..., -1.0550e-05, 2025-07-17T09:02:18.6557084Z -1.0848e-05, 1.9670e-05], device='cuda:0', dtype=torch.bfloat16)) 2025-07-17T09:02:18.6557142Z act: 2025-07-17T09:02:18.6557248Z (tensor(6.3750, device='cuda:0', dtype=torch.bfloat16, 2025-07-17T09:02:18.6557440Z grad_fn=), tensor([[ 2.6673e-06, -1.6975e-04, -2.5940e-04, ..., 1.6332e-05, 2025-07-17T09:02:18.6557505Z 1.3292e-05, 5.2214e-05], 2025-07-17T09:02:18.6557591Z [ 9.2387e-07, -1.6880e-04, -2.5749e-04, ..., 1.6928e-05, 2025-07-17T09:02:18.6557804Z 1.0967e-05, 5.1498e-05], 2025-07-17T09:02:18.6557891Z [ 1.6317e-06, -1.6975e-04, -2.5940e-04, ..., 1.7047e-05, 2025-07-17T09:02:18.6557955Z 1.2994e-05, 5.1260e-05], 2025-07-17T09:02:18.6558014Z ..., 2025-07-17T09:02:18.6558109Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6558169Z 0.0000e+00, 0.0000e+00], 2025-07-17T09:02:18.6558364Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6558426Z 0.0000e+00, 0.0000e+00], 2025-07-17T09:02:18.6558518Z [ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 2025-07-17T09:02:18.6558740Z 0.0000e+00, 0.0000e+00]], device='cuda:0', dtype=torch.bfloat16), tensor([[-0.0015, -0.0039, -0.0004, ..., -0.0033, 0.0006, -0.0012], 2025-07-17T09:02:18.6558850Z [-0.0015, -0.0039, -0.0004, ..., -0.0034, 0.0007, -0.0012], 2025-07-17T09:02:18.6558943Z [-0.0015, -0.0039, -0.0004, ..., -0.0033, 0.0006, -0.0012], 2025-07-17T09:02:18.6559005Z ..., 2025-07-17T09:02:18.6559094Z [-0.0015, -0.0039, -0.0005, ..., -0.0033, 0.0006, -0.0013], 2025-07-17T09:02:18.6559178Z [-0.0015, -0.0040, -0.0004, ..., -0.0033, 0.0007, -0.0013], 2025-07-17T09:02:18.6559284Z [-0.0015, -0.0039, -0.0005, ..., -0.0033, 0.0006, -0.0013]], 2025-07-17T09:02:18.6559466Z device='cuda:0', dtype=torch.bfloat16), tensor([-0.3262, -0.3262, -0.3262, ..., -0.3262, -0.3262, -0.3262], 2025-07-17T09:02:18.6559556Z device='cuda:0', dtype=torch.bfloat16)) 2025-07-17T09:02:18.6559561Z 2025-07-17T09:02:18.6559684Z To execute this test, run the following from the base repo dir: 2025-07-17T09:02:18.6559896Z PYTORCH_TEST_WITH_ROCM=1 python test/inductor/test_max_autotune.py TestMaxAutotune.test_linear_and_cel 2025-07-17T09:02:18.6559899Z 2025-07-17T09:02:18.6560039Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2025-07-17T09:02:18.6560144Z !!!!!!!!!!!!!!!!!!!!!!!!!! stopping after 1 failures !!!!!!!!!!!!!!!!!!!!!!!!!!! 2025-07-17T09:02:18.6560254Z ================== 1 failed, 71 deselected, 2 rerun in 32.60s ================== 2025-07-17T09:02:18.6560319Z Got exit code 1 2025-07-17T09:02:18.6560533Z Test failed consistently, continuing with the rest of the tests due to continue-through-error being set 2025-07-17T09:02:18.6561260Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T09:02:18.6561329Z import pkg_resources 2025-07-17T09:02:18.6562715Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py:1597: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/Context.cpp:78.) 2025-07-17T09:02:18.6562818Z _C._set_float32_matmul_precision(precision) 2025-07-17T09:02:18.6563103Z Test results will be stored in test-reports/python-pytest/inductor.test_max_autotune/inductor.test_max_autotune-c758a1945115c5b7.xml 2025-07-17T09:02:18.6563196Z ============================= test session starts ============================== 2025-07-17T09:02:18.6563378Z platform linux -- Python 3.12.11, pytest-7.3.2, pluggy-1.6.0 -- /opt/conda/envs/py_3.12/bin/python 2025-07-17T09:02:18.6563448Z cachedir: .pytest_cache 2025-07-17T09:02:18.6563711Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2025-07-17T09:02:18.6563904Z rootdir: /var/lib/jenkins/pytorch 2025-07-17T09:02:18.6563971Z configfile: pytest.ini 2025-07-17T09:02:18.6564244Z plugins: rerunfailures-14.0, subtests-0.13.1, flakefinder-1.1.0, xdist-3.3.1, xdoctest-1.1.0, hypothesis-5.35.1, cpp-2.3.0, typeguard-4.3.0 2025-07-17T09:02:18.6564368Z collecting ... collected 132 items / 10 deselected / 122 selected 2025-07-17T09:02:18.6564554Z stepcurrent: skipping 10 already run items. 2025-07-17T09:02:18.6564627Z Running 62 items in this shard 2025-07-17T09:02:18.6564631Z 2025-07-17T09:02:18.6565073Z inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_addmm_persistent_tma_a_transposed_False_b_transposed_False_dynamic_False SKIPPED [0.0004s] (Need device-side TMA support in Triton) [ 1%] 2025-07-17T09:02:18.6565486Z inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_addmm_persistent_tma_a_transposed_False_b_transposed_True_dynamic_True SKIPPED [0.0001s] (Need device-side TMA support in Triton) [ 3%] 2025-07-17T09:02:18.6565904Z inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_addmm_persistent_tma_a_transposed_True_b_transposed_False_dynamic_True SKIPPED [0.0001s] (Need device-side TMA support in Triton) [ 4%] 2025-07-17T09:02:18.6566304Z inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_addmm_persistent_tma_a_transposed_True_b_transposed_True_dynamic_True SKIPPED [0.0001s] (Need device-side TMA support in Triton) [ 6%] 2025-07-17T09:02:18.6566687Z inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_addmm_persistent_tma_illegal_alignment_dynamic_True SKIPPED [0.0001s] (Need device-side TMA support in Triton) [ 8%] 2025-07-17T09:02:18.6567007Z inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_addmm_tma_dynamic_outer_dim SKIPPED [0.0001s] (Need device-side TMA support in Triton) [ 9%] 2025-07-17T09:02:18.6567262Z inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_addmm_zero_size_input_dynamic_True PASSED [2.5610s] [ 11%] 2025-07-17T09:02:18.6567535Z inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_decompose_k_dynamic_False_bfloat16_sizes1 PASSED [7.3167s] [ 12%] 2025-07-17T09:02:18.6567806Z inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_decompose_k_dynamic_False_bfloat16_sizes2 PASSED [17.3046s] [ 14%] 2025-07-17T09:02:18.6568064Z inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_decompose_k_dynamic_False_float16_sizes0 PASSED [4.6113s] [ 16%] 2025-07-17T09:02:18.6568325Z inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_decompose_k_dynamic_False_float16_sizes1 PASSED [6.6991s] [ 17%] 2025-07-17T09:02:18.6568585Z inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_decompose_k_dynamic_True_bfloat16_sizes0 PASSED [15.5135s] [ 19%] 2025-07-17T09:02:18.6568848Z inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_decompose_k_dynamic_True_bfloat16_sizes1 PASSED [21.5637s] [ 20%] 2025-07-17T09:02:18.6569102Z inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_decompose_k_dynamic_True_bfloat16_sizes2 PASSED [18.0099s] [ 22%] 2025-07-17T09:02:18.6569357Z inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_decompose_k_dynamic_True_float16_sizes0 PASSED [15.8006s] [ 24%] 2025-07-17T09:02:18.6569613Z inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_decompose_k_dynamic_True_float16_sizes2 PASSED [17.8759s] [ 25%] 2025-07-17T09:02:18.6569923Z inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_decompose_k_dynamic_input SKIPPED [0.0002s] (decompose_k not supported on ROCm) [ 27%] 2025-07-17T09:02:18.6570243Z inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_decompose_k_dynamic_input_bwd SKIPPED [0.0001s] (decompose_k not supported on ROCm) [ 29%] 2025-07-17T09:02:18.6570561Z inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_exhaustive SKIPPED [0.0001s] (exhaustive currently only thoroughly tested on NVIDIA) [ 30%] 2025-07-17T09:02:18.6570935Z inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_mm_plus_mm_zero_size_input_dynamic_True PASSED [0.8022s] [ 32%] 2025-07-17T09:02:18.6571362Z inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_regular_mm_persistent_tma_a_transposed_False_b_transposed_False_dynamic_False SKIPPED [0.0002s] (Need device-side TMA support in Triton) [ 33%] 2025-07-17T09:02:18.6571986Z inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_regular_mm_persistent_tma_a_transposed_False_b_transposed_True_dynamic_False SKIPPED [0.0001s] (Need device-side TMA support in Triton) [ 35%] 2025-07-17T09:02:18.6572375Z inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_regular_mm_persistent_tma_illegal_alignment_dynamic_False SKIPPED [0.0001s] (Need device-side TMA support in Triton) [ 37%] 2025-07-17T09:02:18.6572760Z inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_regular_mm_persistent_tma_illegal_alignment_dynamic_True SKIPPED [0.0001s] (Need device-side TMA support in Triton) [ 38%] 2025-07-17T09:02:18.6573086Z inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_regular_mm_tma_dynamic_outer_dim SKIPPED [0.0001s] (Need device-side TMA support in Triton) [ 40%] 2025-07-17T09:02:18.6573288Z inductor/test_max_autotune.py::TestMaxAutotune::test_non_contiguous_input_bmm PASSED [16.1844s] [ 41%] 2025-07-17T09:02:18.6573486Z inductor/test_max_autotune.py::TestMaxAutotune::test_non_contiguous_input_mm PASSED [16.1747s] [ 43%] 2025-07-17T09:02:18.6574324Z inductor/test_max_autotune.py::TestMaxAutotune::test_non_contiguous_input_mm_plus_mm SKIPPED [0.0005s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/126867 for platform(s) linux, slow. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests.) [ 45%] 2025-07-17T09:02:18.6574573Z inductor/test_max_autotune.py::TestMaxAutotune::test_triton_template_generated_code_caching_bmm PASSED [2.8325s] [ 46%] 2025-07-17T09:02:18.6575420Z inductor/test_max_autotune.py::TestMaxAutotune::test_triton_template_generated_code_caching_mm_plus_mm SKIPPED [0.0006s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/157878 for platform(s) rocm. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests.) [ 48%] 2025-07-17T09:02:18.6575659Z inductor/test_max_autotune.py::TestMaxAutotunePrecompile::test_precompilation_threads PASSED [0.0044s] [ 50%] 2025-07-17T09:02:18.6576091Z inductor/test_max_autotune.py::TestMaxAutotunePrecompile::test_precompilations E0717 09:00:02.575000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Runtime error during autotuning: 2025-07-17T09:02:18.6576586Z E0717 09:00:02.575000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] No valid triton configs. OutOfResources: out of resource: shared memory, Required: 98304, Hardware limit: 65536. Reducing block sizes or `num_stages` may help.. 2025-07-17T09:02:18.6576795Z E0717 09:00:02.575000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Ignoring this choice. 2025-07-17T09:02:18.6577028Z E0717 09:00:03.213000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Runtime error during autotuning: 2025-07-17T09:02:18.6577510Z E0717 09:00:03.213000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] No valid triton configs. OutOfResources: out of resource: shared memory, Required: 98304, Hardware limit: 65536. Reducing block sizes or `num_stages` may help.. 2025-07-17T09:02:18.6577711Z E0717 09:00:03.213000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Ignoring this choice. 2025-07-17T09:02:18.6577938Z E0717 09:00:03.969000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Runtime error during autotuning: 2025-07-17T09:02:18.6578577Z E0717 09:00:03.969000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] No valid triton configs. OutOfResources: out of resource: shared memory, Required: 131072, Hardware limit: 65536. Reducing block sizes or `num_stages` may help.. 2025-07-17T09:02:18.6578770Z E0717 09:00:03.969000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Ignoring this choice. 2025-07-17T09:02:18.6579101Z E0717 09:00:04.218000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Runtime error during autotuning: 2025-07-17T09:02:18.6579594Z E0717 09:00:04.218000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] No valid triton configs. OutOfResources: out of resource: shared memory, Required: 98304, Hardware limit: 65536. Reducing block sizes or `num_stages` may help.. 2025-07-17T09:02:18.6579794Z E0717 09:00:04.218000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Ignoring this choice. 2025-07-17T09:02:18.6580027Z E0717 09:00:04.337000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Runtime error during autotuning: 2025-07-17T09:02:18.6580501Z E0717 09:00:04.337000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] No valid triton configs. OutOfResources: out of resource: shared memory, Required: 81920, Hardware limit: 65536. Reducing block sizes or `num_stages` may help.. 2025-07-17T09:02:18.6580703Z E0717 09:00:04.337000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Ignoring this choice. 2025-07-17T09:02:18.6580920Z E0717 09:00:04.721000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Runtime error during autotuning: 2025-07-17T09:02:18.6581392Z E0717 09:00:04.721000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] No valid triton configs. OutOfResources: out of resource: shared memory, Required: 98304, Hardware limit: 65536. Reducing block sizes or `num_stages` may help.. 2025-07-17T09:02:18.6581588Z E0717 09:00:04.721000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Ignoring this choice. 2025-07-17T09:02:18.6581812Z E0717 09:00:04.841000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Runtime error during autotuning: 2025-07-17T09:02:18.6582292Z E0717 09:00:04.841000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] No valid triton configs. OutOfResources: out of resource: shared memory, Required: 131072, Hardware limit: 65536. Reducing block sizes or `num_stages` may help.. 2025-07-17T09:02:18.6582496Z E0717 09:00:04.841000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Ignoring this choice. 2025-07-17T09:02:18.6582718Z E0717 09:00:06.159000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Runtime error during autotuning: 2025-07-17T09:02:18.6583190Z E0717 09:00:06.159000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] No valid triton configs. OutOfResources: out of resource: shared memory, Required: 98304, Hardware limit: 65536. Reducing block sizes or `num_stages` may help.. 2025-07-17T09:02:18.6583389Z E0717 09:00:06.159000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Ignoring this choice. 2025-07-17T09:02:18.6583619Z E0717 09:00:06.232000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Runtime error during autotuning: 2025-07-17T09:02:18.6584092Z E0717 09:00:06.232000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] No valid triton configs. OutOfResources: out of resource: shared memory, Required: 98304, Hardware limit: 65536. Reducing block sizes or `num_stages` may help.. 2025-07-17T09:02:18.6584297Z E0717 09:00:06.232000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Ignoring this choice. 2025-07-17T09:02:18.6584522Z E0717 09:00:06.324000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Runtime error during autotuning: 2025-07-17T09:02:18.6584996Z E0717 09:00:06.324000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] No valid triton configs. OutOfResources: out of resource: shared memory, Required: 131072, Hardware limit: 65536. Reducing block sizes or `num_stages` may help.. 2025-07-17T09:02:18.6585396Z E0717 09:00:06.324000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Ignoring this choice. 2025-07-17T09:02:18.6585750Z E0717 09:00:06.343000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Runtime error during autotuning: 2025-07-17T09:02:18.6586257Z E0717 09:00:06.343000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] No valid triton configs. OutOfResources: out of resource: shared memory, Required: 98304, Hardware limit: 65536. Reducing block sizes or `num_stages` may help.. 2025-07-17T09:02:18.6586461Z E0717 09:00:06.343000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Ignoring this choice. 2025-07-17T09:02:18.6586701Z E0717 09:00:06.344000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Runtime error during autotuning: 2025-07-17T09:02:18.6587171Z E0717 09:00:06.344000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] No valid triton configs. OutOfResources: out of resource: shared memory, Required: 81920, Hardware limit: 65536. Reducing block sizes or `num_stages` may help.. 2025-07-17T09:02:18.6587380Z E0717 09:00:06.344000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Ignoring this choice. 2025-07-17T09:02:18.6587601Z E0717 09:00:06.382000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Runtime error during autotuning: 2025-07-17T09:02:18.6588072Z E0717 09:00:06.382000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] No valid triton configs. OutOfResources: out of resource: shared memory, Required: 98304, Hardware limit: 65536. Reducing block sizes or `num_stages` may help.. 2025-07-17T09:02:18.6588270Z E0717 09:00:06.382000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Ignoring this choice. 2025-07-17T09:02:18.6588507Z E0717 09:00:06.383000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Runtime error during autotuning: 2025-07-17T09:02:18.6588986Z E0717 09:00:06.383000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] No valid triton configs. OutOfResources: out of resource: shared memory, Required: 131072, Hardware limit: 65536. Reducing block sizes or `num_stages` may help.. 2025-07-17T09:02:18.6589184Z E0717 09:00:06.383000 99917 site-packages/torch/_inductor/select_algorithm.py:2792] [0/0] Ignoring this choice. 2025-07-17T09:02:18.6589251Z PASSED [16.9809s] [ 51%] 2025-07-17T09:02:18.6590170Z inductor/test_max_autotune.py::TestMaxAutotuneSubproc::test_benchmark_choice_fail_in_subproc /opt/conda/envs/py_3.12/lib/python3.12/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T09:02:18.6590243Z import pkg_resources 2025-07-17T09:02:18.6591621Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py:1597: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/Context.cpp:78.) 2025-07-17T09:02:18.6591717Z _C._set_float32_matmul_precision(precision) 2025-07-17T09:02:18.6591785Z Process SpawnProcess-1: 2025-07-17T09:02:18.6591866Z Traceback (most recent call last): 2025-07-17T09:02:18.6592195Z File "/opt/conda/envs/py_3.12/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap 2025-07-17T09:02:18.6592267Z self.run() 2025-07-17T09:02:18.6592447Z File "/opt/conda/envs/py_3.12/lib/python3.12/multiprocessing/process.py", line 108, in run 2025-07-17T09:02:18.6592545Z self._target(*self._args, **self._kwargs) 2025-07-17T09:02:18.6592858Z File "/var/lib/jenkins/pytorch/test/inductor/test_max_autotune.py", line 74, in benchmark_choice 2025-07-17T09:02:18.6592941Z result = choice.benchmark(*args, out=out) 2025-07-17T09:02:18.6593003Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2025-07-17T09:02:18.6593199Z File "/var/lib/jenkins/pytorch/test/inductor/test_max_autotune.py", line 83, in benchmark 2025-07-17T09:02:18.6593323Z raise RuntimeError("This choice caller will always throw") 2025-07-17T09:02:18.6593429Z RuntimeError: This choice caller will always throw 2025-07-17T09:02:18.6593495Z PASSED [9.6863s] [ 53%] 2025-07-17T09:02:18.6593844Z inductor/test_max_autotune.py::TestMaxAutotuneSubproc::test_max_autotune_mm_plus_mm_autotune_in_subproc_True_autotune_multi_device_False PASSED [4.7604s] [ 54%] 2025-07-17T09:02:18.6594177Z inductor/test_max_autotune.py::TestMaxAutotuneSubproc::test_max_autotune_mm_plus_mm_autotune_in_subproc_True_autotune_multi_device_True PASSED [2.6683s] [ 56%] 2025-07-17T09:02:18.6594434Z inductor/test_max_autotune.py::TestMaxAutotuneSubproc::test_max_autotune_regular_mm_dynamic_False PASSED [5.9203s] [ 58%] 2025-07-17T09:02:18.6594715Z inductor/test_max_autotune.py::TestMaxAutotuneSubproc::test_triton_template_with_epilogues_and_dynamic_shape PASSED [5.4273s] [ 59%] 2025-07-17T09:02:18.6594987Z inductor/test_max_autotune.py::TestMaxAutotuneRemoteCache::test_max_autotune_remote_caching_dynamic_False PASSED [17.2348s] [ 61%] 2025-07-17T09:02:18.6595258Z inductor/test_max_autotune.py::TestMaxAutotuneRemoteCache::test_max_autotune_remote_caching_dynamic_True PASSED [29.5962s] [ 62%] 2025-07-17T09:02:18.6595455Z inductor/test_max_autotune.py::TestTuningProcess::test_tuning_subproc_crash PASSED [4.4257s] [ 64%] 2025-07-17T09:02:18.6595662Z inductor/test_max_autotune.py::TestTuningProcessPool::test_tuning_pool_crash PASSED [4.4033s] [ 66%] 2025-07-17T09:02:18.6595886Z inductor/test_max_autotune.py::TestTuningProcessPool::test_tuning_pool_multiple_devices PASSED [2.4312s] [ 67%] 2025-07-17T09:02:18.6596084Z inductor/test_max_autotune.py::TestPrologueFusion::test_broadcast_x_K_63 PASSED [1.7630s] [ 69%] 2025-07-17T09:02:18.6596274Z inductor/test_max_autotune.py::TestPrologueFusion::test_broadcast_x_K_64 PASSED [1.7450s] [ 70%] 2025-07-17T09:02:18.6596462Z inductor/test_max_autotune.py::TestPrologueFusion::test_broadcast_y PASSED [1.5024s] [ 72%] 2025-07-17T09:02:18.6596711Z inductor/test_max_autotune.py::TestPrologueFusion::test_gather_fusion SKIPPED [0.0002s] (Triton bug in compilation) [ 74%] 2025-07-17T09:02:18.6596922Z inductor/test_max_autotune.py::TestPrologueFusion::test_multiple_fusions_sizes2 PASSED [1.0884s] [ 75%] 2025-07-17T09:02:18.6597128Z inductor/test_max_autotune.py::TestPrologueFusion::test_multiple_inputs_sizes0 PASSED [1.2694s] [ 77%] 2025-07-17T09:02:18.6597328Z inductor/test_max_autotune.py::TestPrologueFusion::test_multiple_inputs_sizes1 PASSED [1.2914s] [ 79%] 2025-07-17T09:02:18.6597524Z inductor/test_max_autotune.py::TestPrologueFusion::test_multiple_inputs_sizes2 PASSED [1.2775s] [ 80%] 2025-07-17T09:02:18.6597744Z inductor/test_max_autotune.py::TestPrologueFusion::test_pending_fusion_pro_and_epi PASSED [2.7350s] [ 82%] 2025-07-17T09:02:18.6597952Z inductor/test_max_autotune.py::TestPrologueFusion::test_pending_fusions_multiple PASSED [6.2329s] [ 83%] 2025-07-17T09:02:18.6598162Z inductor/test_max_autotune.py::TestPrologueFusion::test_preserves_zero_analysis PASSED [1.7112s] [ 85%] 2025-07-17T09:02:18.6598374Z inductor/test_max_autotune.py::TestPrologueFusion::test_prologue_masked_load_sizes1 PASSED [1.4238s] [ 87%] 2025-07-17T09:02:18.6598587Z inductor/test_max_autotune.py::TestPrologueFusion::test_prologue_masked_load_sizes2 PASSED [1.4167s] [ 88%] 2025-07-17T09:02:18.6598921Z inductor/test_max_autotune.py::TestPrologueFusion::test_prologue_multiple_nodes_sizes0 PASSED [1.0408s] [ 90%] 2025-07-17T09:02:18.6599146Z inductor/test_max_autotune.py::TestPrologueFusion::test_prologue_multiple_nodes_sizes1 PASSED [1.1008s] [ 91%] 2025-07-17T09:02:18.6599362Z inductor/test_max_autotune.py::TestPrologueFusion::test_prologue_multiple_nodes_sizes2 PASSED [1.0763s] [ 93%] 2025-07-17T09:02:18.6599760Z inductor/test_max_autotune.py::TestPrologueFusion::test_prologue_read_into_both_inputs_benchmark_fusion_False PASSED [1.3571s] [ 95%] 2025-07-17T09:02:18.6600040Z inductor/test_max_autotune.py::TestPrologueFusion::test_prologue_read_into_both_inputs_benchmark_fusion_True PASSED [1.3468s] [ 96%] 2025-07-17T09:02:18.6600258Z inductor/test_max_autotune.py::TestPrologueFusion::test_storage_offset_prologue PASSED [1.0892s] [ 98%] 2025-07-17T09:02:18.6600446Z inductor/test_max_autotune.py::TestPrologueFusion::test_upcast_sizes0 PASSED [0.9828s] [100%] 2025-07-17T09:02:18.6600453Z 2025-07-17T09:02:18.6600819Z - generated xml file: /var/lib/jenkins/pytorch/test/test-reports/python-pytest/inductor.test_max_autotune/inductor.test_max_autotune-c758a1945115c5b7.xml - 2025-07-17T09:02:18.6600944Z ========== 45 passed, 17 skipped, 10 deselected in 298.33s (0:04:58) =========== 2025-07-17T09:02:18.6601190Z The following tests failed consistently: ['test/inductor/test_max_autotune.py::TestMaxAutotune::test_linear_and_cel'] 2025-07-17T09:02:18.6601195Z 2025-07-17T09:02:18.6601466Z FINISHED PRINTING LOG FILE of inductor/test_max_autotune 1/2 (test/test-reports/inductor.test_max_autotune_1.2_c5c210428b66da3e_.log) 2025-07-17T09:02:18.6601469Z 2025-07-17T09:02:18.6601631Z GITHUB_RUN_ID, GITHUB_RUN_ATTEMPT, or ARTIFACTS_FILE_SUFFIX not set, not uploading 2025-07-17T09:02:18.6601711Z Uploading artifacts took 0.00 seconds 2025-07-17T09:02:18.6601787Z inductor/test_max_autotune 1/2 failed! 2025-07-17T09:02:18.6601920Z Running inductor/test_max_autotune 2/2 ... [2025-07-17 09:02:18.603282] 2025-07-17T09:02:18.6602003Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T09:02:18.6602492Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_max_autotune.py', '--shard-id=2', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 09:02:18.603589] 2025-07-17T09:05:49.1080712Z 2025-07-17T09:05:49.1081787Z inductor/test_max_autotune 2/2 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_max_autotune_2.2_e6d8b22c9d535236_.log 2025-07-17T09:05:49.1096864Z Running 60 items in this shard: test/inductor/test_max_autotune.py::TestMaxAutotune::test_autotune_conv1x1, test/inductor/test_max_autotune.py::TestMaxAutotune::test_baddmm, test/inductor/test_max_autotune.py::TestMaxAutotune::test_cat_addmm, test/inductor/test_max_autotune.py::TestMaxAutotune::test_cat_max_autotune_triton, test/inductor/test_max_autotune.py::TestMaxAutotune::test_conv_cat, test/inductor/test_max_autotune.py::TestMaxAutotune::test_empty_conv_input, test/inductor/test_max_autotune.py::TestMaxAutotune::test_empty_conv_input_with_1x1_kernel, test/inductor/test_max_autotune.py::TestMaxAutotune::test_honor_sm_carveout_with_triton_tma_carveout0_op_scaled_mm, test/inductor/test_max_autotune.py::TestMaxAutotune::test_honor_sm_carveout_with_triton_tma_carveout_0_op_mm, test/inductor/test_max_autotune.py::TestMaxAutotune::test_honor_sm_carveout_with_triton_tma_carveout_0_op_scaled_mm, test/inductor/test_max_autotune.py::TestMaxAutotune::test_inf_timing_multi_template_False, test/inductor/test_max_autotune.py::TestMaxAutotune::test_inf_timing_multi_template_True, test/inductor/test_max_autotune.py::TestMaxAutotune::test_matmul_dropout_device_cpu, test/inductor/test_max_autotune.py::TestMaxAutotune::test_matmul_dropout_device_cuda, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_addmm_persistent_tma_a_transposed_False_b_transposed_False_dynamic_True, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_addmm_persistent_tma_a_transposed_False_b_transposed_True_dynamic_False, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_addmm_persistent_tma_a_transposed_True_b_transposed_False_dynamic_False, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_addmm_persistent_tma_a_transposed_True_b_transposed_True_dynamic_False, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_addmm_persistent_tma_illegal_alignment_dynamic_False, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_addmm_zero_size_input_dynamic_False, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_decompose_k_dynamic_False_bfloat16_sizes0, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_decompose_k_dynamic_False_float16_sizes2, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_decompose_k_dynamic_True_float16_sizes1, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_decompose_k_output_stride, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_disable_decompose_K, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_mm_plus_mm_zero_size_input_dynamic_False, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_regular_mm_persistent_tma_a_transposed_False_b_transposed_False_dynamic_True, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_regular_mm_persistent_tma_a_transposed_False_b_transposed_True_dynamic_True, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_regular_mm_persistent_tma_a_transposed_True_b_transposed_False_dynamic_False, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_regular_mm_persistent_tma_a_transposed_True_b_transposed_False_dynamic_True, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_regular_mm_persistent_tma_a_transposed_True_b_transposed_True_dynamic_False, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_regular_mm_persistent_tma_a_transposed_True_b_transposed_True_dynamic_True, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_regular_mm_zero_size_input_dynamic_False, test/inductor/test_max_autotune.py::TestMaxAutotune::test_max_autotune_regular_mm_zero_size_input_dynamic_True, test/inductor/test_max_autotune.py::TestMaxAutotune::test_mutation_rename, test/inductor/test_max_autotune.py::TestMaxAutotune::test_no_valid_choices, test/inductor/test_max_autotune.py::TestMaxAutotune::test_non_contiguous_input_addmm, test/inductor/test_max_autotune.py::TestMaxAutotune::test_triton_template_generated_code_cache_key, test/inductor/test_max_autotune.py::TestMaxAutotune::test_triton_template_generated_code_cache_strategy, test/inductor/test_max_autotune.py::TestMaxAutotune::test_triton_template_generated_code_caching, test/inductor/test_max_autotune.py::TestMaxAutotunePrecompile::test_filled_cache_precompile, test/inductor/test_max_autotune.py::TestMaxAutotuneSubproc::test_benchmark_choice_in_subproc, test/inductor/test_max_autotune.py::TestMaxAutotuneSubproc::test_max_autotune_addmm_dynamic_False, test/inductor/test_max_autotune.py::TestMaxAutotuneSubproc::test_max_autotune_addmm_dynamic_True, test/inductor/test_max_autotune.py::TestMaxAutotuneSubproc::test_max_autotune_mm_plus_mm_autotune_in_subproc_False_autotune_multi_device_False, test/inductor/test_max_autotune.py::TestMaxAutotuneSubproc::test_max_autotune_mm_plus_mm_autotune_in_subproc_False_autotune_multi_device_True, test/inductor/test_max_autotune.py::TestMaxAutotuneSubproc::test_max_autotune_regular_mm_dynamic_True, test/inductor/test_max_autotune.py::TestTuningProcess::test_tuning_subproc_exception, test/inductor/test_max_autotune.py::TestTuningProcess::test_tuning_subproc_killed, test/inductor/test_max_autotune.py::TestTuningProcess::test_tuning_subproc_timeout, test/inductor/test_max_autotune.py::TestTuningProcess::test_visible_devices, test/inductor/test_max_autotune.py::TestTuningProcessPool::test_tuning_pool_timeout, test/inductor/test_max_autotune.py::TestPrologueFusion::test_downcast, test/inductor/test_max_autotune.py::TestPrologueFusion::test_low_precision, test/inductor/test_max_autotune.py::TestPrologueFusion::test_mismatched_prologue_group, test/inductor/test_max_autotune.py::TestPrologueFusion::test_multiple_fusions_sizes0, test/inductor/test_max_autotune.py::TestPrologueFusion::test_multiple_fusions_sizes1, test/inductor/test_max_autotune.py::TestPrologueFusion::test_prologue_masked_load_sizes0, test/inductor/test_max_autotune.py::TestPrologueFusion::test_upcast_sizes1, test/inductor/test_max_autotune.py::TestPrologueFusion::test_upcast_sizes2 2025-07-17T09:05:49.1111302Z 2025-07-17T09:05:49.1111423Z Running doctests 1/1 ... [2025-07-17 09:05:49.108151] 2025-07-17T09:05:49.1404830Z Start doctest_module('/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch') 2025-07-17T09:05:49.1405187Z Listing tests 2025-07-17T09:05:49.2287662Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py line=1285. 2025-07-17T09:05:49.2288284Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:49.2288723Z load(f, map_location=None, pickle_module=pickle, *, weights_only=True, mmap=None, **pickle_load_args) 2025-07-17T09:05:49.2288982Z 2025-07-17T09:05:49.2289102Z Loads an object saved with :func:`torch.save` from a file. 2025-07-17T09:05:49.2289305Z 2025-07-17T09:05:49.2289458Z :func:`torch.load` uses Python's unpickling facilities but treats storages, 2025-07-17T09:05:49.2289808Z which underlie tensors, specially. They are first deserialized on the 2025-07-17T09:05:49.2290174Z CPU and are then moved to the device they were saved from. If this fails 2025-07-17T09:05:49.2290519Z (e.g. because the run time system doesn't have certain devices), an exception 2025-07-17T09:05:49.2290871Z is raised. However, storages can be dynamically remapped to an alternative 2025-07-17T09:05:49.2291189Z set of devices using the :attr:`map_location` argument. 2025-07-17T09:05:49.2291356Z 2025-07-17T09:05:49.2291517Z If :attr:`map_location` is a callable, it will be called once for each serialized 2025-07-17T09:05:49.2291865Z storage with two arguments: storage and location. The storage argument 2025-07-17T09:05:49.2292213Z will be the initial deserialization of the storage, residing on the CPU. 2025-07-17T09:05:49.2292559Z Each serialized storage has a location tag associated with it which 2025-07-17T09:05:49.2292885Z identifies the device it was saved from, and this tag is the second 2025-07-17T09:05:49.2293220Z argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'`` 2025-07-17T09:05:49.2293578Z for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors. 2025-07-17T09:05:49.2293897Z :attr:`map_location` should return either ``None`` or a storage. If 2025-07-17T09:05:49.2294237Z :attr:`map_location` returns a storage, it will be used as the final deserialized 2025-07-17T09:05:49.2294618Z object, already moved to the right device. Otherwise, :func:`torch.load` will 2025-07-17T09:05:49.2294995Z fall back to the default behavior, as if :attr:`map_location` wasn't specified. 2025-07-17T09:05:49.2295211Z 2025-07-17T09:05:49.2295353Z If :attr:`map_location` is a :class:`torch.device` object or a string containing 2025-07-17T09:05:49.2295699Z a device tag, it indicates the location where all tensors should be loaded. 2025-07-17T09:05:49.2295906Z 2025-07-17T09:05:49.2296079Z Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags 2025-07-17T09:05:49.2296423Z appearing in the file (keys), to ones that specify where to put the 2025-07-17T09:05:49.2296680Z storages (values). 2025-07-17T09:05:49.2296798Z 2025-07-17T09:05:49.2296932Z User extensions can register their own location tags and tagging and 2025-07-17T09:05:49.2297298Z deserialization methods using :func:`torch.serialization.register_package`. 2025-07-17T09:05:49.2298069Z 2025-07-17T09:05:49.2298216Z See :ref:`layout-control` for more advanced tools to manipulate a checkpoint. 2025-07-17T09:05:49.2298425Z 2025-07-17T09:05:49.2298486Z Args: 2025-07-17T09:05:49.2298761Z f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`), 2025-07-17T09:05:49.2299308Z or a string or os.PathLike object containing a file name 2025-07-17T09:05:49.2299675Z map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage 2025-07-17T09:05:49.2299991Z locations 2025-07-17T09:05:49.2300238Z pickle_module: module used for unpickling metadata and objects (has to 2025-07-17T09:05:49.2300552Z match the :attr:`pickle_module` used to serialize file) 2025-07-17T09:05:49.2300854Z weights_only: Indicates whether unpickler should be restricted to 2025-07-17T09:05:49.2301164Z loading only tensors, primitive types, dictionaries 2025-07-17T09:05:49.2301463Z and any types added via :func:`torch.serialization.add_safe_globals`. 2025-07-17T09:05:49.2301753Z See :ref:`weights-only` for more details. 2025-07-17T09:05:49.2302109Z mmap: Indicates whether the file should be mapped rather than loading all the storages into memory. 2025-07-17T09:05:49.2302567Z Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they 2025-07-17T09:05:49.2303020Z are moved to the location that they were tagged with when saving, or specified by ``map_location``. This 2025-07-17T09:05:49.2303475Z second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the 2025-07-17T09:05:49.2303926Z tensor storages from disk to CPU memory in the first step, ``f`` is mapped, which means tensor storages 2025-07-17T09:05:49.2304285Z will be lazily loaded when their data is accessed. 2025-07-17T09:05:49.2304596Z pickle_load_args: (Python 3 only) optional keyword arguments passed over to 2025-07-17T09:05:49.2304936Z :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g., 2025-07-17T09:05:49.2305205Z :attr:`errors=...`. 2025-07-17T09:05:49.2305391Z 2025-07-17T09:05:49.2305485Z .. warning:: 2025-07-17T09:05:49.2305720Z :func:`torch.load()` unless `weights_only` parameter is set to `True`, 2025-07-17T09:05:49.2306039Z uses ``pickle`` module implicitly, which is known to be insecure. 2025-07-17T09:05:49.2306389Z It is possible to construct malicious pickle data which will execute arbitrary code 2025-07-17T09:05:49.2306761Z during unpickling. Never load data that could have come from an untrusted 2025-07-17T09:05:49.2307136Z source in an unsafe mode, or that could have been tampered with. **Only load data you trust**. 2025-07-17T09:05:49.2307370Z 2025-07-17T09:05:49.2307441Z .. note:: 2025-07-17T09:05:49.2307681Z When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors 2025-07-17T09:05:49.2308056Z will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')`` 2025-07-17T09:05:49.2308431Z and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint. 2025-07-17T09:05:49.2308657Z 2025-07-17T09:05:49.2308722Z .. note:: 2025-07-17T09:05:49.2308963Z By default, we decode byte strings as ``utf-8``. This is to avoid a common error 2025-07-17T09:05:49.2309311Z case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...`` 2025-07-17T09:05:49.2309631Z when loading files saved by Python 2 in Python 3. If this default 2025-07-17T09:05:49.2309971Z is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how 2025-07-17T09:05:49.2310335Z these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them 2025-07-17T09:05:49.2310835Z to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them 2025-07-17T09:05:49.2311182Z as byte arrays which can be decoded later with ``byte_array.decode(...)``. 2025-07-17T09:05:49.2311376Z 2025-07-17T09:05:49.2311450Z Example: 2025-07-17T09:05:49.2311636Z >>> # xdoctest: +SKIP("undefined filepaths") 2025-07-17T09:05:49.2312013Z >>> torch.load("tensors.pt", weights_only=True) 2025-07-17T09:05:49.2312255Z # Load all tensors onto the CPU 2025-07-17T09:05:49.2312464Z >>> torch.load( 2025-07-17T09:05:49.2312637Z ... "tensors.pt", 2025-07-17T09:05:49.2312843Z ... map_location=torch.device("cpu"), 2025-07-17T09:05:49.2313065Z ... weights_only=True, 2025-07-17T09:05:49.2313245Z ... ) 2025-07-17T09:05:49.2313433Z # Load all tensors onto the CPU, using a function 2025-07-17T09:05:49.2313652Z >>> torch.load( 2025-07-17T09:05:49.2313841Z ... "tensors.pt", 2025-07-17T09:05:49.2314053Z ... map_location=lambda storage, loc: storage, 2025-07-17T09:05:49.2314281Z ... weights_only=True, 2025-07-17T09:05:49.2314459Z ... ) 2025-07-17T09:05:49.2314608Z # Load all tensors onto GPU 1 2025-07-17T09:05:49.2314802Z >>> torch.load( 2025-07-17T09:05:49.2314963Z ... "tensors.pt", 2025-07-17T09:05:49.2315215Z ... map_location=lambda storage, loc: storage.cuda(1), 2025-07-17T09:05:49.2315451Z ... weights_only=True, 2025-07-17T09:05:49.2315650Z ... ) # type: ignore[attr-defined] 2025-07-17T09:05:49.2315865Z # Map tensors from GPU 1 to GPU 0 2025-07-17T09:05:49.2316056Z >>> torch.load( 2025-07-17T09:05:49.2316226Z ... "tensors.pt", 2025-07-17T09:05:49.2316424Z ... map_location={"cuda:1": "cuda:0"}, 2025-07-17T09:05:49.2316634Z ... weights_only=True, 2025-07-17T09:05:49.2316825Z ... ) 2025-07-17T09:05:49.2316997Z # Load tensor from io.BytesIO object 2025-07-17T09:05:49.2317293Z # Loading from a buffer setting weights_only=False, warning this can be unsafe 2025-07-17T09:05:49.2317591Z >>> with open("tensor.pt", "rb") as f: 2025-07-17T09:05:49.2317803Z ... buffer = io.BytesIO(f.read()) 2025-07-17T09:05:49.2318023Z >>> torch.load(buffer, weights_only=False) 2025-07-17T09:05:49.2318272Z # Load a module with 'ascii' encoding for unpickling 2025-07-17T09:05:49.2318582Z # Loading from a module setting weights_only=False, warning this can be unsafe 2025-07-17T09:05:49.2318904Z >>> torch.load("module.pt", encoding="ascii", weights_only=False) 2025-07-17T09:05:49.2319149Z 2025-07-17T09:05:49.2319378Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:49.2319598Z 2025-07-17T09:05:49.4332621Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=560. 2025-07-17T09:05:49.4333224Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:49.4333468Z 2025-07-17T09:05:49.4333578Z Load a model from a github repo or a local directory. 2025-07-17T09:05:49.4333754Z 2025-07-17T09:05:49.4333895Z Note: Loading a model is the typical use case, but this can also be used to 2025-07-17T09:05:49.4334237Z for loading other objects such as tokenizers, loss functions, etc. 2025-07-17T09:05:49.4334452Z 2025-07-17T09:05:49.4334569Z If ``source`` is 'github', ``repo_or_dir`` is expected to be 2025-07-17T09:05:49.4334855Z of the form ``repo_owner/repo_name[:ref]`` with an optional 2025-07-17T09:05:49.4335110Z ref (a tag or a branch). 2025-07-17T09:05:49.4335229Z 2025-07-17T09:05:49.4335337Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a 2025-07-17T09:05:49.4335677Z path to a local directory. 2025-07-17T09:05:49.4335791Z 2025-07-17T09:05:49.4335862Z Args: 2025-07-17T09:05:49.4336043Z repo_or_dir (str): If ``source`` is 'github', 2025-07-17T09:05:49.4336897Z this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with 2025-07-17T09:05:49.4337316Z an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified, 2025-07-17T09:05:49.4337733Z the default branch is assumed to be ``main`` if it exists, and otherwise ``master``. 2025-07-17T09:05:49.4338279Z If ``source`` is 'local' then it should be a path to a local directory. 2025-07-17T09:05:49.4338608Z model (str): the name of a callable (entrypoint) defined in the 2025-07-17T09:05:49.4338872Z repo/dir's ``hubconf.py``. 2025-07-17T09:05:49.4339135Z *args (optional): the corresponding args for callable ``model``. 2025-07-17T09:05:49.4339442Z source (str, optional): 'github' or 'local'. Specifies how 2025-07-17T09:05:49.4339731Z ``repo_or_dir`` is to be interpreted. Default is 'github'. 2025-07-17T09:05:49.4340039Z trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``. 2025-07-17T09:05:49.4340386Z This parameter was introduced in v1.12 and helps ensuring that users 2025-07-17T09:05:49.4340675Z only run code from repos that they trust. 2025-07-17T09:05:49.4340829Z 2025-07-17T09:05:49.4340962Z - If ``False``, a prompt will ask the user whether the repo should 2025-07-17T09:05:49.4341226Z be trusted. 2025-07-17T09:05:49.4341463Z - If ``True``, the repo will be added to the trusted list and loaded 2025-07-17T09:05:49.4341761Z without requiring explicit confirmation. 2025-07-17T09:05:49.4342029Z - If ``"check"``, the repo will be checked against the list of 2025-07-17T09:05:49.4342330Z trusted repos in the cache. If it is not present in that list, the 2025-07-17T09:05:49.4342651Z behaviour will fall back onto the ``trust_repo=False`` option. 2025-07-17T09:05:49.4342959Z - If ``None``: this will raise a warning, inviting the user to set 2025-07-17T09:05:49.4343259Z ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This 2025-07-17T09:05:49.4343568Z is only present for backward compatibility and will be removed in 2025-07-17T09:05:49.4343826Z v2.0. 2025-07-17T09:05:49.4343920Z 2025-07-17T09:05:49.4344067Z Default is ``None`` and will eventually change to ``"check"`` in v2.0. 2025-07-17T09:05:49.4344392Z force_reload (bool, optional): whether to force a fresh download of 2025-07-17T09:05:49.4344712Z the github repo unconditionally. Does not have any effect if 2025-07-17T09:05:49.4344978Z ``source = 'local'``. Default is ``False``. 2025-07-17T09:05:49.4345251Z verbose (bool, optional): If ``False``, mute messages about hitting 2025-07-17T09:05:49.4345733Z local caches. Note that the message about first download cannot be 2025-07-17T09:05:49.4346037Z muted. Does not have any effect if ``source = 'local'``. 2025-07-17T09:05:49.4346279Z Default is ``True``. 2025-07-17T09:05:49.4346579Z skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit 2025-07-17T09:05:49.4346996Z specified by the ``github`` argument properly belongs to the repo owner. This will make 2025-07-17T09:05:49.4347393Z requests to the GitHub API; you can specify a non-default GitHub token by setting the 2025-07-17T09:05:49.4347739Z ``GITHUB_TOKEN`` environment variable. Default is ``False``. 2025-07-17T09:05:49.4348054Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2025-07-17T09:05:49.4348247Z 2025-07-17T09:05:49.4348318Z Returns: 2025-07-17T09:05:49.4348525Z The output of the ``model`` callable when called with the given 2025-07-17T09:05:49.4348777Z ``*args`` and ``**kwargs``. 2025-07-17T09:05:49.4348905Z 2025-07-17T09:05:49.4348967Z Example: 2025-07-17T09:05:49.4349146Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-07-17T09:05:49.4349372Z >>> # from a github repo 2025-07-17T09:05:49.4349746Z >>> repo = "pytorch/vision" 2025-07-17T09:05:49.4349943Z >>> model = torch.hub.load( 2025-07-17T09:05:49.4350207Z ... repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1" 2025-07-17T09:05:49.4350457Z ... ) 2025-07-17T09:05:49.4350625Z >>> # from a local directory 2025-07-17T09:05:49.4350839Z >>> path = "/some/local/path/pytorch/vision" 2025-07-17T09:05:49.4351065Z >>> # xdoctest: +SKIP 2025-07-17T09:05:49.4351651Z >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT") 2025-07-17T09:05:49.4351869Z 2025-07-17T09:05:49.4352025Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:49.4352247Z 2025-07-17T09:05:49.4352508Z msg = Cannot scrape callname=_load_local in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=652. 2025-07-17T09:05:49.4352981Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:49.4353218Z 2025-07-17T09:05:49.4353331Z Load a model from a local directory with a ``hubconf.py``. 2025-07-17T09:05:49.4353507Z 2025-07-17T09:05:49.4353567Z Args: 2025-07-17T09:05:49.4353782Z hubconf_dir (str): path to a local directory that contains a 2025-07-17T09:05:49.4354032Z ``hubconf.py``. 2025-07-17T09:05:49.4354258Z model (str): name of an entrypoint defined in the directory's 2025-07-17T09:05:49.4354495Z ``hubconf.py``. 2025-07-17T09:05:49.4354732Z *args (optional): the corresponding args for callable ``model``. 2025-07-17T09:05:49.4355054Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2025-07-17T09:05:49.4355246Z 2025-07-17T09:05:49.4355318Z Returns: 2025-07-17T09:05:49.4355530Z a single model with corresponding pretrained weights. 2025-07-17T09:05:49.4355703Z 2025-07-17T09:05:49.4355779Z Example: 2025-07-17T09:05:49.4355950Z >>> # xdoctest: +SKIP("stub local path") 2025-07-17T09:05:49.4356166Z >>> path = "/some/local/path/pytorch/vision" 2025-07-17T09:05:49.4356395Z >>> model = _load_local( 2025-07-17T09:05:49.4356582Z ... path, 2025-07-17T09:05:49.4356750Z ... "resnet50", 2025-07-17T09:05:49.4356945Z ... weights="ResNet50_Weights.IMAGENET1K_V1", 2025-07-17T09:05:49.4357160Z ... ) 2025-07-17T09:05:49.4357241Z 2025-07-17T09:05:49.4357402Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:49.4357611Z 2025-07-17T09:05:49.4357901Z msg = Cannot scrape callname=download_url_to_file in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=691. 2025-07-17T09:05:49.4358395Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:49.4358716Z Download object at the given URL to a local path. 2025-07-17T09:05:49.4358869Z 2025-07-17T09:05:49.4358939Z Args: 2025-07-17T09:05:49.4359107Z url (str): URL of the object to download 2025-07-17T09:05:49.4359387Z dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file`` 2025-07-17T09:05:49.4359789Z hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``. 2025-07-17T09:05:49.4360111Z Default: None 2025-07-17T09:05:49.4360365Z progress (bool, optional): whether or not to display a progress bar to stderr 2025-07-17T09:05:49.4360640Z Default: True 2025-07-17T09:05:49.4360747Z 2025-07-17T09:05:49.4360809Z Example: 2025-07-17T09:05:49.4361003Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-07-17T09:05:49.4361230Z >>> # xdoctest: +REQUIRES(POSIX) 2025-07-17T09:05:49.4361446Z >>> torch.hub.download_url_to_file( 2025-07-17T09:05:49.4361738Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth", 2025-07-17T09:05:49.4362012Z ... "/tmp/temporary_file", 2025-07-17T09:05:49.4362210Z ... ) 2025-07-17T09:05:49.4362297Z 2025-07-17T09:05:49.4362363Z 2025-07-17T09:05:49.4362754Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:49.4362962Z 2025-07-17T09:05:49.4363246Z msg = Cannot scrape callname=load_state_dict_from_url in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=816. 2025-07-17T09:05:49.4363731Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:49.4364040Z Loads the Torch serialized object at the given URL. 2025-07-17T09:05:49.4364309Z 2025-07-17T09:05:49.4364431Z If downloaded file is a zip file, it will be automatically 2025-07-17T09:05:49.4364675Z decompressed. 2025-07-17T09:05:49.4364775Z 2025-07-17T09:05:49.4364909Z If the object is already present in `model_dir`, it's deserialized and 2025-07-17T09:05:49.4365161Z returned. 2025-07-17T09:05:49.4365380Z The default value of ``model_dir`` is ``/checkpoints`` where 2025-07-17T09:05:49.4365690Z ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`. 2025-07-17T09:05:49.4365882Z 2025-07-17T09:05:49.4365943Z Args: 2025-07-17T09:05:49.4366108Z url (str): URL of the object to download 2025-07-17T09:05:49.4366370Z model_dir (str, optional): directory in which to save the object 2025-07-17T09:05:49.4366761Z map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load) 2025-07-17T09:05:49.4367183Z progress (bool, optional): whether or not to display a progress bar to stderr. 2025-07-17T09:05:49.4367465Z Default: True 2025-07-17T09:05:49.4367770Z check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention 2025-07-17T09:05:49.4368157Z ``filename-.ext`` where ```` is the first eight or more 2025-07-17T09:05:49.4368493Z digits of the SHA256 hash of the contents of the file. The hash is used to 2025-07-17T09:05:49.4368807Z ensure unique names and to verify the contents of the file. 2025-07-17T09:05:49.4369063Z Default: False 2025-07-17T09:05:49.4369374Z file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set. 2025-07-17T09:05:49.4369823Z weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects. 2025-07-17T09:05:49.4370238Z Recommended for untrusted sources. See :func:`~torch.load` for more details. 2025-07-17T09:05:49.4370464Z 2025-07-17T09:05:49.4370533Z Example: 2025-07-17T09:05:49.4370721Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-07-17T09:05:49.4370966Z >>> state_dict = torch.hub.load_state_dict_from_url( 2025-07-17T09:05:49.4371264Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth" 2025-07-17T09:05:49.4371511Z ... ) 2025-07-17T09:05:49.4371592Z 2025-07-17T09:05:49.4371659Z 2025-07-17T09:05:49.4371893Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:49.4372103Z 2025-07-17T09:05:49.4511675Z msg = Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=446. 2025-07-17T09:05:49.4512245Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:49.4512636Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2025-07-17T09:05:49.4512852Z 2025-07-17T09:05:49.4513013Z This is helpful when you want to visualize data over some 2025-07-17T09:05:49.4513297Z range of inputs. See below for a plotting example. 2025-07-17T09:05:49.4513455Z 2025-07-17T09:05:49.4513578Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2025-07-17T09:05:49.4513860Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2025-07-17T09:05:49.4514164Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2025-07-17T09:05:49.4514443Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2025-07-17T09:05:49.4515031Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2025-07-17T09:05:49.4515291Z to the result shape. 2025-07-17T09:05:49.4515454Z 2025-07-17T09:05:49.4515540Z .. note:: 2025-07-17T09:05:49.4515748Z 0D inputs are treated equivalently to 1D inputs of a 2025-07-17T09:05:49.4515986Z single element. 2025-07-17T09:05:49.4516105Z 2025-07-17T09:05:49.4516168Z .. warning:: 2025-07-17T09:05:49.4516552Z `torch.meshgrid(*tensors)` currently has the same behavior 2025-07-17T09:05:49.4516839Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2025-07-17T09:05:49.4517006Z 2025-07-17T09:05:49.4517104Z In the future `torch.meshgrid` will transition to 2025-07-17T09:05:49.4517345Z `indexing='xy'` as the default. 2025-07-17T09:05:49.4517478Z 2025-07-17T09:05:49.4517613Z https://github.com/pytorch/pytorch/issues/50276 tracks 2025-07-17T09:05:49.4517933Z this issue with the goal of migrating to NumPy's behavior. 2025-07-17T09:05:49.4518126Z 2025-07-17T09:05:49.4518206Z .. seealso:: 2025-07-17T09:05:49.4518301Z 2025-07-17T09:05:49.4518428Z :func:`torch.cartesian_prod` has the same effect but it 2025-07-17T09:05:49.4518695Z collects the data in a tensor of vectors. 2025-07-17T09:05:49.4518841Z 2025-07-17T09:05:49.4518913Z Args: 2025-07-17T09:05:49.4519176Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2025-07-17T09:05:49.4519513Z treated as tensors of size :math:`(1,)` automatically 2025-07-17T09:05:49.4519678Z 2025-07-17T09:05:49.4519789Z indexing: (str, optional): the indexing mode, either "xy" 2025-07-17T09:05:49.4520067Z or "ij", defaults to "ij". See warning for future changes. 2025-07-17T09:05:49.4520245Z 2025-07-17T09:05:49.4520347Z If "xy" is selected, the first dimension corresponds 2025-07-17T09:05:49.4520631Z to the cardinality of the second input and the second 2025-07-17T09:05:49.4520898Z dimension corresponds to the cardinality of the first 2025-07-17T09:05:49.4521132Z input. 2025-07-17T09:05:49.4521240Z 2025-07-17T09:05:49.4521338Z If "ij" is selected, the dimensions are in the same 2025-07-17T09:05:49.4521586Z order as the cardinality of the inputs. 2025-07-17T09:05:49.4521739Z 2025-07-17T09:05:49.4521808Z Returns: 2025-07-17T09:05:49.4522020Z seq (sequence of Tensors): If the input has :math:`N` 2025-07-17T09:05:49.4522286Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2025-07-17T09:05:49.4522567Z output will also have :math:`N` tensors, where each tensor 2025-07-17T09:05:49.4522825Z is of shape :math:`(S_0, ..., S_{N-1})`. 2025-07-17T09:05:49.4522967Z 2025-07-17T09:05:49.4523041Z Example:: 2025-07-17T09:05:49.4523131Z 2025-07-17T09:05:49.4523223Z >>> x = torch.tensor([1, 2, 3]) 2025-07-17T09:05:49.4523430Z >>> y = torch.tensor([4, 5, 6]) 2025-07-17T09:05:49.4523553Z 2025-07-17T09:05:49.4523672Z Observe the element-wise pairings across the grid, (1, 4), 2025-07-17T09:05:49.4523946Z (1, 5), ..., (3, 6). This is the same thing as the 2025-07-17T09:05:49.4524170Z cartesian product. 2025-07-17T09:05:49.4524398Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2025-07-17T09:05:49.4524628Z >>> grid_x 2025-07-17T09:05:49.4524803Z tensor([[1, 1, 1], 2025-07-17T09:05:49.4524978Z [2, 2, 2], 2025-07-17T09:05:49.4525151Z [3, 3, 3]]) 2025-07-17T09:05:49.4525338Z >>> grid_y 2025-07-17T09:05:49.4525517Z tensor([[4, 5, 6], 2025-07-17T09:05:49.4525735Z [4, 5, 6], 2025-07-17T09:05:49.4525897Z [4, 5, 6]]) 2025-07-17T09:05:49.4526020Z 2025-07-17T09:05:49.4526262Z This correspondence can be seen when these grids are 2025-07-17T09:05:49.4526520Z stacked properly. 2025-07-17T09:05:49.4526762Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2025-07-17T09:05:49.4527042Z ... torch.cartesian_prod(x, y)) 2025-07-17T09:05:49.4527267Z True 2025-07-17T09:05:49.4527363Z 2025-07-17T09:05:49.4527589Z `torch.meshgrid` is commonly used to produce a grid for 2025-07-17T09:05:49.4527847Z plotting. 2025-07-17T09:05:49.4528054Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2025-07-17T09:05:49.4528295Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2025-07-17T09:05:49.4528530Z >>> import matplotlib.pyplot as plt 2025-07-17T09:05:49.4528763Z >>> xs = torch.linspace(-5, 5, steps=100) 2025-07-17T09:05:49.4528989Z >>> ys = torch.linspace(-5, 5, steps=100) 2025-07-17T09:05:49.4529208Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2025-07-17T09:05:49.4529450Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2025-07-17T09:05:49.4529689Z >>> ax = plt.axes(projection='3d') 2025-07-17T09:05:49.4529935Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2025-07-17T09:05:49.4530170Z >>> plt.show() 2025-07-17T09:05:49.4530285Z 2025-07-17T09:05:49.4530376Z .. image:: ../_static/img/meshgrid.png 2025-07-17T09:05:49.4530590Z :width: 512 2025-07-17T09:05:49.4530702Z 2025-07-17T09:05:49.4530763Z 2025-07-17T09:05:49.4531008Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:49.4531220Z 2025-07-17T09:05:49.4531548Z msg = Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=842. 2025-07-17T09:05:49.4532036Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:49.4532483Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> tuple[Tensor, Tensor, Tensor] 2025-07-17T09:05:49.4532775Z 2025-07-17T09:05:49.4532873Z Returns the unique elements of the input tensor. 2025-07-17T09:05:49.4533039Z 2025-07-17T09:05:49.4533218Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2025-07-17T09:05:49.4533587Z this function also eliminates non-consecutive duplicate values. 2025-07-17T09:05:49.4533788Z 2025-07-17T09:05:49.4533924Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2025-07-17T09:05:49.4534297Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2025-07-17T09:05:49.4534703Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2025-07-17T09:05:49.4535064Z :func:`torch.unique_consecutive` which avoids the sorting. 2025-07-17T09:05:49.4535238Z 2025-07-17T09:05:49.4535300Z Args: 2025-07-17T09:05:49.4535478Z input (Tensor): the input tensor 2025-07-17T09:05:49.4535750Z sorted (bool): Whether to sort the unique elements in ascending order 2025-07-17T09:05:49.4536025Z before returning as output. 2025-07-17T09:05:49.4536284Z return_inverse (bool): Whether to also return the indices for where 2025-07-17T09:05:49.4536614Z elements in the original input ended up in the returned unique list. 2025-07-17T09:05:49.4536945Z return_counts (bool): Whether to also return the counts for each unique 2025-07-17T09:05:49.4537204Z element. 2025-07-17T09:05:49.4537421Z dim (int, optional): the dimension to operate upon. If ``None``, the 2025-07-17T09:05:49.4537736Z unique of the flattened input is returned. Otherwise, each of the 2025-07-17T09:05:49.4538050Z tensors indexed by the given dimension is treated as one of the 2025-07-17T09:05:49.4538365Z elements to apply the unique operation upon. See examples for more 2025-07-17T09:05:49.4538834Z details. Default: ``None`` 2025-07-17T09:05:49.4538970Z 2025-07-17T09:05:49.4539034Z Returns: 2025-07-17T09:05:49.4539293Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2025-07-17T09:05:49.4539524Z 2025-07-17T09:05:49.4539645Z - **output** (*Tensor*): the output list of unique scalar elements. 2025-07-17T09:05:49.4540029Z - **inverse_indices** (*Tensor*): (optional) if 2025-07-17T09:05:49.4540306Z :attr:`return_inverse` is True, there will be an additional 2025-07-17T09:05:49.4540607Z returned tensor (same shape as input) representing the indices 2025-07-17T09:05:49.4540915Z for where elements in the original input map to in the output; 2025-07-17T09:05:49.4541214Z otherwise, this function will only return a single tensor. 2025-07-17T09:05:49.4541475Z - **counts** (*Tensor*): (optional) if 2025-07-17T09:05:49.4541735Z :attr:`return_counts` is True, there will be an additional 2025-07-17T09:05:49.4542012Z returned tensor (same shape as output or output.size(dim), 2025-07-17T09:05:49.4542305Z if dim was specified) representing the number of occurrences 2025-07-17T09:05:49.4542564Z for each unique value or tensor. 2025-07-17T09:05:49.4542708Z 2025-07-17T09:05:49.4542778Z Example:: 2025-07-17T09:05:49.4542873Z 2025-07-17T09:05:49.4543010Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2025-07-17T09:05:49.4543259Z >>> output 2025-07-17T09:05:49.4543427Z tensor([1, 2, 3]) 2025-07-17T09:05:49.4543540Z 2025-07-17T09:05:49.4543626Z >>> output, inverse_indices = torch.unique( 2025-07-17T09:05:49.4543928Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2025-07-17T09:05:49.4544199Z >>> output 2025-07-17T09:05:49.4544357Z tensor([1, 2, 3]) 2025-07-17T09:05:49.4544537Z >>> inverse_indices 2025-07-17T09:05:49.4544718Z tensor([0, 2, 1, 2]) 2025-07-17T09:05:49.4544828Z 2025-07-17T09:05:49.4544918Z >>> output, inverse_indices = torch.unique( 2025-07-17T09:05:49.4545201Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2025-07-17T09:05:49.4545588Z >>> output 2025-07-17T09:05:49.4545755Z tensor([1, 2, 3]) 2025-07-17T09:05:49.4545939Z >>> inverse_indices 2025-07-17T09:05:49.4546122Z tensor([[0, 2], 2025-07-17T09:05:49.4546291Z [1, 2]]) 2025-07-17T09:05:49.4546395Z 2025-07-17T09:05:49.4546477Z >>> a = torch.tensor([ 2025-07-17T09:05:49.4546655Z ... [ 2025-07-17T09:05:49.4546825Z ... [1, 1, 0, 0], 2025-07-17T09:05:49.4547017Z ... [1, 1, 0, 0], 2025-07-17T09:05:49.4547193Z ... [0, 0, 1, 1], 2025-07-17T09:05:49.4547358Z ... ], 2025-07-17T09:05:49.4547521Z ... [ 2025-07-17T09:05:49.4547670Z ... [0, 0, 1, 1], 2025-07-17T09:05:49.4547851Z ... [0, 0, 1, 1], 2025-07-17T09:05:49.4548023Z ... [1, 1, 1, 1], 2025-07-17T09:05:49.4548196Z ... ], 2025-07-17T09:05:49.4548344Z ... [ 2025-07-17T09:05:49.4548496Z ... [1, 1, 0, 0], 2025-07-17T09:05:49.4548667Z ... [1, 1, 0, 0], 2025-07-17T09:05:49.4548845Z ... [0, 0, 1, 1], 2025-07-17T09:05:49.4549012Z ... ], 2025-07-17T09:05:49.4549160Z ... ]) 2025-07-17T09:05:49.4549254Z 2025-07-17T09:05:49.4549388Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2025-07-17T09:05:49.4549719Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2025-07-17T09:05:49.4550000Z >>> # each other, so one of them will be removed. 2025-07-17T09:05:49.4550228Z >>> (a[0, :, :] == a[2, :, :]).all() 2025-07-17T09:05:49.4550580Z tensor(True) 2025-07-17T09:05:49.4550770Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2025-07-17T09:05:49.4550979Z >>> a_unique_dim0 2025-07-17T09:05:49.4551151Z tensor([[[0, 0, 1, 1], 2025-07-17T09:05:49.4551330Z [0, 0, 1, 1], 2025-07-17T09:05:49.4551504Z [1, 1, 1, 1]], 2025-07-17T09:05:49.4551674Z [[1, 1, 0, 0], 2025-07-17T09:05:49.4551852Z [1, 1, 0, 0], 2025-07-17T09:05:49.4552159Z [0, 0, 1, 1]]]) 2025-07-17T09:05:49.4552282Z 2025-07-17T09:05:49.4552412Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2025-07-17T09:05:49.4552679Z >>> # `a_unique_dim0`: 2025-07-17T09:05:49.4552875Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2025-07-17T09:05:49.4553087Z tensor(True) 2025-07-17T09:05:49.4553264Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2025-07-17T09:05:49.4553464Z tensor(True) 2025-07-17T09:05:49.4553560Z 2025-07-17T09:05:49.4553692Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2025-07-17T09:05:49.4553998Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2025-07-17T09:05:49.4554250Z >>> # them will be removed. 2025-07-17T09:05:49.4554455Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2025-07-17T09:05:49.4554636Z tensor(True) 2025-07-17T09:05:49.4554806Z >>> torch.unique(a, dim=1) 2025-07-17T09:05:49.4555012Z tensor([[[0, 0, 1, 1], 2025-07-17T09:05:49.4555192Z [1, 1, 0, 0]], 2025-07-17T09:05:49.4555370Z [[1, 1, 1, 1], 2025-07-17T09:05:49.4555548Z [0, 0, 1, 1]], 2025-07-17T09:05:49.4555717Z [[0, 0, 1, 1], 2025-07-17T09:05:49.4555888Z [1, 1, 0, 0]]]) 2025-07-17T09:05:49.4556000Z 2025-07-17T09:05:49.4556140Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2025-07-17T09:05:49.4556439Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2025-07-17T09:05:49.4556720Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2025-07-17T09:05:49.4556970Z >>> # sub-tensors will be removed. 2025-07-17T09:05:49.4557178Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2025-07-17T09:05:49.4557368Z tensor(True) 2025-07-17T09:05:49.4557545Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2025-07-17T09:05:49.4557742Z tensor(True) 2025-07-17T09:05:49.4557911Z >>> torch.unique(a, dim=2) 2025-07-17T09:05:49.4558099Z tensor([[[0, 1], 2025-07-17T09:05:49.4558268Z [0, 1], 2025-07-17T09:05:49.4558433Z [1, 0]], 2025-07-17T09:05:49.4558604Z [[1, 0], 2025-07-17T09:05:49.4558761Z [1, 0], 2025-07-17T09:05:49.4558913Z [1, 1]], 2025-07-17T09:05:49.4559070Z [[0, 1], 2025-07-17T09:05:49.4559618Z [0, 1], 2025-07-17T09:05:49.4559928Z [1, 0]]]) 2025-07-17T09:05:49.4568752Z 2025-07-17T09:05:49.4569041Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:49.4569287Z 2025-07-17T09:05:49.4786505Z msg = Cannot scrape callname=Library.fallback in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=374. 2025-07-17T09:05:49.4787078Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-07-17T09:05:49.4787494Z Registers the function implementation as the fallback for the given key. 2025-07-17T09:05:49.4787704Z 2025-07-17T09:05:49.4787862Z This function only works for a library with global namespace ("_"). 2025-07-17T09:05:49.4788066Z 2025-07-17T09:05:49.4788144Z Args: 2025-07-17T09:05:49.4788416Z fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel` 2025-07-17T09:05:49.4788740Z to register a fallthrough. 2025-07-17T09:05:49.4789088Z dispatch_key: dispatch key that the input function should be registered for. By default, it uses 2025-07-17T09:05:49.4789797Z the dispatch key that the library was created with. 2025-07-17T09:05:49.4790195Z with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument 2025-07-17T09:05:49.4790829Z to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls. 2025-07-17T09:05:49.4791082Z 2025-07-17T09:05:49.4791175Z Example:: 2025-07-17T09:05:49.4791273Z 2025-07-17T09:05:49.4791355Z >>> my_lib = Library("_", "IMPL") 2025-07-17T09:05:49.4791593Z >>> def fallback_kernel(op, *args, **kwargs): 2025-07-17T09:05:49.4791836Z >>> # Handle all autocast ops generically 2025-07-17T09:05:49.4792054Z >>> # ... 2025-07-17T09:05:49.4792264Z >>> my_lib.fallback(fallback_kernel, "Autocast") 2025-07-17T09:05:49.4792506Z 2025-07-17T09:05:49.4792964Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7)) 2025-07-17T09:05:49.4793394Z 2025-07-17T09:05:49.4793487Z my_lib.fallback(fallback_kernel, "Autocast") 2025-07-17T09:05:49.4793703Z ^ 2025-07-17T09:05:49.4843811Z msg = Cannot scrape callname=register_fake in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=933. 2025-07-17T09:05:49.4844368Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-07-17T09:05:49.4844755Z Register a FakeTensor implementation ("fake impl") for this operator. 2025-07-17T09:05:49.4844971Z 2025-07-17T09:05:49.4845108Z Also sometimes known as a "meta kernel", "abstract impl". 2025-07-17T09:05:49.4845283Z 2025-07-17T09:05:49.4845452Z An "FakeTensor implementation" specifies the behavior of this operator on 2025-07-17T09:05:49.4845816Z Tensors that carry no data ("FakeTensor"). Given some input Tensors with 2025-07-17T09:05:49.4846178Z certain properties (sizes/strides/storage_offset/device), it specifies 2025-07-17T09:05:49.4846491Z what the properties of the output Tensors are. 2025-07-17T09:05:49.4846645Z 2025-07-17T09:05:49.4846794Z The FakeTensor implementation has the same signature as the operator. 2025-07-17T09:05:49.4847149Z It is run for both FakeTensors and meta tensors. To write a FakeTensor 2025-07-17T09:05:49.4847488Z implementation, assume that all Tensor inputs to the operator are 2025-07-17T09:05:49.4847811Z regular CPU/CUDA/Meta tensors, but they do not have storage, and 2025-07-17T09:05:49.4848128Z you are trying to return regular CPU/CUDA/Meta tensor(s) as output. 2025-07-17T09:05:49.4848460Z The FakeTensor implementation must consist of only PyTorch operations 2025-07-17T09:05:49.4848785Z (and may not directly access the storage or data of any input or 2025-07-17T09:05:49.4849046Z intermediate Tensors). 2025-07-17T09:05:49.4849175Z 2025-07-17T09:05:49.4849279Z This API may be used as a decorator (see examples). 2025-07-17T09:05:49.4849447Z 2025-07-17T09:05:49.4849542Z For a detailed guide on custom ops, please see 2025-07-17T09:05:49.4849854Z https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html 2025-07-17T09:05:49.4850069Z 2025-07-17T09:05:49.4850131Z Args: 2025-07-17T09:05:49.4850377Z op_name: Operator name (along with the overload) or OpOverload object. 2025-07-17T09:05:49.4850656Z func: Fake tensor implementation. 2025-07-17T09:05:49.4850917Z lib (Optional[Library]): Library to register the fake tensor to. 2025-07-17T09:05:49.4851246Z allow_override: Flag controlling if we want to override an 2025-07-17T09:05:49.4851546Z existing registered fake impl. This is by default off, 2025-07-17T09:05:49.4851833Z and will error you're trying to register a fake impl to 2025-07-17T09:05:49.4852338Z an operator that already has a fake impl. This also only 2025-07-17T09:05:49.4852612Z applies if the custom operator was not created via 2025-07-17T09:05:49.4852897Z torch.library.custom_op, as overriding and existing fake 2025-07-17T09:05:49.4853160Z impl is already allowed. 2025-07-17T09:05:49.4853303Z 2025-07-17T09:05:49.4853370Z Examples: 2025-07-17T09:05:49.4853678Z >>> import torch 2025-07-17T09:05:49.4853870Z >>> import numpy as np 2025-07-17T09:05:49.4854102Z >>> from torch import Tensor 2025-07-17T09:05:49.4854295Z >>> 2025-07-17T09:05:49.4854507Z >>> # Example 1: an operator without data-dependent output shape 2025-07-17T09:05:49.4854831Z >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=()) 2025-07-17T09:05:49.4855164Z >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2025-07-17T09:05:49.4855504Z >>> raise NotImplementedError("Implementation goes here") 2025-07-17T09:05:49.4855750Z >>> 2025-07-17T09:05:49.4855954Z >>> @torch.library.register_fake("mylib::custom_linear") 2025-07-17T09:05:49.4856204Z >>> def _(x, weight, bias): 2025-07-17T09:05:49.4856412Z >>> assert x.dim() == 2 2025-07-17T09:05:49.4856622Z >>> assert weight.dim() == 2 2025-07-17T09:05:49.4856849Z >>> assert bias.dim() == 1 2025-07-17T09:05:49.4857085Z >>> assert x.shape[1] == weight.shape[1] 2025-07-17T09:05:49.4857317Z >>> assert weight.shape[0] == bias.shape[0] 2025-07-17T09:05:49.4857553Z >>> assert x.device == weight.device 2025-07-17T09:05:49.4857761Z >>> 2025-07-17T09:05:49.4857937Z >>> return (x @ weight.t()) + bias 2025-07-17T09:05:49.4858143Z >>> 2025-07-17T09:05:49.4858349Z >>> with torch._subclasses.fake_tensor.FakeTensorMode(): 2025-07-17T09:05:49.4858607Z >>> x = torch.randn(2, 3) 2025-07-17T09:05:49.4858825Z >>> w = torch.randn(3, 3) 2025-07-17T09:05:49.4859025Z >>> b = torch.randn(3) 2025-07-17T09:05:49.4859246Z >>> y = torch.ops.mylib.custom_linear(x, w, b) 2025-07-17T09:05:49.4859471Z >>> 2025-07-17T09:05:49.4859627Z >>> assert y.shape == (2, 3) 2025-07-17T09:05:49.4859832Z >>> 2025-07-17T09:05:49.4860029Z >>> # Example 2: an operator with data-dependent output shape 2025-07-17T09:05:49.4860344Z >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=()) 2025-07-17T09:05:49.4860641Z >>> def custom_nonzero(x: Tensor) -> Tensor: 2025-07-17T09:05:49.4860871Z >>> x_np = x.numpy(force=True) 2025-07-17T09:05:49.4861108Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2025-07-17T09:05:49.4861357Z >>> return torch.tensor(res, device=x.device) 2025-07-17T09:05:49.4861564Z >>> 2025-07-17T09:05:49.4861769Z >>> @torch.library.register_fake("mylib::custom_nonzero") 2025-07-17T09:05:49.4862015Z >>> def _(x): 2025-07-17T09:05:49.4862225Z >>> # Number of nonzero-elements is data-dependent. 2025-07-17T09:05:49.4862482Z >>> # Since we cannot peek at the data in an fake impl, 2025-07-17T09:05:49.4862757Z >>> # we use the ctx object to construct a new symint that 2025-07-17T09:05:49.4863015Z >>> # represents the data-dependent size. 2025-07-17T09:05:49.4863258Z >>> ctx = torch.library.get_ctx() 2025-07-17T09:05:49.4863483Z >>> nnz = ctx.new_dynamic_size() 2025-07-17T09:05:49.4863703Z >>> shape = [nnz, x.dim()] 2025-07-17T09:05:49.4863939Z >>> result = x.new_empty(shape, dtype=torch.int64) 2025-07-17T09:05:49.4864175Z >>> return result 2025-07-17T09:05:49.4864362Z >>> 2025-07-17T09:05:49.4864575Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2025-07-17T09:05:49.4864819Z >>> 2025-07-17T09:05:49.4865121Z >>> x = torch.tensor([0, 1, 2, 3, 4, 0]) 2025-07-17T09:05:49.4865512Z >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x) 2025-07-17T09:05:49.4865819Z >>> trace.print_readable() 2025-07-17T09:05:49.4866010Z >>> 2025-07-17T09:05:49.4866231Z >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x)) 2025-07-17T09:05:49.4866443Z 2025-07-17T09:05:49.4866506Z 2025-07-17T09:05:49.4867029Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('', 38, 1, '_._ = None\n', 38, 2)) 2025-07-17T09:05:49.4867402Z 2025-07-17T09:05:49.4867465Z _._ = None 2025-07-17T09:05:49.4867617Z ^ 2025-07-17T09:05:49.4867988Z msg = Cannot scrape callname=register_autograd in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=1068. 2025-07-17T09:05:49.4868494Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:49.4868827Z Register a backward formula for this custom op. 2025-07-17T09:05:49.4868978Z 2025-07-17T09:05:49.4869118Z In order for an operator to work with autograd, you need to register 2025-07-17T09:05:49.4869387Z a backward formula: 2025-07-17T09:05:49.4869636Z 1. You must tell us how to compute gradients during the backward pass 2025-07-17T09:05:49.4869918Z by providing us a "backward" function. 2025-07-17T09:05:49.4870189Z 2. If you need any values from the forward to compute gradients, you can 2025-07-17T09:05:49.4870483Z use `setup_context` to save values for backward. 2025-07-17T09:05:49.4870642Z 2025-07-17T09:05:49.4870784Z ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``: 2025-07-17T09:05:49.4871107Z - ``grads`` is one or more gradients. The number of gradients matches 2025-07-17T09:05:49.4871375Z the number of outputs of the operator. 2025-07-17T09:05:49.4871650Z The ``ctx`` object is `the same ctx object `_ used by 2025-07-17T09:05:49.4871997Z :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the 2025-07-17T09:05:49.4872315Z same as :meth:`torch.autograd.Function.backward`. 2025-07-17T09:05:49.4872475Z 2025-07-17T09:05:49.4872619Z ``setup_context(ctx, inputs, output)`` runs during the forward pass. 2025-07-17T09:05:49.4872951Z Please save quantities needed for backward onto the ``ctx`` object via 2025-07-17T09:05:49.4873309Z either :meth:`torch.autograd.function.FunctionCtx.save_for_backward` 2025-07-17T09:05:49.4873642Z or assigning them as attributes of ``ctx``. If your custom op has 2025-07-17T09:05:49.4873962Z kwarg-only arguments, we expect the signature of ``setup_context`` 2025-07-17T09:05:49.4874277Z to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``. 2025-07-17T09:05:49.4874452Z 2025-07-17T09:05:49.4874592Z Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is, 2025-07-17T09:05:49.4874921Z they may not directly access :meth:`torch.Tensor.data_ptr` and they must 2025-07-17T09:05:49.4875254Z not depend on or mutate global state. If you need a non-traceable backward, 2025-07-17T09:05:49.4875612Z you can make it a separate custom_op that you call inside ``backward_fn``. 2025-07-17T09:05:49.4875811Z 2025-07-17T09:05:49.4875954Z If you need different autograd behavior on different devices, then we 2025-07-17T09:05:49.4876284Z recommend creating two different custom operators, one for each device 2025-07-17T09:05:49.4876616Z that needs different behavior, and switching between them at runtime. 2025-07-17T09:05:49.4876824Z 2025-07-17T09:05:49.4876891Z Examples: 2025-07-17T09:05:49.4877058Z >>> import torch 2025-07-17T09:05:49.4877240Z >>> import numpy as np 2025-07-17T09:05:49.4877441Z >>> from torch import Tensor 2025-07-17T09:05:49.4877632Z >>> 2025-07-17T09:05:49.4877849Z >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=()) 2025-07-17T09:05:49.4878259Z >>> def numpy_sin(x: Tensor) -> Tensor: 2025-07-17T09:05:49.4878471Z >>> x_np = x.cpu().numpy() 2025-07-17T09:05:49.4878670Z >>> y_np = np.sin(x_np) 2025-07-17T09:05:49.4878902Z >>> return torch.from_numpy(y_np).to(device=x.device) 2025-07-17T09:05:49.4879114Z >>> 2025-07-17T09:05:49.4879303Z >>> def setup_context(ctx, inputs, output) -> Tensor: 2025-07-17T09:05:49.4879534Z >>> x, = inputs 2025-07-17T09:05:49.4879848Z >>> ctx.save_for_backward(x) 2025-07-17T09:05:49.4880048Z >>> 2025-07-17T09:05:49.4880204Z >>> def backward(ctx, grad): 2025-07-17T09:05:49.4880405Z >>> x, = ctx.saved_tensors 2025-07-17T09:05:49.4880602Z >>> return grad * x.cos() 2025-07-17T09:05:49.4880786Z >>> 2025-07-17T09:05:49.4880947Z >>> torch.library.register_autograd( 2025-07-17T09:05:49.4881203Z ... "mylib::numpy_sin", backward, setup_context=setup_context 2025-07-17T09:05:49.4881435Z ... ) 2025-07-17T09:05:49.4881584Z >>> 2025-07-17T09:05:49.4881750Z >>> x = torch.randn(3, requires_grad=True) 2025-07-17T09:05:49.4881966Z >>> y = numpy_sin(x) 2025-07-17T09:05:49.4882192Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2025-07-17T09:05:49.4882453Z >>> assert torch.allclose(grad_x, x.cos()) 2025-07-17T09:05:49.4882653Z >>> 2025-07-17T09:05:49.4882830Z >>> # Example with a keyword-only arg 2025-07-17T09:05:49.4883084Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2025-07-17T09:05:49.4883356Z >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor: 2025-07-17T09:05:49.4883577Z >>> x_np = x.cpu().numpy() 2025-07-17T09:05:49.4883766Z >>> y_np = x_np * val 2025-07-17T09:05:49.4883972Z >>> return torch.from_numpy(y_np).to(device=x.device) 2025-07-17T09:05:49.4884174Z >>> 2025-07-17T09:05:49.4884385Z >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor: 2025-07-17T09:05:49.4884679Z >>> ctx.val = keyword_only_inputs["val"] 2025-07-17T09:05:49.4884876Z >>> 2025-07-17T09:05:49.4885019Z >>> def backward(ctx, grad): 2025-07-17T09:05:49.4885209Z >>> return grad * ctx.val 2025-07-17T09:05:49.4885396Z >>> 2025-07-17T09:05:49.4885551Z >>> torch.library.register_autograd( 2025-07-17T09:05:49.4885818Z ... "mylib::numpy_mul", backward, setup_context=setup_context 2025-07-17T09:05:49.4886064Z ... ) 2025-07-17T09:05:49.4886204Z >>> 2025-07-17T09:05:49.4886371Z >>> x = torch.randn(3, requires_grad=True) 2025-07-17T09:05:49.4886589Z >>> y = numpy_mul(x, val=3.14) 2025-07-17T09:05:49.4886831Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2025-07-17T09:05:49.4887109Z >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14)) 2025-07-17T09:05:49.4887275Z 2025-07-17T09:05:49.4887338Z 2025-07-17T09:05:49.4887568Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:49.4887784Z 2025-07-17T09:05:49.4888091Z msg = Cannot scrape callname=opcheck in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=1482. 2025-07-17T09:05:49.4888560Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:49.4888913Z Given an operator and some sample arguments, tests if the operator is 2025-07-17T09:05:49.4889178Z registered correctly. 2025-07-17T09:05:49.4889290Z 2025-07-17T09:05:49.4889418Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2025-07-17T09:05:49.4889757Z custom op, you specified metadata (e.g. mutability info) about the custom op 2025-07-17T09:05:49.4890095Z and these APIs require that the functions you pass them satisfy certain 2025-07-17T09:05:49.4890424Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2025-07-17T09:05:49.4893022Z ``opcheck`` tests these metadata and properties. 2025-07-17T09:05:49.4893184Z 2025-07-17T09:05:49.4893261Z Concretely, we test the following: 2025-07-17T09:05:49.4893398Z 2025-07-17T09:05:49.4893509Z - test_schema: If the schema matches the implementation of 2025-07-17T09:05:49.4893812Z the operator. For example: if the schema specifies a Tensor is mutated, 2025-07-17T09:05:49.4894244Z then we check the implementation mutates the Tensor. If the schema 2025-07-17T09:05:49.4894555Z specifies that we return a new Tensor, then we check that the 2025-07-17T09:05:49.4894859Z implementation returns a new Tensor (instead of an existing one or 2025-07-17T09:05:49.4895130Z a view of an existing one). 2025-07-17T09:05:49.4895380Z - test_autograd_registration: If the operator supports training 2025-07-17T09:05:49.4895674Z (autograd): we check that its autograd formula is registered via 2025-07-17T09:05:49.4895975Z torch.library.register_autograd or a manual registration to one 2025-07-17T09:05:49.4896291Z or more DispatchKey::Autograd keys. Any other DispatchKey-based 2025-07-17T09:05:49.4896550Z registrations may lead to undefined behavior. 2025-07-17T09:05:49.4896803Z - test_faketensor: If the operator has a FakeTensor kernel 2025-07-17T09:05:49.4897092Z (and if it is correct). The FakeTensor kernel is necessary ( 2025-07-17T09:05:49.4897401Z but not sufficient) for the operator to work with PyTorch compilation 2025-07-17T09:05:49.4897730Z APIs (torch.compile/export/FX). We check that a FakeTensor kernel 2025-07-17T09:05:49.4898039Z (also sometimes known as a meta kernel) was registered for the 2025-07-17T09:05:49.4898326Z operator and that it is correct. This test takes the result of 2025-07-17T09:05:49.4898615Z running the operator on real tensors and the result of running 2025-07-17T09:05:49.4898907Z the operator on FakeTensors and checks that they have the same 2025-07-17T09:05:49.4899178Z Tensor metadata (sizes/strides/dtype/device/etc). 2025-07-17T09:05:49.4899466Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2025-07-17T09:05:49.4899766Z with PyTorch compilation APIs (torch.compile/export/FX). 2025-07-17T09:05:49.4900056Z This checks that the outputs (and gradients, if applicable) are the 2025-07-17T09:05:49.4900342Z same under eager-mode PyTorch and torch.compile. 2025-07-17T09:05:49.4900611Z This test is a superset of ``test_faketensor`` and is an e2e test; 2025-07-17T09:05:49.4900894Z other things it tests are that the operator supports 2025-07-17T09:05:49.4901183Z functionalization and that the backward pass (if it exists) also 2025-07-17T09:05:49.4901456Z supports FakeTensor and functionalization. 2025-07-17T09:05:49.4901605Z 2025-07-17T09:05:49.4901729Z For best results, please call ``opcheck`` multiple times with a 2025-07-17T09:05:49.4902023Z representative set of inputs. If your operator supports 2025-07-17T09:05:49.4902341Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2025-07-17T09:05:49.4902687Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2025-07-17T09:05:49.4902979Z use ``opcheck`` with inputs on all supported devices. 2025-07-17T09:05:49.4903134Z 2025-07-17T09:05:49.4903204Z Args: 2025-07-17T09:05:49.4903401Z op: The operator. Must either be a function decorated with 2025-07-17T09:05:49.4903706Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2025-07-17T09:05:49.4904026Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2025-07-17T09:05:49.4904289Z args: The args to the operator 2025-07-17T09:05:49.4904496Z kwargs: The kwargs to the operator 2025-07-17T09:05:49.4904734Z test_utils: Tests that we should run. Default: all of them. 2025-07-17T09:05:49.4904989Z Example: ("test_schema", "test_faketensor") 2025-07-17T09:05:49.4905249Z raise_exception: If we should raise an exception on the first 2025-07-17T09:05:49.4905746Z error. If False, we will return a dict with information 2025-07-17T09:05:49.4905979Z on if each test passed or not. 2025-07-17T09:05:49.4906250Z rtol (Optional[float]): Relative tolerance for floating point comparisons. 2025-07-17T09:05:49.4906556Z If specified ``atol`` must also be specified. 2025-07-17T09:05:49.4906949Z If omitted, default values based on the ``dtype`` are selected 2025-07-17T09:05:49.4907238Z (see the table in :func:`torch.testing.assert_close`). 2025-07-17T09:05:49.4907534Z atol (Optional[float]): Absolute tolerance for floating point comparisons. 2025-07-17T09:05:49.4907814Z If specified ``rtol`` must also be specified. 2025-07-17T09:05:49.4908063Z If omitted, default values based on the ``dtype`` are selected 2025-07-17T09:05:49.4908328Z (see the table in :func:`torch.testing.assert_close`). 2025-07-17T09:05:49.4908491Z 2025-07-17T09:05:49.4908569Z .. warning:: 2025-07-17T09:05:49.4908659Z 2025-07-17T09:05:49.4908797Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2025-07-17T09:05:49.4909108Z opcheck tests if your usage of torch.library APIs is correct while 2025-07-17T09:05:49.4909413Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2025-07-17T09:05:49.4909738Z mathematically correct. Use both to test custom ops that support 2025-07-17T09:05:49.4910005Z gradient computation. 2025-07-17T09:05:49.4910129Z 2025-07-17T09:05:49.4910188Z Example: 2025-07-17T09:05:49.4910300Z 2025-07-17T09:05:49.4910383Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-07-17T09:05:49.4910647Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2025-07-17T09:05:49.4910924Z >>> def numpy_mul(x: Tensor, y: float) -> Tensor: 2025-07-17T09:05:49.4911147Z >>> x_np = x.numpy(force=True) 2025-07-17T09:05:49.4911341Z >>> z_np = x_np * y 2025-07-17T09:05:49.4911545Z >>> return torch.from_numpy(z_np).to(x.device) 2025-07-17T09:05:49.4911755Z >>> 2025-07-17T09:05:49.4911905Z >>> @numpy_mul.register_fake 2025-07-17T09:05:49.4912089Z >>> def _(x, y): 2025-07-17T09:05:49.4912267Z >>> return torch.empty_like(x) 2025-07-17T09:05:49.4912454Z >>> 2025-07-17T09:05:49.4912625Z >>> def setup_context(ctx, inputs, output): 2025-07-17T09:05:49.4912832Z >>> y, = inputs 2025-07-17T09:05:49.4913007Z >>> ctx.y = y 2025-07-17T09:05:49.4913161Z >>> 2025-07-17T09:05:49.4913307Z >>> def backward(ctx, grad): 2025-07-17T09:05:49.4913495Z >>> return grad * ctx.y, None 2025-07-17T09:05:49.4913672Z >>> 2025-07-17T09:05:49.4913880Z >>> numpy_mul.register_autograd(backward, setup_context=setup_context) 2025-07-17T09:05:49.4914125Z >>> 2025-07-17T09:05:49.4914274Z >>> sample_inputs = [ 2025-07-17T09:05:49.4914454Z >>> (torch.randn(3), 3.14), 2025-07-17T09:05:49.4914661Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2025-07-17T09:05:49.4914888Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2025-07-17T09:05:49.4915156Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2025-07-17T09:05:49.4915397Z >>> ] 2025-07-17T09:05:49.4915537Z >>> 2025-07-17T09:05:49.4915686Z >>> for args in sample_inputs: 2025-07-17T09:05:49.4915897Z >>> torch.library.opcheck(numpy_mul, args) 2025-07-17T09:05:49.4916037Z 2025-07-17T09:05:49.4916094Z 2025-07-17T09:05:49.4916309Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:49.4916520Z 2025-07-17T09:05:49.5788286Z msg = Cannot scrape callname=Tensor.dim_order in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py line=1493. 2025-07-17T09:05:49.5789555Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:49.5789789Z 2025-07-17T09:05:49.5789898Z dim_order(ambiguity_check=False) -> tuple 2025-07-17T09:05:49.5790041Z 2025-07-17T09:05:49.5790202Z Returns the uniquely determined tuple of int describing the dim order or 2025-07-17T09:05:49.5790489Z physical layout of :attr:`self`. 2025-07-17T09:05:49.5790616Z 2025-07-17T09:05:49.5790992Z The dim order represents how dimensions are laid out in memory of dense tensors, 2025-07-17T09:05:49.5791329Z starting from the outermost to the innermost dimension. 2025-07-17T09:05:49.5791535Z 2025-07-17T09:05:49.5791677Z Note that the dim order may not always be uniquely determined. 2025-07-17T09:05:49.5792098Z If `ambiguity_check` is True, this function raises a RuntimeError when the dim order cannot be uniquely determined; 2025-07-17T09:05:49.5792621Z If `ambiguity_check` is a list of memory formats, this function raises a RuntimeError when tensor can not be interpreted 2025-07-17T09:05:49.5793078Z into exactly one of the given memory formats, or it cannot be uniquely determined. 2025-07-17T09:05:49.5793480Z If `ambiguity_check` is False, it will return one of legal dim order(s) without checking its uniqueness. 2025-07-17T09:05:49.5793819Z Otherwise, it will raise TypeError. 2025-07-17T09:05:49.5793967Z 2025-07-17T09:05:49.5794028Z Args: 2025-07-17T09:05:49.5794318Z ambiguity_check (bool or List[torch.memory_format]): The check method for ambiguity of dim order. 2025-07-17T09:05:49.5794586Z 2025-07-17T09:05:49.5794674Z Examples:: 2025-07-17T09:05:49.5794770Z 2025-07-17T09:05:49.5794852Z >>> torch.empty((2, 3, 5, 7)).dim_order() 2025-07-17T09:05:49.5795061Z (0, 1, 2, 3) 2025-07-17T09:05:49.5795267Z >>> torch.empty((2, 3, 5, 7)).transpose(1, 2).dim_order() 2025-07-17T09:05:49.5795489Z (0, 2, 1, 3) 2025-07-17T09:05:49.5795712Z >>> torch.empty((2, 3, 5, 7), memory_format=torch.channels_last).dim_order() 2025-07-17T09:05:49.5795963Z (0, 2, 3, 1) 2025-07-17T09:05:49.5796139Z >>> torch.empty((1, 2, 3, 4)).dim_order() 2025-07-17T09:05:49.5796332Z (0, 1, 2, 3) 2025-07-17T09:05:49.5796471Z >>> try: 2025-07-17T09:05:49.5796674Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check=True) 2025-07-17T09:05:49.5796922Z ... except RuntimeError as e: 2025-07-17T09:05:49.5797118Z ... print(e) 2025-07-17T09:05:49.5797410Z The tensor does not have unique dim order, or cannot map to exact one of the given memory formats. 2025-07-17T09:05:49.5797735Z >>> torch.empty((1, 2, 3, 4)).dim_order( 2025-07-17T09:05:49.5798004Z ... ambiguity_check=[torch.contiguous_format, torch.channels_last] 2025-07-17T09:05:49.5798286Z ... ) # It can be mapped to contiguous format 2025-07-17T09:05:49.5798497Z (0, 1, 2, 3) 2025-07-17T09:05:49.5798644Z >>> try: 2025-07-17T09:05:49.5798843Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check="ILLEGAL") 2025-07-17T09:05:49.5799095Z ... except TypeError as e: 2025-07-17T09:05:49.5799278Z ... print(e) 2025-07-17T09:05:49.5799519Z The ambiguity_check argument must be a bool or a list of memory formats. 2025-07-17T09:05:49.5799724Z 2025-07-17T09:05:49.5799829Z .. warning:: 2025-07-17T09:05:49.5800047Z The dim_order tensor API is experimental and subject to change. 2025-07-17T09:05:49.5800227Z 2025-07-17T09:05:49.5800389Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:49.5800595Z 2025-07-17T09:05:49.5946378Z msg = Cannot scrape callname=sum in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py line=202. 2025-07-17T09:05:49.5946959Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:49.5947311Z Return the sum of each row of the given sparse tensor. 2025-07-17T09:05:49.5947480Z 2025-07-17T09:05:49.5947631Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2025-07-17T09:05:49.5947967Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2025-07-17T09:05:49.5948728Z reduce over all of them. When sum over all ``sparse_dim``, this method 2025-07-17T09:05:49.5949027Z returns a dense tensor instead of a sparse tensor. 2025-07-17T09:05:49.5949196Z 2025-07-17T09:05:49.5949352Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2025-07-17T09:05:49.5949707Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2025-07-17T09:05:49.5950038Z 2025-07-17T09:05:49.5950181Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2025-07-17T09:05:49.5950534Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2025-07-17T09:05:49.5950752Z 2025-07-17T09:05:49.5950819Z Args: 2025-07-17T09:05:49.5950999Z input (Tensor): the input sparse tensor 2025-07-17T09:05:49.5951315Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2025-07-17T09:05:49.5951611Z over all dims. 2025-07-17T09:05:49.5951894Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2025-07-17T09:05:49.5952195Z Default: dtype of :attr:`input`. 2025-07-17T09:05:49.5952330Z 2025-07-17T09:05:49.5952413Z Example:: 2025-07-17T09:05:49.5952503Z 2025-07-17T09:05:49.5952577Z >>> nnz = 3 2025-07-17T09:05:49.5952747Z >>> dims = [5, 5, 2, 3] 2025-07-17T09:05:49.5952980Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2025-07-17T09:05:49.5953264Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2025-07-17T09:05:49.5953531Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2025-07-17T09:05:49.5953752Z >>> size = torch.Size(dims) 2025-07-17T09:05:49.5953979Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-07-17T09:05:49.5954217Z >>> S = torch.sparse_coo_tensor(I, V, size) 2025-07-17T09:05:49.5954427Z >>> S 2025-07-17T09:05:49.5954607Z tensor(indices=tensor([[2, 0, 3], 2025-07-17T09:05:49.5954823Z [2, 4, 1]]), 2025-07-17T09:05:49.5955050Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2025-07-17T09:05:49.5955281Z [ 0.3411, 0.0918, -0.2312]], 2025-07-17T09:05:49.5955420Z 2025-07-17T09:05:49.5955505Z [[ 0.5348, 0.0634, -2.0494], 2025-07-17T09:05:49.5955713Z [-0.7125, -1.0646, 2.1844]], 2025-07-17T09:05:49.5955843Z 2025-07-17T09:05:49.5955922Z [[ 0.1276, 0.1874, -0.6334], 2025-07-17T09:05:49.5956124Z [-1.9682, -0.5340, 0.7483]]]), 2025-07-17T09:05:49.5956359Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2025-07-17T09:05:49.5956531Z 2025-07-17T09:05:49.5956657Z # when sum over only part of sparse_dims, return a sparse tensor 2025-07-17T09:05:49.5956944Z >>> torch.sparse.sum(S, [1, 3]) 2025-07-17T09:05:49.5957170Z tensor(indices=tensor([[0, 2, 3]]), 2025-07-17T09:05:49.5957391Z values=tensor([[-1.4512, 0.4073], 2025-07-17T09:05:49.5957603Z [-0.8901, 0.2017], 2025-07-17T09:05:49.5957806Z [-0.3183, -1.7539]]), 2025-07-17T09:05:49.5958038Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2025-07-17T09:05:49.5958183Z 2025-07-17T09:05:49.5958300Z # when sum over all sparse dim, return a dense tensor 2025-07-17T09:05:49.5958540Z # with summed dims squeezed 2025-07-17T09:05:49.5958753Z >>> torch.sparse.sum(S, [0, 1, 3]) 2025-07-17T09:05:49.5958957Z tensor([-2.6596, -1.1450]) 2025-07-17T09:05:49.5959137Z 2025-07-17T09:05:49.5959374Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:49.5959595Z 2025-07-17T09:05:49.6544072Z msg = Cannot scrape callname=is_available in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/accelerator/__init__.py line=66. 2025-07-17T09:05:49.6545205Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-07-17T09:05:49.6545754Z Check if the current accelerator is available at runtime: it was build, all the 2025-07-17T09:05:49.6546118Z required drivers are available and at least one device is visible. 2025-07-17T09:05:49.6546420Z See :ref:`accelerator` for details. 2025-07-17T09:05:49.6546583Z 2025-07-17T09:05:49.6546849Z Returns: 2025-07-17T09:05:49.6547115Z bool: A boolean indicating if there is an available :ref:`accelerator`. 2025-07-17T09:05:49.6547354Z 2025-07-17T09:05:49.6547529Z .. note:: This API delegates to the device-specific version of `is_available`. 2025-07-17T09:05:49.6547899Z On CUDA, when the environment variable ``PYTORCH_NVML_BASED_CUDA_CHECK=1`` is set, 2025-07-17T09:05:49.6548274Z this function will NOT poison fork. Otherwise, it will. For more details, see 2025-07-17T09:05:49.6548593Z :ref:`multiprocessing-poison-fork-note`. 2025-07-17T09:05:49.6548743Z 2025-07-17T09:05:49.6548821Z Example:: 2025-07-17T09:05:49.6548910Z 2025-07-17T09:05:49.6549085Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2025-07-17T09:05:49.6549364Z 2025-07-17T09:05:49.6549771Z Original Error: SyntaxError('invalid syntax', ('', 1, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 1, 78)) 2025-07-17T09:05:49.6550140Z 2025-07-17T09:05:49.6550305Z assert torch.accelerator.is_available() "No available accelerators detected." 2025-07-17T09:05:49.6550599Z ^ 2025-07-17T09:05:49.6553373Z msg = Cannot scrape callname=synchronize in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/accelerator/__init__.py line=212. 2025-07-17T09:05:49.6553905Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-07-17T09:05:49.6554269Z Wait for all kernels in all streams on the given device to complete. 2025-07-17T09:05:49.6554461Z 2025-07-17T09:05:49.6554533Z Args: 2025-07-17T09:05:49.6554809Z device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match 2025-07-17T09:05:49.6555202Z the current :ref:`accelerator` device type. If not given, 2025-07-17T09:05:49.6555526Z use :func:`torch.accelerator.current_device_index` by default. 2025-07-17T09:05:49.6555719Z 2025-07-17T09:05:49.6555898Z .. note:: This function is a no-op if the current :ref:`accelerator` is not initialized. 2025-07-17T09:05:49.6556142Z 2025-07-17T09:05:49.6556206Z Example:: 2025-07-17T09:05:49.6556308Z 2025-07-17T09:05:49.6556402Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-07-17T09:05:49.6556710Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2025-07-17T09:05:49.6557028Z >>> start_event = torch.Event(enable_timing=True) 2025-07-17T09:05:49.6557279Z >>> end_event = torch.Event(enable_timing=True) 2025-07-17T09:05:49.6557512Z >>> start_event.record() 2025-07-17T09:05:49.6557782Z >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator()) 2025-07-17T09:05:49.6558067Z >>> sum = torch.sum(tensor) 2025-07-17T09:05:49.6558276Z >>> end_event.record() 2025-07-17T09:05:49.6558491Z >>> torch.accelerator.synchronize() 2025-07-17T09:05:49.6558758Z >>> elapsed_time_ms = start_event.elapsed_time(end_event) 2025-07-17T09:05:49.6558993Z 2025-07-17T09:05:49.6559392Z Original Error: SyntaxError('invalid syntax', ('', 2, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 2, 78)) 2025-07-17T09:05:49.6559751Z 2025-07-17T09:05:49.6559919Z assert torch.accelerator.is_available() "No available accelerators detected." 2025-07-17T09:05:49.6560211Z ^ 2025-07-17T09:05:49.6636650Z msg = Cannot scrape callname=cudart in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/__init__.py line=434. 2025-07-17T09:05:49.6637231Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-07-17T09:05:49.6637551Z Retrieves the CUDA runtime API module. 2025-07-17T09:05:49.6637702Z 2025-07-17T09:05:49.6637705Z 2025-07-17T09:05:49.6638112Z This function initializes the CUDA runtime environment if it is not already 2025-07-17T09:05:49.6638482Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2025-07-17T09:05:49.6638834Z runtime API module provides access to various CUDA runtime functions. 2025-07-17T09:05:49.6639042Z 2025-07-17T09:05:49.6639108Z Args: 2025-07-17T09:05:49.6639259Z ``None`` 2025-07-17T09:05:49.6639359Z 2025-07-17T09:05:49.6639423Z Returns: 2025-07-17T09:05:49.6639609Z module: The CUDA runtime API module (_cudart). 2025-07-17T09:05:49.6639774Z 2025-07-17T09:05:49.6639848Z Raises: 2025-07-17T09:05:49.6640087Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2025-07-17T09:05:49.6640511Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2025-07-17T09:05:49.6640785Z 2025-07-17T09:05:49.6640889Z Example of CUDA operations with profiling: 2025-07-17T09:05:49.6641102Z >>> import torch 2025-07-17T09:05:49.6641318Z >>> from torch.cuda import cudart, check_error 2025-07-17T09:05:49.6641551Z >>> import os 2025-07-17T09:05:49.6641709Z >>> 2025-07-17T09:05:49.6641884Z >>> os.environ["CUDA_PROFILE"] = "1" 2025-07-17T09:05:49.6642089Z >>> 2025-07-17T09:05:49.6642273Z >>> def perform_cuda_operations_with_streams(): 2025-07-17T09:05:49.6642510Z >>> stream = torch.cuda.Stream() 2025-07-17T09:05:49.6642731Z >>> with torch.cuda.stream(stream): 2025-07-17T09:05:49.6642962Z >>> x = torch.randn(100, 100, device='cuda') 2025-07-17T09:05:49.6643192Z >>> y = torch.randn(100, 100, device='cuda') 2025-07-17T09:05:49.6643406Z >>> z = torch.mul(x, y) 2025-07-17T09:05:49.6643605Z >>> return z 2025-07-17T09:05:49.6643777Z >>> 2025-07-17T09:05:49.6643934Z >>> torch.cuda.synchronize() 2025-07-17T09:05:49.6644157Z >>> print("====== Start nsys profiling ======") 2025-07-17T09:05:49.6644405Z >>> check_error(cudart().cudaProfilerStart()) 2025-07-17T09:05:49.6644648Z >>> with torch.autograd.profiler.emit_nvtx(): 2025-07-17T09:05:49.6644899Z >>> result = perform_cuda_operations_with_streams() 2025-07-17T09:05:49.6645146Z >>> print("CUDA operations completed.") 2025-07-17T09:05:49.6645398Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2025-07-17T09:05:49.6645646Z >>> print("====== End nsys profiling ======") 2025-07-17T09:05:49.6645785Z 2025-07-17T09:05:49.6645916Z To run this example and save the profiling information, execute: 2025-07-17T09:05:49.6646341Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2025-07-17T09:05:49.6646608Z 2025-07-17T09:05:49.6646765Z This command profiles the CUDA operations in the provided script and saves 2025-07-17T09:05:49.6647120Z the profiling information to a file named `trace_name.prof`. 2025-07-17T09:05:49.6647455Z The `--profile-from-start off` option ensures that profiling starts only 2025-07-17T09:05:49.6647757Z after the `cudaProfilerStart` call in the script. 2025-07-17T09:05:49.6648053Z The `--csv` and `--print-summary` options format the profiling output as a 2025-07-17T09:05:49.6648331Z CSV file and print a summary, respectively. 2025-07-17T09:05:49.6648629Z The `-o` option specifies the output file name, and the `-f` option forces the 2025-07-17T09:05:49.6648949Z overwrite of the output file if it already exists. 2025-07-17T09:05:49.6649177Z 2025-07-17T09:05:49.6649793Z 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)) 2025-07-17T09:05:49.6650206Z 2025-07-17T09:05:49.6650406Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2025-07-17T09:05:49.6650731Z ^ 2025-07-17T09:05:49.6681167Z msg = Cannot scrape callname=Future.then in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py line=101. 2025-07-17T09:05:49.6681788Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:49.6682030Z 2025-07-17T09:05:49.6682175Z Append the given callback function to this ``Future``, which will be run 2025-07-17T09:05:49.6682513Z when the ``Future`` is completed. Multiple callbacks can be added to 2025-07-17T09:05:49.6682834Z the same ``Future``, but the order in which they will be executed cannot 2025-07-17T09:05:49.6683161Z be guaranteed (to enforce a certain order consider chaining: 2025-07-17T09:05:49.6683462Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2025-07-17T09:05:49.6683772Z is the reference to this ``Future``. The callback function can use the 2025-07-17T09:05:49.6684097Z :meth:`value` method to get the value. Note that if this ``Future`` is 2025-07-17T09:05:49.6684419Z already completed, the given callback will be run immediately inline. 2025-07-17T09:05:49.6684617Z 2025-07-17T09:05:49.6684756Z If the ``Future``'s value contains tensors that reside on GPUs, the 2025-07-17T09:05:49.6685075Z callback might be invoked while the async kernels that are populating 2025-07-17T09:05:49.6685407Z those tensors haven't yet finished executing on the device. However, the 2025-07-17T09:05:49.6685737Z callback will be invoked with some dedicated streams set as current 2025-07-17T09:05:49.6686059Z (fetched from a global pool) which will be synchronized with those 2025-07-17T09:05:49.6686401Z kernels. Hence any operation performed by the callback on these tensors 2025-07-17T09:05:49.6686726Z will be scheduled on the device after the kernels complete. In other 2025-07-17T09:05:49.6687039Z words, as long as the callback doesn't switch streams, it can safely 2025-07-17T09:05:49.6687361Z manipulate the result without any additional synchronization. This is 2025-07-17T09:05:49.6687666Z similar to the non-blocking behavior of :meth:`wait`. 2025-07-17T09:05:49.6687840Z 2025-07-17T09:05:49.6687969Z Similarly, if the callback returns a value that contains tensors that 2025-07-17T09:05:49.6688285Z reside on a GPU, it can do so even if the kernels that are producing 2025-07-17T09:05:49.6688598Z these tensors are still running on the device, as long as the callback 2025-07-17T09:05:49.6688920Z didn't change streams during its execution. If one wants to change 2025-07-17T09:05:49.6689228Z streams, one must be careful to re-synchronize them with the original 2025-07-17T09:05:49.6689546Z streams, that is, those that were current when the callback was invoked. 2025-07-17T09:05:49.6689733Z 2025-07-17T09:05:49.6689805Z Args: 2025-07-17T09:05:49.6690016Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2025-07-17T09:05:49.6690278Z the only argument. 2025-07-17T09:05:49.6690407Z 2025-07-17T09:05:49.6690518Z Returns: 2025-07-17T09:05:49.6690715Z A new ``Future`` object that holds the return value of the 2025-07-17T09:05:49.6690997Z ``callback`` and will be marked as completed when the given 2025-07-17T09:05:49.6691241Z ``callback`` finishes. 2025-07-17T09:05:49.6691366Z 2025-07-17T09:05:49.6691485Z .. note:: Note that if the callback function throws, either 2025-07-17T09:05:49.6691783Z through the original future being completed with an exception and 2025-07-17T09:05:49.6692091Z calling ``fut.wait()``, or through other code in the callback, the 2025-07-17T09:05:49.6692388Z future returned by ``then`` will be marked appropriately with the 2025-07-17T09:05:49.6692877Z encountered error. However, if this callback later completes 2025-07-17T09:05:49.6693186Z additional futures, those futures are not marked as completed with 2025-07-17T09:05:49.6693498Z an error and the user is responsible for handling completion/waiting 2025-07-17T09:05:49.6693760Z on those futures independently. 2025-07-17T09:05:49.6693887Z 2025-07-17T09:05:49.6693961Z Example:: 2025-07-17T09:05:49.6694041Z 2025-07-17T09:05:49.6694260Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2025-07-17T09:05:49.6694493Z >>> def callback(fut): 2025-07-17T09:05:49.6694699Z ... print(f"RPC return value is {fut.wait()}.") 2025-07-17T09:05:49.6694922Z >>> fut = torch.futures.Future() 2025-07-17T09:05:49.6695160Z >>> # The inserted callback will print the return value when 2025-07-17T09:05:49.6695416Z >>> # receiving the response from "worker1" 2025-07-17T09:05:49.6695637Z >>> cb_fut = fut.then(callback) 2025-07-17T09:05:49.6695839Z >>> chain_cb_fut = cb_fut.then( 2025-07-17T09:05:49.6696072Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2025-07-17T09:05:49.6696299Z ... ) 2025-07-17T09:05:49.6696455Z >>> fut.set_result(5) 2025-07-17T09:05:49.6696636Z RPC return value is 5. 2025-07-17T09:05:49.6696819Z Chained cb done. None 2025-07-17T09:05:49.6696919Z 2025-07-17T09:05:49.6697088Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:49.6697305Z 2025-07-17T09:05:49.6697643Z msg = Cannot scrape callname=Future.set_result in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py line=211. 2025-07-17T09:05:49.6698163Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:49.6698379Z 2025-07-17T09:05:49.6698524Z Set the result for this ``Future``, which will mark this ``Future`` as 2025-07-17T09:05:49.6698858Z completed and trigger all attached callbacks. Note that a ``Future`` 2025-07-17T09:05:49.6699140Z cannot be marked completed twice. 2025-07-17T09:05:49.6699272Z 2025-07-17T09:05:49.6699415Z If the result contains tensors that reside on GPUs, this method can be 2025-07-17T09:05:49.6699731Z called even if the asynchronous kernels that are populating those 2025-07-17T09:05:49.6700055Z tensors haven't yet completed running on the device, provided that the 2025-07-17T09:05:49.6700388Z streams on which those kernels were enqueued are set as the current ones 2025-07-17T09:05:49.6700727Z when this method is called. Put simply, it's safe to call this method 2025-07-17T09:05:49.6701039Z immediately after launching those kernels, without any additional 2025-07-17T09:05:49.6701371Z synchronization, as long as one doesn't change streams in between. This 2025-07-17T09:05:49.6701715Z method will record events on all the relevant current streams and will 2025-07-17T09:05:49.6702026Z use them to ensure proper scheduling for all the consumers of this 2025-07-17T09:05:49.6702270Z ``Future``. 2025-07-17T09:05:49.6702355Z 2025-07-17T09:05:49.6702430Z Args: 2025-07-17T09:05:49.6702630Z result (object): the result object of this ``Future``. 2025-07-17T09:05:49.6702792Z 2025-07-17T09:05:49.6702863Z Example:: 2025-07-17T09:05:49.6702946Z 2025-07-17T09:05:49.6703051Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2025-07-17T09:05:49.6703283Z >>> import threading 2025-07-17T09:05:49.6703443Z >>> import time 2025-07-17T09:05:49.6703627Z >>> def slow_set_future(fut, value): 2025-07-17T09:05:49.6703834Z ... time.sleep(0.5) 2025-07-17T09:05:49.6704027Z ... fut.set_result(value) 2025-07-17T09:05:49.6704223Z >>> fut = torch.futures.Future() 2025-07-17T09:05:49.6704420Z >>> t = threading.Thread( 2025-07-17T09:05:49.6704617Z ... target=slow_set_future, 2025-07-17T09:05:49.6704822Z ... args=(fut, torch.ones(2) * 3) 2025-07-17T09:05:49.6705018Z ... ) 2025-07-17T09:05:49.6705160Z >>> t.start() 2025-07-17T09:05:49.6705445Z >>> print(fut.wait()) 2025-07-17T09:05:49.6705811Z tensor([3., 3.]) 2025-07-17T09:05:49.6705977Z >>> t.join() 2025-07-17T09:05:49.6706074Z 2025-07-17T09:05:49.6706225Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:49.6706450Z 2025-07-17T09:05:49.6798809Z msg = Cannot scrape callname=compute_required_storage_length in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims_common/__init__.py line=1848. 2025-07-17T09:05:49.6799788Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:49.6800173Z Computes the minimum storage size to hold the given tensor geometry. 2025-07-17T09:05:49.6800380Z 2025-07-17T09:05:49.6800448Z Example 2025-07-17T09:05:49.6800599Z ======= 2025-07-17T09:05:49.6800685Z 2025-07-17T09:05:49.6800832Z This is the size of a newly allocated tensor's storage, in units of elements 2025-07-17T09:05:49.6801040Z 2025-07-17T09:05:49.6801117Z >>> t = torch.empty((10, 20)) 2025-07-17T09:05:49.6801406Z >>> compute_required_storage_length(t.shape, t.stride(), t.storage_offset()) 2025-07-17T09:05:49.6801677Z 200 2025-07-17T09:05:49.6801757Z 2025-07-17T09:05:49.6801839Z >>> # xdoctest: +SKIP(failing) 2025-07-17T09:05:49.6802064Z >>> t2 = torch.empty_strided((1, 2, 3), (5, 7, 11)) 2025-07-17T09:05:49.6802306Z >>> size = compute_required_storage_length( 2025-07-17T09:05:49.6802543Z ... t2.shape, t2.stride(), t2.storage_offset() 2025-07-17T09:05:49.6802761Z ... ) 2025-07-17T09:05:49.6802911Z >>> size == t.storage().size() 2025-07-17T09:05:49.6803097Z True 2025-07-17T09:05:49.6803187Z 2025-07-17T09:05:49.6803314Z A valid tensor may have a larger storage size, but never smaller 2025-07-17T09:05:49.6803503Z 2025-07-17T09:05:49.6803578Z >>> slice = torch.empty(100)[20:40] 2025-07-17T09:05:49.6803788Z >>> slice.storage().size() 2025-07-17T09:05:49.6803971Z 100 2025-07-17T09:05:49.6804055Z 2025-07-17T09:05:49.6804134Z >>> compute_required_storage_length( 2025-07-17T09:05:49.6804391Z ... slice.shape, slice.stride(), slice.storage_offset() 2025-07-17T09:05:49.6804623Z ... ) 2025-07-17T09:05:49.6804762Z 40 2025-07-17T09:05:49.6804838Z 2025-07-17T09:05:49.6804905Z 2025-07-17T09:05:49.6805143Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:49.6805357Z 2025-07-17T09:05:49.7033560Z msg = Cannot scrape callname=compile_shader in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/mps/__init__.py line=145. 2025-07-17T09:05:49.7034141Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:49.7034505Z Compiles compute shader from source and allows one to invoke kernels 2025-07-17T09:05:49.7034843Z defined there from the comfort of Python runtime 2025-07-17T09:05:49.7035085Z Example:: 2025-07-17T09:05:49.7035192Z 2025-07-17T09:05:49.7035304Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_MPS) 2025-07-17T09:05:49.7035551Z >>> lib = torch.mps.compile_shader( 2025-07-17T09:05:49.7035966Z ... "kernel void full(device float* out, constant float& val, uint idx [[thread_position_in_grid]]) { out[idx] = val; }" 2025-07-17T09:05:49.7036317Z ... ) 2025-07-17T09:05:49.7036500Z >>> x = torch.zeros(16, device="mps") 2025-07-17T09:05:49.7036721Z >>> lib.full(x, 3.14) 2025-07-17T09:05:49.7036909Z 2025-07-17T09:05:49.7037156Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:49.7037372Z 2025-07-17T09:05:49.7244572Z msg = Cannot scrape callname=vmap in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py line=39. 2025-07-17T09:05:49.7245242Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:49.7245481Z 2025-07-17T09:05:49.7245624Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2025-07-17T09:05:49.7245959Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2025-07-17T09:05:49.7246757Z pushes the map into PyTorch operations called by ``func``, effectively 2025-07-17T09:05:49.7247050Z vectorizing those operations. 2025-07-17T09:05:49.7247182Z 2025-07-17T09:05:49.7247316Z vmap is useful for handling batch dimensions: one can write a function 2025-07-17T09:05:49.7247638Z ``func`` that runs on examples and then lift it to a function that can 2025-07-17T09:05:49.7248140Z take batches of examples with ``vmap(func)``. vmap can also be used to 2025-07-17T09:05:49.7248450Z compute batched gradients when composed with autograd. 2025-07-17T09:05:49.7248619Z 2025-07-17T09:05:49.7248708Z .. note:: 2025-07-17T09:05:49.7248925Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2025-07-17T09:05:49.7249195Z convenience. Use whichever one you'd like. 2025-07-17T09:05:49.7249339Z 2025-07-17T09:05:49.7249407Z Args: 2025-07-17T09:05:49.7249619Z func (function): A Python function that takes one or more arguments. 2025-07-17T09:05:49.7249893Z Must return one or more Tensors. 2025-07-17T09:05:49.7250175Z in_dims (int or nested structure): Specifies which dimension of the 2025-07-17T09:05:49.7250481Z inputs should be mapped over. ``in_dims`` should have a 2025-07-17T09:05:49.7250795Z structure like the inputs. If the ``in_dim`` for a particular 2025-07-17T09:05:49.7251093Z input is None, then that indicates there is no map dimension. 2025-07-17T09:05:49.7251335Z Default: 0. 2025-07-17T09:05:49.7251571Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2025-07-17T09:05:49.7251866Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2025-07-17T09:05:49.7252149Z it should have one element per output. Default: 0. 2025-07-17T09:05:49.7252435Z randomness (str): Specifies whether the randomness in this 2025-07-17T09:05:49.7252763Z vmap should be the same or different across batches. If 'different', 2025-07-17T09:05:49.7253102Z the randomness for each batch will be different. If 'same', the 2025-07-17T09:05:49.7253422Z randomness will be the same across batches. If 'error', any calls to 2025-07-17T09:05:49.7253748Z random functions will error. Default: 'error'. WARNING: this flag 2025-07-17T09:05:49.7254064Z only applies to random PyTorch operations and does not apply to 2025-07-17T09:05:49.7254340Z Python's random module or numpy randomness. 2025-07-17T09:05:49.7254637Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2025-07-17T09:05:49.7254987Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2025-07-17T09:05:49.7255342Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2025-07-17T09:05:49.7255718Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2025-07-17T09:05:49.7255944Z 2025-07-17T09:05:49.7256014Z Returns: 2025-07-17T09:05:49.7256229Z Returns a new "batched" function. It takes the same inputs as 2025-07-17T09:05:49.7256539Z ``func``, except each input has an extra dimension at the index 2025-07-17T09:05:49.7256835Z specified by ``in_dims``. It takes returns the same outputs as 2025-07-17T09:05:49.7257135Z ``func``, except each output has an extra dimension at the index 2025-07-17T09:05:49.7257391Z specified by ``out_dims``. 2025-07-17T09:05:49.7257507Z 2025-07-17T09:05:49.7257577Z .. warning: 2025-07-17T09:05:49.7257792Z :func:`vmap` works best with functional-style code. Please do not 2025-07-17T09:05:49.7258093Z perform any side-effects in ``func``, with the exception of 2025-07-17T09:05:49.7258419Z in-place PyTorch operations. Examples of side-effects include mutating 2025-07-17T09:05:49.7258764Z Python data structures and assigning values to variables not captured 2025-07-17T09:05:49.7259032Z in ``func``. 2025-07-17T09:05:49.7259133Z 2025-07-17T09:05:49.7259278Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2025-07-17T09:05:49.7259771Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2025-07-17T09:05:49.7260107Z rummaging through docs, use :func:`vmap` to construct a new function. 2025-07-17T09:05:49.7260307Z 2025-07-17T09:05:49.7260398Z >>> torch.dot # [D], [D] -> [] 2025-07-17T09:05:49.7260671Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2025-07-17T09:05:49.7261091Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2025-07-17T09:05:49.7261314Z >>> batched_dot(x, y) 2025-07-17T09:05:49.7261423Z 2025-07-17T09:05:49.7261576Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2025-07-17T09:05:49.7261858Z model authoring experience. 2025-07-17T09:05:49.7261971Z 2025-07-17T09:05:49.7262055Z >>> batch_size, feature_size = 3, 5 2025-07-17T09:05:49.7262295Z >>> weights = torch.randn(feature_size, requires_grad=True) 2025-07-17T09:05:49.7262530Z >>> 2025-07-17T09:05:49.7262686Z >>> def model(feature_vec): 2025-07-17T09:05:49.7262908Z >>> # Very simple linear model with activation 2025-07-17T09:05:49.7263136Z >>> return feature_vec.dot(weights).relu() 2025-07-17T09:05:49.7263338Z >>> 2025-07-17T09:05:49.7263522Z >>> examples = torch.randn(batch_size, feature_size) 2025-07-17T09:05:49.7263768Z >>> result = torch.vmap(model)(examples) 2025-07-17T09:05:49.7263898Z 2025-07-17T09:05:49.7264063Z :func:`vmap` can also help vectorize computations that were previously difficult 2025-07-17T09:05:49.7264417Z or impossible to batch. One example is higher-order gradient computation. 2025-07-17T09:05:49.7264769Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2025-07-17T09:05:49.7265125Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2025-07-17T09:05:49.7265573Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2025-07-17T09:05:49.7265918Z we can vectorize the whole computation, computing the Jacobian in a single 2025-07-17T09:05:49.7266210Z call to ``autograd.grad``. 2025-07-17T09:05:49.7266347Z 2025-07-17T09:05:49.7266407Z >>> # Setup 2025-07-17T09:05:49.7266555Z >>> N = 5 2025-07-17T09:05:49.7266859Z >>> f = lambda x: x**2 2025-07-17T09:05:49.7267338Z >>> x = torch.randn(N, requires_grad=True) 2025-07-17T09:05:49.7267603Z >>> y = f(x) 2025-07-17T09:05:49.7278357Z >>> I_N = torch.eye(N) 2025-07-17T09:05:49.7278558Z >>> 2025-07-17T09:05:49.7278741Z >>> # Sequential approach 2025-07-17T09:05:49.7279016Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2025-07-17T09:05:49.7279315Z >>> for v in I_N.unbind()] 2025-07-17T09:05:49.7279550Z >>> jacobian = torch.stack(jacobian_rows) 2025-07-17T09:05:49.7279760Z >>> 2025-07-17T09:05:49.7279931Z >>> # vectorized gradient computation 2025-07-17T09:05:49.7280147Z >>> def get_vjp(v): 2025-07-17T09:05:49.7280337Z >>> return torch.autograd.grad(y, x, v) 2025-07-17T09:05:49.7280572Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2025-07-17T09:05:49.7280720Z 2025-07-17T09:05:49.7280890Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2025-07-17T09:05:49.7281122Z 2025-07-17T09:05:49.7281204Z >>> torch.dot # [D], [D] -> [] 2025-07-17T09:05:49.7281423Z >>> batched_dot = torch.vmap( 2025-07-17T09:05:49.7281626Z ... torch.vmap(torch.dot) 2025-07-17T09:05:49.7281842Z ... ) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2025-07-17T09:05:49.7282094Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2025-07-17T09:05:49.7282345Z >>> batched_dot(x, y) # tensor of size [2, 3] 2025-07-17T09:05:49.7282491Z 2025-07-17T09:05:49.7282653Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2025-07-17T09:05:49.7282969Z the dimension that each inputs are batched along as 2025-07-17T09:05:49.7283125Z 2025-07-17T09:05:49.7283211Z >>> torch.dot # [N], [N] -> [] 2025-07-17T09:05:49.7283482Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2025-07-17T09:05:49.7283978Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2025-07-17T09:05:49.7284195Z >>> batched_dot( 2025-07-17T09:05:49.7284365Z ... x, y 2025-07-17T09:05:49.7284592Z ... ) # output is [5] instead of [2] if batched along the 0th dimension 2025-07-17T09:05:49.7284780Z 2025-07-17T09:05:49.7284941Z If there are multiple inputs each of which is batched along different dimensions, 2025-07-17T09:05:49.7285422Z ``in_dims`` must be a tuple with the batch dimension for each input as 2025-07-17T09:05:49.7285619Z 2025-07-17T09:05:49.7285696Z >>> torch.dot # [D], [D] -> [] 2025-07-17T09:05:49.7285973Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2025-07-17T09:05:49.7286259Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2025-07-17T09:05:49.7286469Z >>> batched_dot( 2025-07-17T09:05:49.7286633Z ... x, y 2025-07-17T09:05:49.7286849Z ... ) # second arg doesn't have a batch dim because in_dim[1] was None 2025-07-17T09:05:49.7287035Z 2025-07-17T09:05:49.7287187Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2025-07-17T09:05:49.7287477Z matching the shape of the input: 2025-07-17T09:05:49.7287604Z 2025-07-17T09:05:49.7287711Z >>> f = lambda dict: torch.dot(dict["x"], dict["y"]) 2025-07-17T09:05:49.7287934Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2025-07-17T09:05:49.7288157Z >>> input = {"x": x, "y": y} 2025-07-17T09:05:49.7288394Z >>> batched_dot = torch.vmap(f, in_dims=({"x": 0, "y": None},)) 2025-07-17T09:05:49.7288643Z >>> batched_dot(input) 2025-07-17T09:05:49.7288760Z 2025-07-17T09:05:49.7288920Z By default, the output is batched along the first dimension. However, it can be batched 2025-07-17T09:05:49.7289240Z along any dimension by using ``out_dims`` 2025-07-17T09:05:49.7289390Z 2025-07-17T09:05:49.7289460Z >>> f = lambda x: x**2 2025-07-17T09:05:49.7289648Z >>> x = torch.randn(2, 5) 2025-07-17T09:05:49.7289868Z >>> batched_pow = torch.vmap(f, out_dims=1) 2025-07-17T09:05:49.7290089Z >>> batched_pow(x) # [5, 2] 2025-07-17T09:05:49.7290203Z 2025-07-17T09:05:49.7290390Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2025-07-17T09:05:49.7290694Z accept kwargs 2025-07-17T09:05:49.7290784Z 2025-07-17T09:05:49.7290886Z >>> x = torch.randn([2, 5]) 2025-07-17T09:05:49.7291079Z >>> def fn(x, scale=4.): 2025-07-17T09:05:49.7291278Z >>> return x * scale 2025-07-17T09:05:49.7291447Z >>> 2025-07-17T09:05:49.7291612Z >>> batched_pow = torch.vmap(fn) 2025-07-17T09:05:49.7291835Z >>> assert torch.allclose(batched_pow(x), x * 4) 2025-07-17T09:05:49.7292130Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2025-07-17T09:05:49.7292340Z 2025-07-17T09:05:49.7292410Z .. note:: 2025-07-17T09:05:49.7292648Z vmap does not provide general autobatching or handle variable-length 2025-07-17T09:05:49.7292932Z sequences out of the box. 2025-07-17T09:05:49.7293050Z 2025-07-17T09:05:49.7293206Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:49.7293427Z 2025-07-17T09:05:49.7293702Z msg = Cannot scrape callname=grad in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py line=306. 2025-07-17T09:05:49.7294190Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:49.7294556Z ``grad`` operator helps computing gradients of ``func`` with respect to the 2025-07-17T09:05:49.7294893Z input(s) specified by ``argnums``. This operator can be nested to 2025-07-17T09:05:49.7295164Z compute higher-order gradients. 2025-07-17T09:05:49.7295290Z 2025-07-17T09:05:49.7295361Z Args: 2025-07-17T09:05:49.7295582Z func (Callable): A Python function that takes one or more arguments. 2025-07-17T09:05:49.7295939Z Must return a single-element Tensor. If specified ``has_aux`` equals ``True``, 2025-07-17T09:05:49.7296495Z function can return a tuple of single-element Tensor and other auxiliary objects: 2025-07-17T09:05:49.7296792Z ``(output, aux)``. 2025-07-17T09:05:49.7297072Z argnums (int or Tuple[int]): Specifies arguments to compute gradients with respect to. 2025-07-17T09:05:49.7297437Z ``argnums`` can be single integer or tuple of integers. Default: 0. 2025-07-17T09:05:49.7297875Z has_aux (bool): Flag indicating that ``func`` returns a tensor and other 2025-07-17T09:05:49.7298186Z auxiliary objects: ``(output, aux)``. Default: False. 2025-07-17T09:05:49.7298365Z 2025-07-17T09:05:49.7298427Z Returns: 2025-07-17T09:05:49.7298698Z Function to compute gradients with respect to its inputs. By default, the output of 2025-07-17T09:05:49.7299080Z the function is the gradient tensor(s) with respect to the first argument. 2025-07-17T09:05:49.7299451Z If specified ``has_aux`` equals ``True``, tuple of gradients and output auxiliary objects 2025-07-17T09:05:49.7299834Z is returned. If ``argnums`` is a tuple of integers, a tuple of output gradients with 2025-07-17T09:05:49.7300143Z respect to each ``argnums`` value is returned. 2025-07-17T09:05:49.7300297Z 2025-07-17T09:05:49.7300392Z Example of using ``grad``: 2025-07-17T09:05:49.7300509Z 2025-07-17T09:05:49.7300589Z >>> # xdoctest: +SKIP 2025-07-17T09:05:49.7300795Z >>> from torch.func import grad 2025-07-17T09:05:49.7301005Z >>> x = torch.randn([]) 2025-07-17T09:05:49.7301212Z >>> cos_x = grad(lambda x: torch.sin(x))(x) 2025-07-17T09:05:49.7301438Z >>> assert torch.allclose(cos_x, x.cos()) 2025-07-17T09:05:49.7301645Z >>> 2025-07-17T09:05:49.7301809Z >>> # Second-order gradients 2025-07-17T09:05:49.7302035Z >>> neg_sin_x = grad(grad(lambda x: torch.sin(x)))(x) 2025-07-17T09:05:49.7302291Z >>> assert torch.allclose(neg_sin_x, -x.sin()) 2025-07-17T09:05:49.7302451Z 2025-07-17T09:05:49.7302603Z When composed with ``vmap``, ``grad`` can be used to compute per-sample-gradients: 2025-07-17T09:05:49.7302822Z 2025-07-17T09:05:49.7302890Z >>> # xdoctest: +SKIP 2025-07-17T09:05:49.7303095Z >>> from torch.func import grad, vmap 2025-07-17T09:05:49.7303317Z >>> batch_size, feature_size = 3, 5 2025-07-17T09:05:49.7303518Z >>> 2025-07-17T09:05:49.7303691Z >>> def model(weights, feature_vec): 2025-07-17T09:05:49.7303924Z >>> # Very simple linear model with activation 2025-07-17T09:05:49.7304170Z >>> assert feature_vec.dim() == 1 2025-07-17T09:05:49.7304394Z >>> return feature_vec.dot(weights).relu() 2025-07-17T09:05:49.7304601Z >>> 2025-07-17T09:05:49.7304789Z >>> def compute_loss(weights, example, target): 2025-07-17T09:05:49.7305024Z >>> y = model(weights, example) 2025-07-17T09:05:49.7305334Z >>> return ((y - target) ** 2).mean() # MSELoss 2025-07-17T09:05:49.7305561Z >>> 2025-07-17T09:05:49.7305770Z >>> weights = torch.randn(feature_size, requires_grad=True) 2025-07-17T09:05:49.7306054Z >>> examples = torch.randn(batch_size, feature_size) 2025-07-17T09:05:49.7306296Z >>> targets = torch.randn(batch_size) 2025-07-17T09:05:49.7306529Z >>> inputs = (weights, examples, targets) 2025-07-17T09:05:49.7306821Z >>> grad_weight_per_example = vmap(grad(compute_loss), in_dims=(None, 0, 0))( 2025-07-17T09:05:49.7307103Z ... *inputs 2025-07-17T09:05:49.7307264Z ... ) 2025-07-17T09:05:49.7307361Z 2025-07-17T09:05:49.7307476Z Example of using ``grad`` with ``has_aux`` and ``argnums``: 2025-07-17T09:05:49.7307655Z 2025-07-17T09:05:49.7307723Z >>> # xdoctest: +SKIP 2025-07-17T09:05:49.7307925Z >>> from torch.func import grad 2025-07-17T09:05:49.7308146Z >>> def my_loss_func(y, y_pred): 2025-07-17T09:05:49.7308373Z >>> loss_per_sample = (0.5 * y_pred - y) ** 2 2025-07-17T09:05:49.7308758Z >>> loss = loss_per_sample.mean() 2025-07-17T09:05:49.7308988Z >>> return loss, (y_pred, loss_per_sample) 2025-07-17T09:05:49.7309198Z >>> 2025-07-17T09:05:49.7309387Z >>> fn = grad(my_loss_func, argnums=(0, 1), has_aux=True) 2025-07-17T09:05:49.7309625Z >>> y_true = torch.rand(4) 2025-07-17T09:05:49.7309848Z >>> y_preds = torch.rand(4, requires_grad=True) 2025-07-17T09:05:49.7310191Z >>> out = fn(y_true, y_preds) 2025-07-17T09:05:49.7310486Z >>> # > output is ((grads w.r.t y_true, grads w.r.t y_preds), (y_pred, loss_per_sample)) 2025-07-17T09:05:49.7310709Z 2025-07-17T09:05:49.7310775Z .. note:: 2025-07-17T09:05:49.7310979Z Using PyTorch ``torch.no_grad`` together with ``grad``. 2025-07-17T09:05:49.7311157Z 2025-07-17T09:05:49.7311257Z Case 1: Using ``torch.no_grad`` inside a function: 2025-07-17T09:05:49.7311423Z 2025-07-17T09:05:49.7311495Z >>> # xdoctest: +SKIP 2025-07-17T09:05:49.7311694Z >>> def f(x): 2025-07-17T09:05:49.7311881Z >>> with torch.no_grad(): 2025-07-17T09:05:49.7312089Z >>> c = x ** 2 2025-07-17T09:05:49.7312284Z >>> return x - c 2025-07-17T09:05:49.7312408Z 2025-07-17T09:05:49.7312537Z In this case, ``grad(f)(x)`` will respect the inner ``torch.no_grad``. 2025-07-17T09:05:49.7312733Z 2025-07-17T09:05:49.7312852Z Case 2: Using ``grad`` inside ``torch.no_grad`` context manager: 2025-07-17T09:05:49.7313035Z 2025-07-17T09:05:49.7313102Z >>> # xdoctest: +SKIP 2025-07-17T09:05:49.7313292Z >>> with torch.no_grad(): 2025-07-17T09:05:49.7313494Z >>> grad(f)(x) 2025-07-17T09:05:49.7313606Z 2025-07-17T09:05:49.7313751Z In this case, ``grad`` will respect the inner ``torch.no_grad``, but not the 2025-07-17T09:05:49.7314085Z outer one. This is because ``grad`` is a "function transform": its result 2025-07-17T09:05:49.7314433Z should not depend on the result of a context manager outside of ``f``. 2025-07-17T09:05:49.7314629Z 2025-07-17T09:05:49.7314699Z 2025-07-17T09:05:49.7314931Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:49.7315140Z 2025-07-17T09:05:49.8213270Z msg = Cannot scrape callname=CustomOpDef.register_fake in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py line=396. 2025-07-17T09:05:49.8213949Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-07-17T09:05:49.8214308Z Register a FakeTensor implementation for this custom op. 2025-07-17T09:05:49.8214480Z 2025-07-17T09:05:49.8214651Z This is necessary to get the operator to work efficiently with torch.compile. 2025-07-17T09:05:49.8214869Z 2025-07-17T09:05:49.8215019Z The Fake impl (sometimes also known as a meta kernel or abstract impl) 2025-07-17T09:05:49.8215356Z specifies the behavior of this operator on Tensors that carry no data. 2025-07-17T09:05:49.8215666Z Given some input Tensors with certain properties 2025-07-17T09:05:49.8216000Z (sizes/strides/storage_offset/device), it specifies what the properties of 2025-07-17T09:05:49.8216304Z the output Tensors are. 2025-07-17T09:05:49.8216435Z 2025-07-17T09:05:49.8216567Z Please see :func:`torch.library.impl_abstract` for more details. 2025-07-17T09:05:49.8216762Z 2025-07-17T09:05:49.8216831Z Args: 2025-07-17T09:05:49.8217039Z fn (Callable): The function to register as the FakeTensor 2025-07-17T09:05:49.8217286Z implementation. 2025-07-17T09:05:49.8217409Z 2025-07-17T09:05:49.8217470Z Examples: 2025-07-17T09:05:49.8217646Z >>> import torch 2025-07-17T09:05:49.8217845Z >>> import numpy as np 2025-07-17T09:05:49.8218051Z >>> from torch import Tensor 2025-07-17T09:05:49.8218242Z >>> 2025-07-17T09:05:49.8218459Z >>> # Example 1: an operator without data-dependent output shape 2025-07-17T09:05:49.8219382Z >>> @torch.library.custom_op("mylib::linear", mutates_args=()) 2025-07-17T09:05:49.8219768Z >>> def linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2025-07-17T09:05:49.8220040Z >>> return (x @ weight.t()) + bias 2025-07-17T09:05:49.8220249Z >>> 2025-07-17T09:05:49.8220421Z >>> @linear.register_fake 2025-07-17T09:05:49.8220878Z >>> def _(x, weight, bias): 2025-07-17T09:05:49.8221083Z >>> assert x.dim() == 2 2025-07-17T09:05:49.8221279Z >>> assert weight.dim() == 2 2025-07-17T09:05:49.8221494Z >>> assert bias.dim() == 1 2025-07-17T09:05:49.8221726Z >>> assert x.shape[1] == weight.shape[1] 2025-07-17T09:05:49.8221963Z >>> assert weight.shape[0] == bias.shape[0] 2025-07-17T09:05:49.8222205Z >>> assert x.device == weight.device 2025-07-17T09:05:49.8222459Z >>> return x.new_empty(x.size(0), weight.size(0)) 2025-07-17T09:05:49.8222688Z >>> 2025-07-17T09:05:49.8222855Z >>> x = torch.randn(2, 2) 2025-07-17T09:05:49.8223068Z >>> weight = torch.randn(2, 2) 2025-07-17T09:05:49.8223288Z >>> bias = torch.randn(2) 2025-07-17T09:05:49.8223517Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-07-17T09:05:49.8223802Z >>> out = torch.compile(linear, fullgraph=True)(x, weight, bias) 2025-07-17T09:05:49.8224074Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-07-17T09:05:49.8224371Z >>> assert torch.allclose(out, torch.nn.functional.linear(x, weight, bias)) 2025-07-17T09:05:49.8224649Z >>> 2025-07-17T09:05:49.8224861Z >>> # Example 2: an operator with data-dependent output shape 2025-07-17T09:05:49.8225162Z >>> @torch.library.custom_op("mylib::nonzero", mutates_args=()) 2025-07-17T09:05:49.8225525Z >>> def nonzero(x: Tensor) -> Tensor: 2025-07-17T09:05:49.8225748Z >>> x_np = x.cpu().numpy() 2025-07-17T09:05:49.8225966Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2025-07-17T09:05:49.8226204Z >>> return torch.tensor(res, device=x.device) 2025-07-17T09:05:49.8226418Z >>> 2025-07-17T09:05:49.8226586Z >>> @nonzero.register_fake 2025-07-17T09:05:49.8226788Z >>> def _(x): 2025-07-17T09:05:49.8227010Z >>> # Number of nonzero-elements is data-dependent. 2025-07-17T09:05:49.8227274Z >>> # Since we cannot peek at the data in an abstract impl, 2025-07-17T09:05:49.8227546Z >>> # we use the ctx object to construct a new symint that 2025-07-17T09:05:49.8227798Z >>> # represents the data-dependent size. 2025-07-17T09:05:49.8228031Z >>> ctx = torch.library.get_ctx() 2025-07-17T09:05:49.8228247Z >>> nnz = ctx.new_dynamic_size() 2025-07-17T09:05:49.8228464Z >>> shape = [nnz, x.dim()] 2025-07-17T09:05:49.8228702Z >>> result = x.new_empty(shape, dtype=torch.int64) 2025-07-17T09:05:49.8228940Z >>> return result 2025-07-17T09:05:49.8229122Z >>> 2025-07-17T09:05:49.8229293Z >>> x = torch.tensor([0, 1, 2, 0, 0, 1]) 2025-07-17T09:05:49.8229519Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-07-17T09:05:49.8229769Z >>> out = torch.compile(nonzero, fullgraph=True)(x) 2025-07-17T09:05:49.8230017Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-07-17T09:05:49.8230244Z >>> assert torch.allclose(out, x.nonzero()) 2025-07-17T09:05:49.8230393Z 2025-07-17T09:05:49.8230457Z 2025-07-17T09:05:49.8230852Z Original Error: IndentationError('expected an indented block after function definition on line 36', ('', 37, 1, '_._ = None\n', 37, 2)) 2025-07-17T09:05:49.8231208Z 2025-07-17T09:05:49.8231269Z _._ = None 2025-07-17T09:05:49.8231412Z ^ 2025-07-17T09:05:49.8335530Z msg = Cannot scrape callname=triton_op in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/triton.py line=21. 2025-07-17T09:05:49.8336138Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:49.8336532Z Create a custom operator whose implementation is backed by 1+ triton kernels. 2025-07-17T09:05:49.8336760Z 2025-07-17T09:05:49.8336896Z This is a more structured way of using triton kernels with PyTorch. 2025-07-17T09:05:49.8337569Z Prefer using triton kernels with no ``torch.library`` custom operator wrappers 2025-07-17T09:05:49.8337964Z (like :func:`torch.library.custom_op`, :func:`torch.library.triton_op`) because 2025-07-17T09:05:49.8338259Z that is simpler; 2025-07-17T09:05:49.8338531Z only use :func:`torch.library.custom_op`/:func:`torch.library.triton_op` if you 2025-07-17T09:05:49.8338891Z want to create an operator that behaves like PyTorch built-in operators. 2025-07-17T09:05:49.8339228Z For example, you may use a ``torch.library`` wrapper API to define the 2025-07-17T09:05:49.8339562Z behavior of the triton kernel when passed a tensor subclass or under 2025-07-17T09:05:49.8339841Z a TorchDispatchMode. 2025-07-17T09:05:49.8339954Z 2025-07-17T09:05:49.8340127Z Use :func:`torch.library.triton_op` instead of :func:`torch.library.custom_op` 2025-07-17T09:05:49.8340418Z when the implementation 2025-07-17T09:05:49.8340682Z consists of 1+ triton kernels. :func:`torch.library.custom_op` treats 2025-07-17T09:05:49.8341033Z custom operators as opaque (:func:`torch.compile` and 2025-07-17T09:05:49.8341344Z :func:`torch.export.export` will never trace into them), but ``triton_op`` 2025-07-17T09:05:49.8341686Z makes the implementation visible to these subsystems, allowing them 2025-07-17T09:05:49.8341963Z to optimize the triton kernel(s). 2025-07-17T09:05:49.8342106Z 2025-07-17T09:05:49.8342222Z Note that ``fn`` must only consist of calls to PyTorch-understood 2025-07-17T09:05:49.8342556Z operators and triton kernels. Any triton kernels called inside ``fn`` 2025-07-17T09:05:49.8342887Z must be wrapped in a call to :func:`torch.library.wrap_triton`. 2025-07-17T09:05:49.8343074Z 2025-07-17T09:05:49.8343137Z Args: 2025-07-17T09:05:49.8343375Z name (str): A name for the custom op that looks like "{namespace}::{name}", 2025-07-17T09:05:49.8343714Z e.g. "mylib::my_linear". The name is used as the op's stable identifier 2025-07-17T09:05:49.8344023Z in PyTorch subsystems (e.g. torch.export, FX graphs). 2025-07-17T09:05:49.8344342Z To avoid name collisions, please use your project name as the namespace; 2025-07-17T09:05:49.8344696Z e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. 2025-07-17T09:05:49.8345060Z mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. 2025-07-17T09:05:49.8345545Z This MUST be accurate, otherwise, the behavior is undefined. If "unknown", 2025-07-17T09:05:49.8345922Z it pessimistically assumes that all inputs to the operator are being mutated. 2025-07-17T09:05:49.8346275Z schema (None | str): A schema string for the operator. If None 2025-07-17T09:05:49.8346583Z (recommended) we'll infer a schema for the operator from its type 2025-07-17T09:05:49.8346890Z annotations. We recommend letting us infer a schema unless you 2025-07-17T09:05:49.8347169Z have a specific reason not to. 2025-07-17T09:05:49.8347412Z Example: "(Tensor x, int y) -> (Tensor, Tensor)". 2025-07-17T09:05:49.8347578Z 2025-07-17T09:05:49.8347645Z Example:: 2025-07-17T09:05:49.8347748Z 2025-07-17T09:05:49.8347845Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-07-17T09:05:49.8348062Z >>> import torch 2025-07-17T09:05:49.8348278Z >>> from torch.library import triton_op, wrap_triton 2025-07-17T09:05:49.8348501Z >>> 2025-07-17T09:05:49.8348666Z >>> import triton 2025-07-17T09:05:49.8349054Z >>> from triton import language as tl 2025-07-17T09:05:49.8349259Z >>> 2025-07-17T09:05:49.8349412Z >>> @triton.jit 2025-07-17T09:05:49.8349584Z >>> def add_kernel( 2025-07-17T09:05:49.8349770Z >>> in_ptr0, 2025-07-17T09:05:49.8349941Z >>> in_ptr1, 2025-07-17T09:05:49.8350102Z >>> out_ptr, 2025-07-17T09:05:49.8350273Z >>> n_elements, 2025-07-17T09:05:49.8350583Z >>> BLOCK_SIZE: "tl.constexpr", 2025-07-17T09:05:49.8350791Z >>> ): 2025-07-17T09:05:49.8350962Z >>> pid = tl.program_id(axis=0) 2025-07-17T09:05:49.8351170Z >>> block_start = pid * BLOCK_SIZE 2025-07-17T09:05:49.8351407Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2025-07-17T09:05:49.8351642Z >>> mask = offsets < n_elements 2025-07-17T09:05:49.8351848Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2025-07-17T09:05:49.8352072Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2025-07-17T09:05:49.8352292Z >>> output = x + y 2025-07-17T09:05:49.8352498Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2025-07-17T09:05:49.8352713Z >>> 2025-07-17T09:05:49.8352897Z >>> @triton_op("mylib::add", mutates_args={}) 2025-07-17T09:05:49.8353173Z >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: 2025-07-17T09:05:49.8353442Z >>> output = torch.empty_like(x) 2025-07-17T09:05:49.8353667Z >>> n_elements = output.numel() 2025-07-17T09:05:49.8353864Z >>> 2025-07-17T09:05:49.8354023Z >>> def grid(meta): 2025-07-17T09:05:49.8354256Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2025-07-17T09:05:49.8354492Z >>> 2025-07-17T09:05:49.8354704Z >>> # NB: we need to wrap the triton kernel in a call to wrap_triton 2025-07-17T09:05:49.8355004Z >>> wrap_triton(add_kernel)[grid](x, y, output, n_elements, 16) 2025-07-17T09:05:49.8355262Z >>> return output 2025-07-17T09:05:49.8355443Z >>> 2025-07-17T09:05:49.8355598Z >>> @torch.compile 2025-07-17T09:05:49.8355785Z >>> def f(x, y): 2025-07-17T09:05:49.8355998Z >>> return add(x, y) 2025-07-17T09:05:49.8356180Z >>> 2025-07-17T09:05:49.8356346Z >>> x = torch.randn(3, device="cuda") 2025-07-17T09:05:49.8356574Z >>> y = torch.randn(3, device="cuda") 2025-07-17T09:05:49.8356781Z >>> 2025-07-17T09:05:49.8356931Z >>> z = f(x, y) 2025-07-17T09:05:49.8357102Z >>> assert torch.allclose(z, x + y) 2025-07-17T09:05:49.8357241Z 2025-07-17T09:05:49.8357299Z 2025-07-17T09:05:49.8357536Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:49.8357759Z 2025-07-17T09:05:49.8358053Z msg = Cannot scrape callname=wrap_triton in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/triton.py line=202. 2025-07-17T09:05:49.8358560Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:49.8358907Z Allows capture of a triton kernel into a graph via make_fx or 2025-07-17T09:05:49.8359167Z non-strict ``torch.export``. 2025-07-17T09:05:49.8359295Z 2025-07-17T09:05:49.8359416Z These technologies perform Dispatcher-based tracing (via 2025-07-17T09:05:49.8359730Z ``__torch_dispatch__``) and cannot see calls to raw triton kernels. 2025-07-17T09:05:49.8360060Z The ``wrap_triton`` API wraps a triton kernel into a callable that 2025-07-17T09:05:49.8360329Z can actually be traced into a graph. 2025-07-17T09:05:49.8360465Z 2025-07-17T09:05:49.8360606Z Please use this API together with :func:`torch.library.triton_op`. 2025-07-17T09:05:49.8360790Z 2025-07-17T09:05:49.8360867Z Examples: 2025-07-17T09:05:49.8360962Z 2025-07-17T09:05:49.8361044Z >>> # xdoctest: +SKIP 2025-07-17T09:05:49.8361235Z >>> import torch 2025-07-17T09:05:49.8361416Z >>> import triton 2025-07-17T09:05:49.8361769Z >>> from triton import language as tl 2025-07-17T09:05:49.8362037Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2025-07-17T09:05:49.8362305Z >>> from torch.library import wrap_triton 2025-07-17T09:05:49.8362513Z >>> 2025-07-17T09:05:49.8362669Z >>> @triton.jit 2025-07-17T09:05:49.8362841Z >>> def add_kernel( 2025-07-17T09:05:49.8363021Z >>> in_ptr0, 2025-07-17T09:05:49.8363305Z >>> in_ptr1, 2025-07-17T09:05:49.8363478Z >>> out_ptr, 2025-07-17T09:05:49.8363650Z >>> n_elements, 2025-07-17T09:05:49.8363831Z >>> BLOCK_SIZE: "tl.constexpr", 2025-07-17T09:05:49.8364038Z >>> ): 2025-07-17T09:05:49.8364205Z >>> pid = tl.program_id(axis=0) 2025-07-17T09:05:49.8364414Z >>> block_start = pid * BLOCK_SIZE 2025-07-17T09:05:49.8364651Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2025-07-17T09:05:49.8364895Z >>> mask = offsets < n_elements 2025-07-17T09:05:49.8365122Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2025-07-17T09:05:49.8365359Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2025-07-17T09:05:49.8365574Z >>> output = x + y 2025-07-17T09:05:49.8365786Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2025-07-17T09:05:49.8366006Z >>> 2025-07-17T09:05:49.8366158Z >>> def add(x, y): 2025-07-17T09:05:49.8366351Z >>> output = torch.empty_like(x) 2025-07-17T09:05:49.8366569Z >>> n_elements = output.numel() 2025-07-17T09:05:49.8366762Z >>> 2025-07-17T09:05:49.8366916Z >>> def grid_fn(meta): 2025-07-17T09:05:49.8367155Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2025-07-17T09:05:49.8367402Z >>> 2025-07-17T09:05:49.8367614Z >>> wrap_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16) 2025-07-17T09:05:49.8367874Z >>> return output 2025-07-17T09:05:49.8368061Z >>> 2025-07-17T09:05:49.8368224Z >>> x = torch.randn(3, device="cuda") 2025-07-17T09:05:49.8368441Z >>> y = torch.randn(3, device="cuda") 2025-07-17T09:05:49.8368640Z >>> gm = make_fx(add)(x, y) 2025-07-17T09:05:49.8368834Z >>> print(gm.code) 2025-07-17T09:05:49.8369024Z >>> # def forward(self, x_1, y_1): 2025-07-17T09:05:49.8369312Z >>> # empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False) 2025-07-17T09:05:49.8369670Z >>> # triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation( 2025-07-17T09:05:49.8369974Z >>> # kernel_idx = 0, constant_args_idx = 0, 2025-07-17T09:05:49.8370206Z >>> # grid = [(1, 1, 1)], kwargs = { 2025-07-17T09:05:49.8370453Z >>> # 'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like, 2025-07-17T09:05:49.8370696Z >>> # 'n_elements': 3, 'BLOCK_SIZE': 16 2025-07-17T09:05:49.8370914Z >>> # }) 2025-07-17T09:05:49.8371095Z >>> # return empty_like 2025-07-17T09:05:49.8371215Z 2025-07-17T09:05:49.8371283Z 2025-07-17T09:05:49.8371519Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:49.8371731Z 2025-07-17T09:05:49.8380449Z msg = Cannot scrape callname=unsafe_generate_fake_kernels in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_profile.py line=94. 2025-07-17T09:05:49.8381101Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:49.8381335Z 2025-07-17T09:05:49.8381495Z Registers a fake kernel based on the given operator profiles. This fake 2025-07-17T09:05:49.8381877Z kernel registration will override any existing fake kernel registrations. 2025-07-17T09:05:49.8382090Z 2025-07-17T09:05:49.8382235Z The input is a dictionary mapping operator names to a set of operator 2025-07-17T09:05:49.8382575Z profiles, which we will use to generate fake kernels. The operator profiles 2025-07-17T09:05:49.8383150Z are a record of the input and output tensor metadata. Based on this 2025-07-17T09:05:49.8383484Z information we will match a given input to the recorded profile, and return 2025-07-17T09:05:49.8383831Z an output with the same metadata as in the recorded profile. If a profile 2025-07-17T09:05:49.8384127Z doesn't exist then an exception will be thrown. 2025-07-17T09:05:49.8384291Z 2025-07-17T09:05:49.8384571Z The fake kernel generation is considered unsafe because it relies on the 2025-07-17T09:05:49.8384921Z rigid, pre-defined operator profiles that do not account for potential 2025-07-17T09:05:49.8385375Z variations in output behavior. Specifically, the generated kernels assume a 2025-07-17T09:05:49.8385751Z fixed relationship between input and output ranks. However, in reality, it's 2025-07-17T09:05:49.8386116Z possible that data-dependent operations may produce outputs of different 2025-07-17T09:05:49.8386472Z ranks even when given inputs of the same rank. The generated fake kernels 2025-07-17T09:05:49.8386825Z are inflexible and unable to accommodate these nuances, making them 2025-07-17T09:05:49.8387089Z potentially unsafe. 2025-07-17T09:05:49.8387186Z 2025-07-17T09:05:49.8387258Z Args: 2025-07-17T09:05:49.8387477Z op_profiles (dict[str, set[OpProfile]]): A dictionary mapping operator 2025-07-17T09:05:49.8387800Z name to a set of operator profiles from which we will generate fake 2025-07-17T09:05:49.8388049Z kernels. 2025-07-17T09:05:49.8388154Z 2025-07-17T09:05:49.8388219Z Examples: 2025-07-17T09:05:49.8388309Z 2025-07-17T09:05:49.8388425Z >>> # Example: Registering an op-profile from draft-export 2025-07-17T09:05:49.8388666Z >>> import torch 2025-07-17T09:05:49.8388875Z >>> from torch.export._draft_export import draft_export 2025-07-17T09:05:49.8389106Z >>> 2025-07-17T09:05:49.8389312Z >>> @torch.library.custom_op("mylib::foo", mutates_args=()) 2025-07-17T09:05:49.8389573Z >>> def foo(x: Tensor, y: Tensor) -> Tensor: 2025-07-17T09:05:49.8389799Z >>> return x + y 2025-07-17T09:05:49.8389973Z >>> 2025-07-17T09:05:49.8390131Z >>> class M(torch.nn.Module): 2025-07-17T09:05:49.8390333Z >>> def forward(self, a, b): 2025-07-17T09:05:49.8390549Z >>> res = torch.ops.mylib.foo(a, b) # no fake impl 2025-07-17T09:05:49.8390774Z >>> return res 2025-07-17T09:05:49.8390951Z >>> 2025-07-17T09:05:49.8391154Z >>> ep = draft_export(M(), (torch.ones(3, 4), torch.ones(3, 4)) 2025-07-17T09:05:49.8391392Z >>> 2025-07-17T09:05:49.8391661Z >>> with torch._library.fake_profile.unsafe_generate_fake_kernels(ep._report.op_profiles): 2025-07-17T09:05:49.8391994Z >>> decomp = ep.run_decompositions() 2025-07-17T09:05:49.8392134Z 2025-07-17T09:05:49.8392137Z 2025-07-17T09:05:49.8392298Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:49.8392508Z 2025-07-17T09:05:49.8687706Z msg = Cannot scrape callname=assert_close in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_comparison.py line=1331. 2025-07-17T09:05:49.8688414Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:49.8688765Z Asserts that ``actual`` and ``expected`` are close. 2025-07-17T09:05:49.8688945Z 2025-07-17T09:05:49.8689167Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2025-07-17T09:05:49.8689460Z 2025-07-17T09:05:49.8689535Z .. math:: 2025-07-17T09:05:49.8689640Z 2025-07-17T09:05:49.8689867Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2025-07-17T09:05:49.8690145Z 2025-07-17T09:05:49.8690353Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2025-07-17T09:05:49.8690743Z only considered equal to each other if ``equal_nan`` is ``True``. 2025-07-17T09:05:49.8690939Z 2025-07-17T09:05:49.8691445Z In addition, they are only considered close if they have the same 2025-07-17T09:05:49.8691633Z 2025-07-17T09:05:49.8691755Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2025-07-17T09:05:49.8692020Z - ``dtype`` (if ``check_dtype`` is ``True``), 2025-07-17T09:05:49.8692268Z - ``layout`` (if ``check_layout`` is ``True``), and 2025-07-17T09:05:49.8692522Z - stride (if ``check_stride`` is ``True``). 2025-07-17T09:05:49.8692816Z 2025-07-17T09:05:49.8693014Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2025-07-17T09:05:49.8693265Z 2025-07-17T09:05:49.8693491Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2025-07-17T09:05:49.8693980Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2025-07-17T09:05:49.8694404Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2025-07-17T09:05:49.8694632Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2025-07-17T09:05:49.8694635Z 2025-07-17T09:05:49.8694818Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2025-07-17T09:05:49.8695032Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2025-07-17T09:05:49.8695113Z definition above. 2025-07-17T09:05:49.8695116Z 2025-07-17T09:05:49.8695295Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2025-07-17T09:05:49.8695527Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2025-07-17T09:05:49.8695736Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2025-07-17T09:05:49.8695964Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2025-07-17T09:05:49.8696109Z their elements are considered close according to the above definition. 2025-07-17T09:05:49.8696125Z 2025-07-17T09:05:49.8696194Z .. note:: 2025-07-17T09:05:49.8696197Z 2025-07-17T09:05:49.8696408Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2025-07-17T09:05:49.8696600Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2025-07-17T09:05:49.8696776Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2025-07-17T09:05:49.8696779Z 2025-07-17T09:05:49.8696840Z Args: 2025-07-17T09:05:49.8696928Z actual (Any): Actual input. 2025-07-17T09:05:49.8697009Z expected (Any): Expected input. 2025-07-17T09:05:49.8697219Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2025-07-17T09:05:49.8697343Z are allowed. Otherwise type equality is required. 2025-07-17T09:05:49.8697560Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2025-07-17T09:05:49.8697731Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2025-07-17T09:05:49.8697930Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2025-07-17T09:05:49.8698099Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2025-07-17T09:05:49.8698252Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2025-07-17T09:05:49.8698435Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2025-07-17T09:05:49.8698591Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2025-07-17T09:05:49.8698866Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2025-07-17T09:05:49.8699070Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2025-07-17T09:05:49.8699283Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2025-07-17T09:05:49.8699488Z :func:`torch.promote_types`) before being compared. 2025-07-17T09:05:49.8699706Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2025-07-17T09:05:49.8699897Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2025-07-17T09:05:49.8699972Z compared. 2025-07-17T09:05:49.8700178Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2025-07-17T09:05:49.8700397Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2025-07-17T09:05:49.8700602Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2025-07-17T09:05:49.8700695Z should return the new message. 2025-07-17T09:05:49.8700698Z 2025-07-17T09:05:49.8700759Z Raises: 2025-07-17T09:05:49.8700915Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2025-07-17T09:05:49.8701018Z ValueError: If only ``rtol`` or ``atol`` is specified. 2025-07-17T09:05:49.8701218Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2025-07-17T09:05:49.8701422Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2025-07-17T09:05:49.8701505Z different types. 2025-07-17T09:05:49.8701711Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2025-07-17T09:05:49.8701938Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2025-07-17T09:05:49.8702128Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2025-07-17T09:05:49.8702317Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2025-07-17T09:05:49.8702399Z :attr:`~torch.Tensor.layout`. 2025-07-17T09:05:49.8702543Z AssertionError: If only one of corresponding tensors is quantized. 2025-07-17T09:05:49.8702761Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2025-07-17T09:05:49.8702939Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2025-07-17T09:05:49.8703017Z :attr:`~torch.Tensor.device`. 2025-07-17T09:05:49.8703221Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2025-07-17T09:05:49.8703429Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2025-07-17T09:05:49.8703647Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2025-07-17T09:05:49.8703650Z 2025-07-17T09:05:49.8703861Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2025-07-17T09:05:49.8703974Z ``dtype``'s, the maximum of both tolerances is used. 2025-07-17T09:05:49.8703977Z 2025-07-17T09:05:49.8704072Z +---------------------------+------------+----------+ 2025-07-17T09:05:49.8704165Z | ``dtype`` | ``rtol`` | ``atol`` | 2025-07-17T09:05:49.8704234Z +===========================+============+==========+ 2025-07-17T09:05:49.8704440Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2025-07-17T09:05:49.8704523Z +---------------------------+------------+----------+ 2025-07-17T09:05:49.8704619Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2025-07-17T09:05:49.8704703Z +---------------------------+------------+----------+ 2025-07-17T09:05:49.8704794Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2025-07-17T09:05:49.8704973Z +---------------------------+------------+----------+ 2025-07-17T09:05:49.8705070Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2025-07-17T09:05:49.8705150Z +---------------------------+------------+----------+ 2025-07-17T09:05:49.8705251Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2025-07-17T09:05:49.8705456Z +---------------------------+------------+----------+ 2025-07-17T09:05:49.8705546Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2025-07-17T09:05:49.8705627Z +---------------------------+------------+----------+ 2025-07-17T09:05:49.8705720Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2025-07-17T09:05:49.8705799Z +---------------------------+------------+----------+ 2025-07-17T09:05:49.8705892Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2025-07-17T09:05:49.8705973Z +---------------------------+------------+----------+ 2025-07-17T09:05:49.8706072Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2025-07-17T09:05:49.8706158Z +---------------------------+------------+----------+ 2025-07-17T09:05:49.8706238Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2025-07-17T09:05:49.8706332Z +---------------------------+------------+----------+ 2025-07-17T09:05:49.8706413Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2025-07-17T09:05:49.8706506Z +---------------------------+------------+----------+ 2025-07-17T09:05:49.8706591Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2025-07-17T09:05:49.8706677Z +---------------------------+------------+----------+ 2025-07-17T09:05:49.8706759Z | other | ``0.0`` | ``0.0`` | 2025-07-17T09:05:49.8706848Z +---------------------------+------------+----------+ 2025-07-17T09:05:49.8706852Z 2025-07-17T09:05:49.8706918Z .. note:: 2025-07-17T09:05:49.8706922Z 2025-07-17T09:05:49.8707147Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2025-07-17T09:05:49.8707353Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2025-07-17T09:05:49.8707524Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2025-07-17T09:05:49.8707527Z 2025-07-17T09:05:49.8707597Z >>> import functools 2025-07-17T09:05:49.8707758Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2025-07-17T09:05:49.8707831Z >>> assert_equal(1e-9, 1e-10) 2025-07-17T09:05:49.8707926Z Traceback (most recent call last): 2025-07-17T09:05:49.8707988Z ... 2025-07-17T09:05:49.8708084Z AssertionError: Scalars are not equal! 2025-07-17T09:05:49.8708154Z 2025-07-17T09:05:49.8708246Z Expected 1e-10 but got 1e-09. 2025-07-17T09:05:49.8708332Z Absolute difference: 9.000000000000001e-10 2025-07-17T09:05:49.8708414Z Relative difference: 9.0 2025-07-17T09:05:49.8708417Z 2025-07-17T09:05:49.8708479Z Examples: 2025-07-17T09:05:49.8708568Z >>> # tensor to tensor comparison 2025-07-17T09:05:49.8708660Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2025-07-17T09:05:49.8708756Z >>> actual = torch.acos(torch.cos(expected)) 2025-07-17T09:05:49.8708853Z >>> torch.testing.assert_close(actual, expected) 2025-07-17T09:05:49.8708856Z 2025-07-17T09:05:49.8708936Z >>> # scalar to scalar comparison 2025-07-17T09:05:49.8709004Z >>> import math 2025-07-17T09:05:49.8709083Z >>> expected = math.sqrt(2.0) 2025-07-17T09:05:49.8709326Z >>> actual = 2.0 / math.sqrt(2.0) 2025-07-17T09:05:49.8709416Z >>> torch.testing.assert_close(actual, expected) 2025-07-17T09:05:49.8709429Z 2025-07-17T09:05:49.8709515Z >>> # numpy array to numpy array comparison 2025-07-17T09:05:49.8709592Z >>> import numpy as np 2025-07-17T09:05:49.8709685Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2025-07-17T09:05:49.8709908Z >>> actual = np.arccos(np.cos(expected)) 2025-07-17T09:05:49.8710014Z >>> torch.testing.assert_close(actual, expected) 2025-07-17T09:05:49.8710017Z 2025-07-17T09:05:49.8710097Z >>> # sequence to sequence comparison 2025-07-17T09:05:49.8710177Z >>> import numpy as np 2025-07-17T09:05:49.8710333Z >>> # The types of the sequences do not have to match. They only have to have the same 2025-07-17T09:05:49.8710433Z >>> # length and their elements have to match. 2025-07-17T09:05:49.8710537Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2025-07-17T09:05:49.8710628Z >>> actual = tuple(expected) 2025-07-17T09:05:49.8710716Z >>> torch.testing.assert_close(actual, expected) 2025-07-17T09:05:49.8710719Z 2025-07-17T09:05:49.8710807Z >>> # mapping to mapping comparison 2025-07-17T09:05:49.8710891Z >>> from collections import OrderedDict 2025-07-17T09:05:49.8710968Z >>> import numpy as np 2025-07-17T09:05:49.8711043Z >>> foo = torch.tensor(1.0) 2025-07-17T09:05:49.8711114Z >>> bar = 2.0 2025-07-17T09:05:49.8711184Z >>> baz = np.array(3.0) 2025-07-17T09:05:49.8711345Z >>> # The types and a possible ordering of mappings do not have to match. They only 2025-07-17T09:05:49.8711476Z >>> # have to have the same set of keys and their elements have to match. 2025-07-17T09:05:49.8711607Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2025-07-17T09:05:49.8711696Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2025-07-17T09:05:49.8711795Z >>> torch.testing.assert_close(actual, expected) 2025-07-17T09:05:49.8711798Z 2025-07-17T09:05:49.8711880Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2025-07-17T09:05:49.8711960Z >>> actual = expected.clone() 2025-07-17T09:05:49.8712071Z >>> # By default, directly related instances can be compared 2025-07-17T09:05:49.8712210Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2025-07-17T09:05:49.8712335Z >>> # This check can be made more strict with allow_subclasses=False 2025-07-17T09:05:49.8712411Z >>> torch.testing.assert_close( 2025-07-17T09:05:49.8712544Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2025-07-17T09:05:49.8712602Z ... ) 2025-07-17T09:05:49.8712695Z Traceback (most recent call last): 2025-07-17T09:05:49.8712754Z ... 2025-07-17T09:05:49.8712893Z TypeError: No comparison pair was able to handle inputs of type 2025-07-17T09:05:49.8713023Z and . 2025-07-17T09:05:49.8713177Z >>> # If the inputs are not directly related, they are never considered close 2025-07-17T09:05:49.8713283Z >>> torch.testing.assert_close(actual.numpy(), expected) 2025-07-17T09:05:49.8713370Z Traceback (most recent call last): 2025-07-17T09:05:49.8713428Z ... 2025-07-17T09:05:49.8713614Z TypeError: No comparison pair was able to handle inputs of type 2025-07-17T09:05:49.8713687Z and . 2025-07-17T09:05:49.8713854Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2025-07-17T09:05:49.8713937Z >>> # their type if check_dtype=False. 2025-07-17T09:05:49.8714050Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2025-07-17T09:05:49.8714053Z 2025-07-17T09:05:49.8714122Z >>> # NaN != NaN by default. 2025-07-17T09:05:49.8714214Z >>> expected = torch.tensor(float("Nan")) 2025-07-17T09:05:49.8714396Z >>> actual = expected.clone() 2025-07-17T09:05:49.8714501Z >>> torch.testing.assert_close(actual, expected) 2025-07-17T09:05:49.8714577Z Traceback (most recent call last): 2025-07-17T09:05:49.8714646Z ... 2025-07-17T09:05:49.8714727Z AssertionError: Scalars are not close! 2025-07-17T09:05:49.8714788Z 2025-07-17T09:05:49.8714977Z Expected nan but got nan. 2025-07-17T09:05:49.8715072Z Absolute difference: nan (up to 1e-05 allowed) 2025-07-17T09:05:49.8715176Z Relative difference: nan (up to 1.3e-06 allowed) 2025-07-17T09:05:49.8715297Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2025-07-17T09:05:49.8715301Z 2025-07-17T09:05:49.8715395Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2025-07-17T09:05:49.8715474Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2025-07-17T09:05:49.8715581Z >>> # The default error message can be overwritten. 2025-07-17T09:05:49.8715756Z >>> torch.testing.assert_close(actual, expected, msg="Argh, the tensors are not close!") 2025-07-17T09:05:49.8715846Z Traceback (most recent call last): 2025-07-17T09:05:49.8715904Z ... 2025-07-17T09:05:49.8716014Z AssertionError: Argh, the tensors are not close! 2025-07-17T09:05:49.8716148Z >>> # If msg is a callable, it can be used to augment the generated message with 2025-07-17T09:05:49.8716231Z >>> # extra information 2025-07-17T09:05:49.8716308Z >>> torch.testing.assert_close( 2025-07-17T09:05:49.8716446Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2025-07-17T09:05:49.8716504Z ... ) 2025-07-17T09:05:49.8716588Z Traceback (most recent call last): 2025-07-17T09:05:49.8716644Z ... 2025-07-17T09:05:49.8716728Z AssertionError: Header 2025-07-17T09:05:49.8716792Z 2025-07-17T09:05:49.8716863Z Tensor-likes are not close! 2025-07-17T09:05:49.8716940Z 2025-07-17T09:05:49.8717011Z Mismatched elements: 2 / 3 (66.7%) 2025-07-17T09:05:49.8717162Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2025-07-17T09:05:49.8717296Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2025-07-17T09:05:49.8717390Z 2025-07-17T09:05:49.8717454Z Footer 2025-07-17T09:05:49.8717526Z 2025-07-17T09:05:49.8717679Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:49.8717682Z 2025-07-17T09:05:51.2140585Z msg = Cannot scrape callname=print_assert_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=286. 2025-07-17T09:05:51.2141267Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:51.2141500Z 2025-07-17T09:05:51.2141652Z Test if two objects are equal, and print an error message if test fails. 2025-07-17T09:05:51.2141910Z 2025-07-17T09:05:51.2142054Z The test is performed with ``actual == desired``. 2025-07-17T09:05:51.2142213Z 2025-07-17T09:05:51.2142292Z Parameters 2025-07-17T09:05:51.2142448Z ---------- 2025-07-17T09:05:51.2142612Z test_string : str 2025-07-17T09:05:51.2142800Z The message supplied to AssertionError. 2025-07-17T09:05:51.2143004Z actual : object 2025-07-17T09:05:51.2143209Z The object to test for equality against `desired`. 2025-07-17T09:05:51.2143454Z desired : object 2025-07-17T09:05:51.2143624Z The expected result. 2025-07-17T09:05:51.2143745Z 2025-07-17T09:05:51.2143807Z Examples 2025-07-17T09:05:51.2143961Z -------- 2025-07-17T09:05:51.2144133Z >>> np.testing.print_assert_equal( 2025-07-17T09:05:51.2144353Z ... "Test XYZ of func xyz", [0, 1], [0, 1] 2025-07-17T09:05:51.2144562Z ... ) # doctest: +SKIP 2025-07-17T09:05:51.2144754Z >>> np.testing.print_assert_equal( 2025-07-17T09:05:51.2144950Z ... "Test XYZ of func xyz", [0, 1], [0, 2] 2025-07-17T09:05:51.2145964Z ... ) # doctest: +SKIP 2025-07-17T09:05:51.2146135Z Traceback (most recent call last): 2025-07-17T09:05:51.2146322Z ... 2025-07-17T09:05:51.2146494Z AssertionError: Test XYZ of func xyz failed 2025-07-17T09:05:51.2146700Z ACTUAL: 2025-07-17T09:05:51.2146844Z [0, 1] 2025-07-17T09:05:51.2146988Z DESIRED: 2025-07-17T09:05:51.2147129Z [0, 2] 2025-07-17T09:05:51.2147203Z 2025-07-17T09:05:51.2147206Z 2025-07-17T09:05:51.2147599Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:51.2147818Z 2025-07-17T09:05:51.2148163Z msg = Cannot scrape callname=assert_almost_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=331. 2025-07-17T09:05:51.2148699Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:51.2148930Z 2025-07-17T09:05:51.2149070Z Raises an AssertionError if two items are not equal up to desired 2025-07-17T09:05:51.2149328Z precision. 2025-07-17T09:05:51.2149420Z 2025-07-17T09:05:51.2149554Z .. note:: It is recommended to use one of `assert_allclose`, 2025-07-17T09:05:51.2149858Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-07-17T09:05:51.2150158Z instead of this function for more consistent floating point 2025-07-17T09:05:51.2150410Z comparisons. 2025-07-17T09:05:51.2150521Z 2025-07-17T09:05:51.2150653Z The test verifies that the elements of `actual` and `desired` satisfy. 2025-07-17T09:05:51.2150857Z 2025-07-17T09:05:51.2150963Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2025-07-17T09:05:51.2151132Z 2025-07-17T09:05:51.2151274Z That is a looser test than originally documented, but agrees with what the 2025-07-17T09:05:51.2151623Z actual implementation in `assert_array_almost_equal` did up to rounding 2025-07-17T09:05:51.2151966Z vagaries. An exception is raised at conflicting values. For ndarrays this 2025-07-17T09:05:51.2152250Z delegates to assert_array_almost_equal 2025-07-17T09:05:51.2152386Z 2025-07-17T09:05:51.2152457Z Parameters 2025-07-17T09:05:51.2152602Z ---------- 2025-07-17T09:05:51.2152748Z actual : array_like 2025-07-17T09:05:51.2152914Z The object to check. 2025-07-17T09:05:51.2153076Z desired : array_like 2025-07-17T09:05:51.2153244Z The expected object. 2025-07-17T09:05:51.2153427Z decimal : int, optional 2025-07-17T09:05:51.2153614Z Desired precision, default is 7. 2025-07-17T09:05:51.2153811Z err_msg : str, optional 2025-07-17T09:05:51.2154008Z The error message to be printed in case of failure. 2025-07-17T09:05:51.2154241Z verbose : bool, optional 2025-07-17T09:05:51.2154480Z If True, the conflicting values are appended to the error message. 2025-07-17T09:05:51.2154660Z 2025-07-17T09:05:51.2154726Z Raises 2025-07-17T09:05:51.2154862Z ------ 2025-07-17T09:05:51.2155007Z AssertionError 2025-07-17T09:05:51.2155219Z If actual and desired are not equal up to specified precision. 2025-07-17T09:05:51.2155406Z 2025-07-17T09:05:51.2155468Z See Also 2025-07-17T09:05:51.2155626Z -------- 2025-07-17T09:05:51.2155852Z assert_allclose: Compare two array_like objects for equality with desired 2025-07-17T09:05:51.2156147Z relative and/or absolute precision. 2025-07-17T09:05:51.2156424Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-07-17T09:05:51.2156623Z 2025-07-17T09:05:51.2156688Z Examples 2025-07-17T09:05:51.2156840Z -------- 2025-07-17T09:05:51.2157031Z >>> from torch._numpy.testing import assert_almost_equal 2025-07-17T09:05:51.2157285Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2025-07-17T09:05:51.2157557Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2025-07-17T09:05:51.2157814Z Traceback (most recent call last): 2025-07-17T09:05:51.2158001Z ... 2025-07-17T09:05:51.2158152Z AssertionError: 2025-07-17T09:05:51.2158345Z Arrays are not almost equal to 10 decimals 2025-07-17T09:05:51.2158558Z ACTUAL: 2.3333333333333 2025-07-17T09:05:51.2158732Z DESIRED: 2.33333334 2025-07-17T09:05:51.2158985Z 2025-07-17T09:05:51.2159051Z >>> assert_almost_equal( 2025-07-17T09:05:51.2159295Z ... np.array([1.0, 2.3333333333333]), np.array([1.0, 2.33333334]), decimal=9 2025-07-17T09:05:51.2159546Z ... ) 2025-07-17T09:05:51.2159705Z Traceback (most recent call last): 2025-07-17T09:05:51.2159903Z ... 2025-07-17T09:05:51.2160041Z AssertionError: 2025-07-17T09:05:51.2160217Z Arrays are not almost equal to 9 decimals 2025-07-17T09:05:51.2160430Z 2025-07-17T09:05:51.2160711Z Mismatched elements: 1 / 2 (50%) 2025-07-17T09:05:51.2160927Z Max absolute difference: 6.666699636781459e-09 2025-07-17T09:05:51.2161156Z Max relative difference: 2.8571569790287484e-09 2025-07-17T09:05:51.2161399Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2025-07-17T09:05:51.2161640Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2025-07-17T09:05:51.2161780Z 2025-07-17T09:05:51.2161782Z 2025-07-17T09:05:51.2161944Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:51.2162162Z 2025-07-17T09:05:51.2162578Z msg = Cannot scrape callname=assert_approx_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=457. 2025-07-17T09:05:51.2163112Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:51.2163341Z 2025-07-17T09:05:51.2163483Z Raises an AssertionError if two items are not equal up to significant 2025-07-17T09:05:51.2163736Z digits. 2025-07-17T09:05:51.2163832Z 2025-07-17T09:05:51.2163948Z .. note:: It is recommended to use one of `assert_allclose`, 2025-07-17T09:05:51.2164236Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-07-17T09:05:51.2164530Z instead of this function for more consistent floating point 2025-07-17T09:05:51.2164778Z comparisons. 2025-07-17T09:05:51.2164881Z 2025-07-17T09:05:51.2165014Z Given two numbers, check that they are approximately equal. 2025-07-17T09:05:51.2165329Z Approximately equal is defined as the number of significant digits 2025-07-17T09:05:51.2165599Z that agree. 2025-07-17T09:05:51.2165681Z 2025-07-17T09:05:51.2165755Z Parameters 2025-07-17T09:05:51.2165905Z ---------- 2025-07-17T09:05:51.2166046Z actual : scalar 2025-07-17T09:05:51.2166209Z The object to check. 2025-07-17T09:05:51.2166393Z desired : scalar 2025-07-17T09:05:51.2166560Z The expected object. 2025-07-17T09:05:51.2166749Z significant : int, optional 2025-07-17T09:05:51.2166950Z Desired precision, default is 7. 2025-07-17T09:05:51.2167163Z err_msg : str, optional 2025-07-17T09:05:51.2167372Z The error message to be printed in case of failure. 2025-07-17T09:05:51.2167605Z verbose : bool, optional 2025-07-17T09:05:51.2167837Z If True, the conflicting values are appended to the error message. 2025-07-17T09:05:51.2168017Z 2025-07-17T09:05:51.2168086Z Raises 2025-07-17T09:05:51.2168219Z ------ 2025-07-17T09:05:51.2168366Z AssertionError 2025-07-17T09:05:51.2168586Z If actual and desired are not equal up to specified precision. 2025-07-17T09:05:51.2168774Z 2025-07-17T09:05:51.2168834Z See Also 2025-07-17T09:05:51.2168979Z -------- 2025-07-17T09:05:51.2169210Z assert_allclose: Compare two array_like objects for equality with desired 2025-07-17T09:05:51.2169521Z relative and/or absolute precision. 2025-07-17T09:05:51.2169804Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-07-17T09:05:51.2169988Z 2025-07-17T09:05:51.2170063Z Examples 2025-07-17T09:05:51.2170212Z -------- 2025-07-17T09:05:51.2170375Z >>> np.testing.assert_approx_equal( 2025-07-17T09:05:51.2170592Z ... 0.12345677777777e-20, 0.1234567e-20 2025-07-17T09:05:51.2170786Z ... ) # doctest: +SKIP 2025-07-17T09:05:51.2170969Z >>> np.testing.assert_approx_equal( 2025-07-17T09:05:51.2171167Z ... 0.12345670e-20, 2025-07-17T09:05:51.2171344Z ... 0.12345671e-20, # doctest: +SKIP 2025-07-17T09:05:51.2171542Z ... significant=8, 2025-07-17T09:05:51.2171709Z ... ) 2025-07-17T09:05:51.2171866Z >>> np.testing.assert_approx_equal( 2025-07-17T09:05:51.2172225Z ... 0.12345670e-20, 2025-07-17T09:05:51.2172390Z ... 0.12345672e-20, # doctest: +SKIP 2025-07-17T09:05:51.2172579Z ... significant=8, 2025-07-17T09:05:51.2172794Z ... ) 2025-07-17T09:05:51.2172951Z Traceback (most recent call last): 2025-07-17T09:05:51.2173138Z ... 2025-07-17T09:05:51.2173288Z AssertionError: 2025-07-17T09:05:51.2173473Z Items are not equal to 8 significant digits: 2025-07-17T09:05:51.2173803Z ACTUAL: 1.234567e-21 2025-07-17T09:05:51.2173979Z DESIRED: 1.2345672e-21 2025-07-17T09:05:51.2174074Z 2025-07-17T09:05:51.2174188Z the evaluated condition that raises the exception is 2025-07-17T09:05:51.2174347Z 2025-07-17T09:05:51.2174486Z >>> abs(0.12345670e-20 / 1e-21 - 0.12345672e-20 / 1e-21) >= 10 ** -(8 - 1) 2025-07-17T09:05:51.2174735Z True 2025-07-17T09:05:51.2174813Z 2025-07-17T09:05:51.2174816Z 2025-07-17T09:05:51.2174981Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:51.2175198Z 2025-07-17T09:05:51.2175540Z msg = Cannot scrape callname=assert_array_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=744. 2025-07-17T09:05:51.2176094Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:51.2176320Z 2025-07-17T09:05:51.2176451Z Raises an AssertionError if two array_like objects are not equal. 2025-07-17T09:05:51.2176645Z 2025-07-17T09:05:51.2176778Z Given two array_like objects, check that the shape is equal and all 2025-07-17T09:05:51.2177098Z elements of these objects are equal (but see the Notes for the special 2025-07-17T09:05:51.2177595Z handling of a scalar). An exception is raised at shape mismatch or 2025-07-17T09:05:51.2178227Z conflicting values. In contrast to the standard usage in numpy, NaNs 2025-07-17T09:05:51.2178675Z are compared like numbers, no assertion is raised if both objects have 2025-07-17T09:05:51.2179056Z NaNs in the same positions. 2025-07-17T09:05:51.2179193Z 2025-07-17T09:05:51.2179455Z The usual caution for verifying equality with floating point numbers is 2025-07-17T09:05:51.2179836Z advised. 2025-07-17T09:05:51.2179977Z 2025-07-17T09:05:51.2180074Z Parameters 2025-07-17T09:05:51.2188551Z ---------- 2025-07-17T09:05:51.2188741Z x : array_like 2025-07-17T09:05:51.2188930Z The actual object to check. 2025-07-17T09:05:51.2189128Z y : array_like 2025-07-17T09:05:51.2189312Z The desired, expected object. 2025-07-17T09:05:51.2189521Z err_msg : str, optional 2025-07-17T09:05:51.2189739Z The error message to be printed in case of failure. 2025-07-17T09:05:51.2189986Z verbose : bool, optional 2025-07-17T09:05:51.2190244Z If True, the conflicting values are appended to the error message. 2025-07-17T09:05:51.2190518Z strict : bool, optional 2025-07-17T09:05:51.2190758Z If True, raise an AssertionError when either the shape or the data 2025-07-17T09:05:51.2191061Z type of the array_like objects does not match. The special 2025-07-17T09:05:51.2191369Z handling for scalars mentioned in the Notes section is disabled. 2025-07-17T09:05:51.2191554Z 2025-07-17T09:05:51.2191631Z Raises 2025-07-17T09:05:51.2191787Z ------ 2025-07-17T09:05:51.2191944Z AssertionError 2025-07-17T09:05:51.2192124Z If actual and desired objects are not equal. 2025-07-17T09:05:51.2192285Z 2025-07-17T09:05:51.2192348Z See Also 2025-07-17T09:05:51.2192502Z -------- 2025-07-17T09:05:51.2192741Z assert_allclose: Compare two array_like objects for equality with desired 2025-07-17T09:05:51.2193054Z relative and/or absolute precision. 2025-07-17T09:05:51.2193346Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-07-17T09:05:51.2193541Z 2025-07-17T09:05:51.2193601Z Notes 2025-07-17T09:05:51.2193748Z ----- 2025-07-17T09:05:51.2193956Z When one of `x` and `y` is a scalar and the other is array_like, the 2025-07-17T09:05:51.2194286Z function checks that each element of the array_like object is equal to 2025-07-17T09:05:51.2194636Z the scalar. This behaviour can be disabled with the `strict` parameter. 2025-07-17T09:05:51.2195023Z 2025-07-17T09:05:51.2195096Z Examples 2025-07-17T09:05:51.2195249Z -------- 2025-07-17T09:05:51.2195416Z The first assert does not raise an exception: 2025-07-17T09:05:51.2195575Z 2025-07-17T09:05:51.2195661Z >>> np.testing.assert_array_equal( 2025-07-17T09:05:51.2195897Z ... [1.0, 2.33333, np.nan], [np.exp(0), 2.33333, np.nan] 2025-07-17T09:05:51.2196119Z ... ) 2025-07-17T09:05:51.2196213Z 2025-07-17T09:05:51.2196477Z Use `assert_allclose` or one of the nulp (number of floating point values) 2025-07-17T09:05:51.2196768Z functions for these cases instead: 2025-07-17T09:05:51.2196912Z 2025-07-17T09:05:51.2196989Z >>> np.testing.assert_allclose( 2025-07-17T09:05:51.2197252Z ... [1.0, np.pi, np.nan], [1, np.sqrt(np.pi) ** 2, np.nan], rtol=1e-10, atol=0 2025-07-17T09:05:51.2197505Z ... ) 2025-07-17T09:05:51.2197580Z 2025-07-17T09:05:51.2197722Z As mentioned in the Notes section, `assert_array_equal` has special 2025-07-17T09:05:51.2198071Z handling for scalars. Here the test checks that each value in `x` is 3: 2025-07-17T09:05:51.2198266Z 2025-07-17T09:05:51.2198353Z >>> x = np.full((2, 5), fill_value=3) 2025-07-17T09:05:51.2198567Z >>> np.testing.assert_array_equal(x, 3) 2025-07-17T09:05:51.2198711Z 2025-07-17T09:05:51.2198851Z Use `strict` to raise an AssertionError when comparing a scalar with an 2025-07-17T09:05:51.2199090Z array: 2025-07-17T09:05:51.2199182Z 2025-07-17T09:05:51.2199285Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2025-07-17T09:05:51.2199534Z Traceback (most recent call last): 2025-07-17T09:05:51.2199739Z ... 2025-07-17T09:05:51.2199897Z AssertionError: 2025-07-17T09:05:51.2200069Z Arrays are not equal 2025-07-17T09:05:51.2200237Z 2025-07-17T09:05:51.2200401Z (shapes (2, 5), () mismatch) 2025-07-17T09:05:51.2200600Z x: torch.ndarray([[3, 3, 3, 3, 3], 2025-07-17T09:05:51.2200809Z [3, 3, 3, 3, 3]]) 2025-07-17T09:05:51.2200989Z y: torch.ndarray(3) 2025-07-17T09:05:51.2201099Z 2025-07-17T09:05:51.2201234Z The `strict` parameter also ensures that the array data types match: 2025-07-17T09:05:51.2201439Z 2025-07-17T09:05:51.2201511Z >>> x = np.array([2, 2, 2]) 2025-07-17T09:05:51.2201718Z >>> y = np.array([2.0, 2.0, 2.0], dtype=np.float32) 2025-07-17T09:05:51.2201977Z >>> np.testing.assert_array_equal(x, y, strict=True) 2025-07-17T09:05:51.2202224Z Traceback (most recent call last): 2025-07-17T09:05:51.2202419Z ... 2025-07-17T09:05:51.2202576Z AssertionError: 2025-07-17T09:05:51.2202743Z Arrays are not equal 2025-07-17T09:05:51.2202913Z 2025-07-17T09:05:51.2203101Z (dtypes dtype("int64"), dtype("float32") mismatch) 2025-07-17T09:05:51.2203349Z x: torch.ndarray([2, 2, 2]) 2025-07-17T09:05:51.2203538Z y: torch.ndarray([2., 2., 2.]) 2025-07-17T09:05:51.2203669Z 2025-07-17T09:05:51.2203824Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:51.2204055Z 2025-07-17T09:05:51.2204395Z msg = Cannot scrape callname=assert_array_almost_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=851. 2025-07-17T09:05:51.2204953Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:51.2205180Z 2025-07-17T09:05:51.2205316Z Raises an AssertionError if two objects are not equal up to desired 2025-07-17T09:05:51.2205584Z precision. 2025-07-17T09:05:51.2205683Z 2025-07-17T09:05:51.2205804Z .. note:: It is recommended to use one of `assert_allclose`, 2025-07-17T09:05:51.2206090Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-07-17T09:05:51.2206388Z instead of this function for more consistent floating point 2025-07-17T09:05:51.2206642Z comparisons. 2025-07-17T09:05:51.2206745Z 2025-07-17T09:05:51.2206906Z The test verifies identical shapes and that the elements of ``actual`` and 2025-07-17T09:05:51.2207193Z ``desired`` satisfy. 2025-07-17T09:05:51.2207292Z 2025-07-17T09:05:51.2207392Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2025-07-17T09:05:51.2207663Z 2025-07-17T09:05:51.2207817Z That is a looser test than originally documented, but agrees with what the 2025-07-17T09:05:51.2208173Z actual implementation did up to rounding vagaries. An exception is raised 2025-07-17T09:05:51.2208539Z at shape mismatch or conflicting values. In contrast to the standard usage 2025-07-17T09:05:51.2208882Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2025-07-17T09:05:51.2209292Z objects have NaNs in the same positions. 2025-07-17T09:05:51.2209431Z 2025-07-17T09:05:51.2209501Z Parameters 2025-07-17T09:05:51.2209656Z ---------- 2025-07-17T09:05:51.2209809Z x : array_like 2025-07-17T09:05:51.2209984Z The actual object to check. 2025-07-17T09:05:51.2210176Z y : array_like 2025-07-17T09:05:51.2210346Z The desired, expected object. 2025-07-17T09:05:51.2210549Z decimal : int, optional 2025-07-17T09:05:51.2210746Z Desired precision, default is 6. 2025-07-17T09:05:51.2210954Z err_msg : str, optional 2025-07-17T09:05:51.2211170Z The error message to be printed in case of failure. 2025-07-17T09:05:51.2211400Z verbose : bool, optional 2025-07-17T09:05:51.2211643Z If True, the conflicting values are appended to the error message. 2025-07-17T09:05:51.2211839Z 2025-07-17T09:05:51.2211899Z Raises 2025-07-17T09:05:51.2212045Z ------ 2025-07-17T09:05:51.2212194Z AssertionError 2025-07-17T09:05:51.2212418Z If actual and desired are not equal up to specified precision. 2025-07-17T09:05:51.2212609Z 2025-07-17T09:05:51.2212669Z See Also 2025-07-17T09:05:51.2212820Z -------- 2025-07-17T09:05:51.2213049Z assert_allclose: Compare two array_like objects for equality with desired 2025-07-17T09:05:51.2213348Z relative and/or absolute precision. 2025-07-17T09:05:51.2213631Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-07-17T09:05:51.2213823Z 2025-07-17T09:05:51.2213894Z Examples 2025-07-17T09:05:51.2214035Z -------- 2025-07-17T09:05:51.2214208Z the first assert does not raise an exception 2025-07-17T09:05:51.2214365Z 2025-07-17T09:05:51.2214522Z >>> np.testing.assert_array_almost_equal([1.0, 2.333, np.nan], [1.0, 2.333, np.nan]) 2025-07-17T09:05:51.2214752Z 2025-07-17T09:05:51.2214834Z >>> np.testing.assert_array_almost_equal( 2025-07-17T09:05:51.2215087Z ... [1.0, 2.33333, np.nan], [1.0, 2.33339, np.nan], decimal=5 2025-07-17T09:05:51.2215307Z ... ) 2025-07-17T09:05:51.2215470Z Traceback (most recent call last): 2025-07-17T09:05:51.2215674Z ... 2025-07-17T09:05:51.2215829Z AssertionError: 2025-07-17T09:05:51.2216009Z Arrays are not almost equal to 5 decimals 2025-07-17T09:05:51.2216221Z 2025-07-17T09:05:51.2216414Z Mismatched elements: 1 / 3 (33.3%) 2025-07-17T09:05:51.2216622Z Max absolute difference: 5.999999999994898e-05 2025-07-17T09:05:51.2216863Z Max relative difference: 2.5713661239633743e-05 2025-07-17T09:05:51.2217129Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2025-07-17T09:05:51.2217400Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2025-07-17T09:05:51.2217574Z 2025-07-17T09:05:51.2217656Z >>> np.testing.assert_array_almost_equal( 2025-07-17T09:05:51.2217895Z ... [1.0, 2.33333, np.nan], [1.0, 2.33333, 5], decimal=5 2025-07-17T09:05:51.2218113Z ... ) 2025-07-17T09:05:51.2218279Z Traceback (most recent call last): 2025-07-17T09:05:51.2218473Z ... 2025-07-17T09:05:51.2218628Z AssertionError: 2025-07-17T09:05:51.2218805Z Arrays are not almost equal to 5 decimals 2025-07-17T09:05:51.2218998Z 2025-07-17T09:05:51.2219171Z x and y nan location mismatch: 2025-07-17T09:05:51.2219407Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2025-07-17T09:05:51.2219678Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2025-07-17T09:05:51.2219843Z 2025-07-17T09:05:51.2219845Z 2025-07-17T09:05:51.2220002Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:51.2220229Z 2025-07-17T09:05:51.2220594Z msg = Cannot scrape callname=clear_and_catch_warnings in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=1848. 2025-07-17T09:05:51.2221295Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:51.2221654Z Context manager that resets warning registry for catching warnings 2025-07-17T09:05:51.2221856Z 2025-07-17T09:05:51.2222004Z Warnings can be slippery, because, whenever a warning is triggered, Python 2025-07-17T09:05:51.2222453Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2025-07-17T09:05:51.2222806Z it impossible to retrigger the warning in this module, whatever you put in 2025-07-17T09:05:51.2223162Z the warnings filters. This context manager accepts a sequence of `modules` 2025-07-17T09:05:51.2223475Z as a keyword argument to its constructor and: 2025-07-17T09:05:51.2223622Z 2025-07-17T09:05:51.2223771Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2025-07-17T09:05:51.2224037Z on entry; 2025-07-17T09:05:51.2224250Z * resets ``__warningregistry__`` to its previous state on exit. 2025-07-17T09:05:51.2224423Z 2025-07-17T09:05:51.2224574Z This makes it possible to trigger any warning afresh inside the context 2025-07-17T09:05:51.2224894Z manager without disturbing the state of warnings outside. 2025-07-17T09:05:51.2225069Z 2025-07-17T09:05:51.2225225Z For compatibility with Python 3.0, please consider all arguments to be 2025-07-17T09:05:51.2225582Z keyword-only. 2025-07-17T09:05:51.2225676Z 2025-07-17T09:05:51.2225750Z Parameters 2025-07-17T09:05:51.2225901Z ---------- 2025-07-17T09:05:51.2226062Z record : bool, optional 2025-07-17T09:05:51.2226300Z Specifies whether warnings should be captured by a custom 2025-07-17T09:05:51.2227072Z implementation of ``warnings.showwarning()`` and be appended to a list 2025-07-17T09:05:51.2227419Z returned by the context manager. Otherwise None is returned by the 2025-07-17T09:05:51.2227770Z context manager. The objects appended to the list are arguments whose 2025-07-17T09:05:51.2228085Z attributes mirror the arguments to ``showwarning()``. 2025-07-17T09:05:51.2228339Z modules : sequence, optional 2025-07-17T09:05:51.2228599Z Sequence of modules for which to reset warnings registry on entry and 2025-07-17T09:05:51.2228915Z restore on exit. To work correctly, all 'ignore' filters should 2025-07-17T09:05:51.2229181Z filter by one of these modules. 2025-07-17T09:05:51.2229308Z 2025-07-17T09:05:51.2229379Z Examples 2025-07-17T09:05:51.2229534Z -------- 2025-07-17T09:05:51.2229684Z >>> import warnings 2025-07-17T09:05:51.2229913Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2025-07-17T09:05:51.2230177Z ... modules=[np.core.fromnumeric] 2025-07-17T09:05:51.2230374Z ... ): 2025-07-17T09:05:51.2230549Z ... warnings.simplefilter("always") 2025-07-17T09:05:51.2230823Z ... warnings.filterwarnings("ignore", module="np.core.fromnumeric") 2025-07-17T09:05:51.2231148Z ... # do something that raises a warning but ignore those in 2025-07-17T09:05:51.2231400Z ... # np.core.fromnumeric 2025-07-17T09:05:51.2231599Z 2025-07-17T09:05:51.2231841Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:51.2232068Z 2025-07-17T09:05:51.2470219Z msg = Cannot scrape callname=ThroughputBenchmark in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/throughput_benchmark.py line=61. 2025-07-17T09:05:51.2470887Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:51.2471142Z 2025-07-17T09:05:51.2471345Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2025-07-17T09:05:51.2471596Z 2025-07-17T09:05:51.2471779Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2025-07-17T09:05:51.2472188Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2025-07-17T09:05:51.2472910Z server like load. It can emulate multiple calling threads to a single module 2025-07-17T09:05:51.2473260Z provided. In the future we plan to enhance this component to support inter and 2025-07-17T09:05:51.2473625Z intra-op parallelism as well as multiple models running in a single process. 2025-07-17T09:05:51.2473830Z 2025-07-17T09:05:51.2474150Z Please note that even though nn.Module is supported, it might incur an overhead 2025-07-17T09:05:51.2474508Z from the need to hold GIL every time we execute Python code or pass around 2025-07-17T09:05:51.2474881Z inputs as Python objects. As soon as you have a ScriptModule version of your 2025-07-17T09:05:51.2475229Z model for inference deployment it is better to switch to using it in this 2025-07-17T09:05:51.2475500Z benchmark. 2025-07-17T09:05:51.2475588Z 2025-07-17T09:05:51.2475665Z Example:: 2025-07-17T09:05:51.2475744Z 2025-07-17T09:05:51.2475842Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T09:05:51.2476081Z >>> from torch.utils import ThroughputBenchmark 2025-07-17T09:05:51.2476330Z >>> bench = ThroughputBenchmark(my_module) 2025-07-17T09:05:51.2476590Z >>> # Pre-populate benchmark's data set with the inputs 2025-07-17T09:05:51.2476834Z >>> for input in inputs: 2025-07-17T09:05:51.2477116Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2025-07-17T09:05:51.2477413Z ... bench.add_input(input[0], x2=input[1]) 2025-07-17T09:05:51.2477683Z >>> # Inputs supplied above are randomly used during the execution 2025-07-17T09:05:51.2477947Z >>> stats = bench.benchmark( 2025-07-17T09:05:51.2478142Z ... num_calling_threads=4, 2025-07-17T09:05:51.2478350Z ... num_warmup_iters = 100, 2025-07-17T09:05:51.2478554Z ... num_iters = 1000, 2025-07-17T09:05:51.2478727Z ... ) 2025-07-17T09:05:51.2478922Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2025-07-17T09:05:51.2479211Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2025-07-17T09:05:51.2479391Z 2025-07-17T09:05:51.2479541Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:51.2479757Z 2025-07-17T09:05:51.2706019Z msg = Cannot scrape callname=CppExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1147. 2025-07-17T09:05:51.2706669Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:51.2706912Z 2025-07-17T09:05:51.2707019Z Create a :class:`setuptools.Extension` for C++. 2025-07-17T09:05:51.2707181Z 2025-07-17T09:05:51.2707329Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-07-17T09:05:51.2707684Z bare minimum (but often sufficient) arguments to build a C++ extension. 2025-07-17T09:05:51.2707893Z 2025-07-17T09:05:51.2708022Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-07-17T09:05:51.2708312Z constructor. Full list arguments can be found at 2025-07-17T09:05:51.2708703Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2025-07-17T09:05:51.2708966Z 2025-07-17T09:05:51.2709038Z .. warning:: 2025-07-17T09:05:51.2709272Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-07-17T09:05:51.2709600Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-07-17T09:05:51.2709916Z the user's responsibility in their library to not use APIs from 2025-07-17T09:05:51.2710283Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-07-17T09:05:51.2710626Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-07-17T09:05:51.2710958Z example, to give access to custom ops from python, the library should 2025-07-17T09:05:51.2711239Z register the ops through the dispatcher. 2025-07-17T09:05:51.2711382Z 2025-07-17T09:05:51.2711528Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-07-17T09:05:51.2712238Z as a compile flag when py_limited_api is specified as an option for 2025-07-17T09:05:51.2712549Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-07-17T09:05:51.2712862Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-07-17T09:05:51.2713200Z safety, and sanity in order to encourage best practices. To target a 2025-07-17T09:05:51.2713685Z different version, set min_supported_cpython to the hexcode of the 2025-07-17T09:05:51.2713964Z CPython version of choice. 2025-07-17T09:05:51.2714123Z 2025-07-17T09:05:51.2714183Z Example: 2025-07-17T09:05:51.2714352Z >>> # xdoctest: +SKIP 2025-07-17T09:05:51.2714573Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-07-17T09:05:51.2714818Z >>> from setuptools import setup 2025-07-17T09:05:51.2715084Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2025-07-17T09:05:51.2715343Z >>> setup( 2025-07-17T09:05:51.2715515Z ... name='extension', 2025-07-17T09:05:51.2715688Z ... ext_modules=[ 2025-07-17T09:05:51.2715861Z ... CppExtension( 2025-07-17T09:05:51.2716044Z ... name='extension', 2025-07-17T09:05:51.2716247Z ... sources=['extension.cpp'], 2025-07-17T09:05:51.2716459Z ... extra_compile_args=['-g'], 2025-07-17T09:05:51.2716694Z ... extra_link_args=['-Wl,--no-as-needed', '-lm']) 2025-07-17T09:05:51.2716918Z ... ], 2025-07-17T09:05:51.2717072Z ... cmdclass={ 2025-07-17T09:05:51.2717255Z ... 'build_ext': BuildExtension 2025-07-17T09:05:51.2717446Z ... }) 2025-07-17T09:05:51.2717527Z 2025-07-17T09:05:51.2717685Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:51.2717891Z 2025-07-17T09:05:51.2718258Z msg = Cannot scrape callname=CUDAExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1217. 2025-07-17T09:05:51.2718786Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:51.2719004Z 2025-07-17T09:05:51.2719108Z Create a :class:`setuptools.Extension` for CUDA/C++. 2025-07-17T09:05:51.2719269Z 2025-07-17T09:05:51.2719413Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-07-17T09:05:51.2719742Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2025-07-17T09:05:51.2720081Z extension. This includes the CUDA include path, library path and runtime 2025-07-17T09:05:51.2720340Z library. 2025-07-17T09:05:51.2720416Z 2025-07-17T09:05:51.2720555Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-07-17T09:05:51.2720856Z constructor. Full list arguments can be found at 2025-07-17T09:05:51.2721216Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2025-07-17T09:05:51.2721463Z 2025-07-17T09:05:51.2721533Z .. warning:: 2025-07-17T09:05:51.2721774Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-07-17T09:05:51.2722103Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-07-17T09:05:51.2722423Z the user's responsibility in their library to not use APIs from 2025-07-17T09:05:51.2722741Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-07-17T09:05:51.2723072Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-07-17T09:05:51.2723395Z example, to give access to custom ops from python, the library should 2025-07-17T09:05:51.2723671Z register the ops through the dispatcher. 2025-07-17T09:05:51.2723813Z 2025-07-17T09:05:51.2723950Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-07-17T09:05:51.2724273Z as a compile flag when py_limited_api is specified as an option for 2025-07-17T09:05:51.2724582Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-07-17T09:05:51.2724907Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-07-17T09:05:51.2725370Z safety, and sanity in order to encourage best practices. To target a 2025-07-17T09:05:51.2725692Z different version, set min_supported_cpython to the hexcode of the 2025-07-17T09:05:51.2725960Z CPython version of choice. 2025-07-17T09:05:51.2726076Z 2025-07-17T09:05:51.2726145Z Example: 2025-07-17T09:05:51.2726300Z >>> # xdoctest: +SKIP 2025-07-17T09:05:51.2726613Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-07-17T09:05:51.2726850Z >>> from setuptools import setup 2025-07-17T09:05:51.2727123Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2025-07-17T09:05:51.2727387Z >>> setup( 2025-07-17T09:05:51.2727554Z ... name='cuda_extension', 2025-07-17T09:05:51.2727750Z ... ext_modules=[ 2025-07-17T09:05:51.2727935Z ... CUDAExtension( 2025-07-17T09:05:51.2728133Z ... name='cuda_extension', 2025-07-17T09:05:51.2728392Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-07-17T09:05:51.2728673Z ... extra_compile_args={'cxx': ['-g'], 2025-07-17T09:05:51.2728902Z ... 'nvcc': ['-O2']}, 2025-07-17T09:05:51.2729153Z ... extra_link_args=['-Wl,--no-as-needed', '-lcuda']) 2025-07-17T09:05:51.2729368Z ... ], 2025-07-17T09:05:51.2729516Z ... cmdclass={ 2025-07-17T09:05:51.2729711Z ... 'build_ext': BuildExtension 2025-07-17T09:05:51.2729905Z ... }) 2025-07-17T09:05:51.2729994Z 2025-07-17T09:05:51.2730064Z Compute capabilities: 2025-07-17T09:05:51.2730166Z 2025-07-17T09:05:51.2730340Z By default the extension will be compiled to run on all archs of the cards visible during the 2025-07-17T09:05:51.2730759Z building process of the extension, plus PTX. If down the road a new card is installed the 2025-07-17T09:05:51.2731172Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2025-07-17T09:05:51.2731591Z newer than the newest version for which your nvcc can build fully-compiled binaries, PyTorch 2025-07-17T09:05:51.2732001Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2025-07-17T09:05:51.2732307Z support (see below for details on PTX). 2025-07-17T09:05:51.2732445Z 2025-07-17T09:05:51.2732634Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2025-07-17T09:05:51.2732965Z CCs you want the extension to support: 2025-07-17T09:05:51.2733099Z 2025-07-17T09:05:51.2733232Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2025-07-17T09:05:51.2733554Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2025-07-17T09:05:51.2733752Z 2025-07-17T09:05:51.2733943Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2025-07-17T09:05:51.2734369Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2025-07-17T09:05:51.2734780Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2025-07-17T09:05:51.2735193Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2025-07-17T09:05:51.2735611Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2025-07-17T09:05:51.2736026Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2025-07-17T09:05:51.2736431Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2025-07-17T09:05:51.2736859Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2025-07-17T09:05:51.2737171Z "8.0 8.6" would be better. 2025-07-17T09:05:51.2737290Z 2025-07-17T09:05:51.2737461Z Note that while it's possible to include all supported archs, the more archs get included the 2025-07-17T09:05:51.2738064Z slower the building process will be, as it will build a separate kernel image for each arch. 2025-07-17T09:05:51.2738294Z 2025-07-17T09:05:51.2738491Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2025-07-17T09:05:51.2738881Z To workaround the issue, move python binding logic to pure C++ file. 2025-07-17T09:05:51.2739075Z 2025-07-17T09:05:51.2739140Z Example use: 2025-07-17T09:05:51.2739441Z #include 2025-07-17T09:05:51.2739664Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2025-07-17T09:05:51.2739822Z 2025-07-17T09:05:51.2739896Z Instead of: 2025-07-17T09:05:51.2740060Z #include 2025-07-17T09:05:51.2740281Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2025-07-17T09:05:51.2740437Z 2025-07-17T09:05:51.2740609Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2025-07-17T09:05:51.2741123Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2025-07-17T09:05:51.2741479Z 2025-07-17T09:05:51.2741553Z Relocatable device code linking: 2025-07-17T09:05:51.2741680Z 2025-07-17T09:05:51.2741847Z If you want to reference device symbols across compilation units (across object files), 2025-07-17T09:05:51.2742231Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2025-07-17T09:05:51.2742667Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2025-07-17T09:05:51.2743120Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2025-07-17T09:05:51.2743573Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2025-07-17T09:05:51.2743945Z helps reduce the protentional perf degradation of `-rdc`. 2025-07-17T09:05:51.2744222Z Note that it needs to be used at both steps to be useful. 2025-07-17T09:05:51.2744381Z 2025-07-17T09:05:51.2744596Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2025-07-17T09:05:51.2744983Z There is also a case where `-dlink` is used without `-rdc`: 2025-07-17T09:05:51.2745398Z when an extension is linked against a static lib containing rdc-compiled objects 2025-07-17T09:05:51.2745753Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2025-07-17T09:05:51.2745941Z 2025-07-17T09:05:51.2746085Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2025-07-17T09:05:51.2746268Z 2025-07-17T09:05:51.2746336Z Example: 2025-07-17T09:05:51.2746496Z >>> # xdoctest: +SKIP 2025-07-17T09:05:51.2746698Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-07-17T09:05:51.2746917Z >>> CUDAExtension( 2025-07-17T09:05:51.2747087Z ... name='cuda_extension', 2025-07-17T09:05:51.2747322Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-07-17T09:05:51.2747554Z ... dlink=True, 2025-07-17T09:05:51.2747747Z ... dlink_libraries=["dlink_lib"], 2025-07-17T09:05:51.2747965Z ... extra_compile_args={'cxx': ['-g'], 2025-07-17T09:05:51.2748212Z ... 'nvcc': ['-O2', '-rdc=true']}) 2025-07-17T09:05:51.2748356Z 2025-07-17T09:05:51.2748513Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:51.2748724Z 2025-07-17T09:05:51.2749055Z msg = Cannot scrape callname=SyclExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1408. 2025-07-17T09:05:51.2749580Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:51.2749796Z 2025-07-17T09:05:51.2749915Z Creates a :class:`setuptools.Extension` for SYCL/C++. 2025-07-17T09:05:51.2750074Z 2025-07-17T09:05:51.2750231Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-07-17T09:05:51.2750564Z bare minimum (but often sufficient) arguments to build a SYCL/C++ 2025-07-17T09:05:51.2750977Z extension. 2025-07-17T09:05:51.2751062Z 2025-07-17T09:05:51.2751198Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-07-17T09:05:51.2751448Z constructor. 2025-07-17T09:05:51.2751536Z 2025-07-17T09:05:51.2751611Z .. warning:: 2025-07-17T09:05:51.2751835Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-07-17T09:05:51.2752293Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-07-17T09:05:51.2752618Z the user's responsibility in their library to not use APIs from 2025-07-17T09:05:51.2752942Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-07-17T09:05:51.2753272Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-07-17T09:05:51.2753604Z example, to give access to custom ops from python, the library should 2025-07-17T09:05:51.2753882Z register the ops through the dispatcher. 2025-07-17T09:05:51.2754034Z 2025-07-17T09:05:51.2754175Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-07-17T09:05:51.2754511Z as a compile flag when py_limited_api is specified as an option for 2025-07-17T09:05:51.2754833Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-07-17T09:05:51.2755157Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-07-17T09:05:51.2755486Z safety, and sanity in order to encourage best practices. To target a 2025-07-17T09:05:51.2755815Z different version, set min_supported_cpython to the hexcode of the 2025-07-17T09:05:51.2756085Z CPython version of choice. 2025-07-17T09:05:51.2756204Z 2025-07-17T09:05:51.2756279Z Example: 2025-07-17T09:05:51.2756426Z >>> # xdoctest: +SKIP 2025-07-17T09:05:51.2756639Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-07-17T09:05:51.2756929Z >>> from torch.utils.cpp_extension import BuildExtension, SyclExtension 2025-07-17T09:05:51.2757184Z >>> setup( 2025-07-17T09:05:51.2757357Z ... name='xpu_extension', 2025-07-17T09:05:51.2757550Z ... ext_modules=[ 2025-07-17T09:05:51.2757729Z ... SyclExtension( 2025-07-17T09:05:51.2757927Z ... name='xpu_extension', 2025-07-17T09:05:51.2758174Z ... sources=['extension.cpp', 'extension_kernel.cpp'], 2025-07-17T09:05:51.2758459Z ... extra_compile_args={'cxx': ['-g', '-std=c++20', '-fPIC']}) 2025-07-17T09:05:51.2758706Z ... ], 2025-07-17T09:05:51.2758856Z ... cmdclass={ 2025-07-17T09:05:51.2759044Z ... 'build_ext': BuildExtension 2025-07-17T09:05:51.2759237Z ... }) 2025-07-17T09:05:51.2759330Z 2025-07-17T09:05:51.2759512Z By default the extension will be compiled to run on all archs of the cards visible during the 2025-07-17T09:05:51.2759919Z building process of the extension. If down the road a new card is installed the 2025-07-17T09:05:51.2760287Z extension may need to be recompiled. You can override the default behavior using 2025-07-17T09:05:51.2760680Z `TORCH_XPU_ARCH_LIST` to explicitly specify which device architectures you want the extension 2025-07-17T09:05:51.2760985Z to support: 2025-07-17T09:05:51.2761070Z 2025-07-17T09:05:51.2761207Z ``TORCH_XPU_ARCH_LIST="pvc,xe-lpg" python build_my_extension.py`` 2025-07-17T09:05:51.2761387Z 2025-07-17T09:05:51.2761570Z Note that while it's possible to include all supported archs, the more archs get included the 2025-07-17T09:05:51.2761983Z slower the building process will be, as it will build a separate kernel image for each arch. 2025-07-17T09:05:51.2762212Z 2025-07-17T09:05:51.2762317Z Note: Ninja is required to build SyclExtension. 2025-07-17T09:05:51.2762462Z 2025-07-17T09:05:51.2762621Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:51.2762827Z 2025-07-17T09:05:51.2763178Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1585. 2025-07-17T09:05:51.2763820Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:51.2764044Z 2025-07-17T09:05:51.2764137Z Load a PyTorch C++ extension just-in-time (JIT). 2025-07-17T09:05:51.2764295Z 2025-07-17T09:05:51.2764426Z To load an extension, a Ninja build file is emitted, which is used to 2025-07-17T09:05:51.2764746Z compile the given sources into a dynamic library. This library is 2025-07-17T09:05:51.2765176Z subsequently loaded into the current Python process as a module and 2025-07-17T09:05:51.2765466Z returned from this function, ready for use. 2025-07-17T09:05:51.2765610Z 2025-07-17T09:05:51.2765736Z By default, the directory to which the build file is emitted and the 2025-07-17T09:05:51.2766075Z resulting library compiled to is ``/torch_extensions/``, where 2025-07-17T09:05:51.2766408Z ```` is the temporary folder on the current platform and ```` 2025-07-17T09:05:51.2766726Z the name of the extension. This location can be overridden in two ways. 2025-07-17T09:05:51.2767053Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2025-07-17T09:05:51.2767370Z replaces ``/torch_extensions`` and all extensions will be compiled 2025-07-17T09:05:51.2767694Z into subfolders of this directory. Second, if the ``build_directory`` 2025-07-17T09:05:51.2768030Z argument to this function is supplied, it overrides the entire path, i.e. 2025-07-17T09:05:51.2768332Z the library will be compiled into that folder directly. 2025-07-17T09:05:51.2768503Z 2025-07-17T09:05:51.2768630Z To compile the sources, the default system compiler (``c++``) is used, 2025-07-17T09:05:51.2768967Z which can be overridden by setting the ``CXX`` environment variable. To pass 2025-07-17T09:05:51.2769324Z additional arguments to the compilation process, ``extra_cflags`` or 2025-07-17T09:05:51.2769656Z ``extra_ldflags`` can be provided. For example, to compile your extension 2025-07-17T09:05:51.2769981Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2025-07-17T09:05:51.2770284Z ``extra_cflags`` to pass further include directories. 2025-07-17T09:05:51.2770452Z 2025-07-17T09:05:51.2770596Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2025-07-17T09:05:51.2770917Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2025-07-17T09:05:51.2771233Z detected and compiled with nvcc rather than the C++ compiler. This includes 2025-07-17T09:05:51.2771580Z passing the CUDA lib64 directory as a library directory, and linking 2025-07-17T09:05:51.2771865Z ``cudart``. You can pass additional flags to nvcc via 2025-07-17T09:05:51.2772146Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2025-07-17T09:05:51.2772474Z heuristics for finding the CUDA install directory are used, which usually 2025-07-17T09:05:51.2772801Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2025-07-17T09:05:51.2773050Z safest option. 2025-07-17T09:05:51.2773141Z 2025-07-17T09:05:51.2773285Z SYCL support with mixed compilation is provided. Simply pass SYCL source 2025-07-17T09:05:51.2773612Z files (``.sycl``) along with other sources. Such files will be detected 2025-07-17T09:05:51.2773928Z and compiled with SYCL compiler (such as Intel DPC++ Compiler) rather 2025-07-17T09:05:51.2774246Z than the C++ compiler. You can pass additional flags to SYCL compiler 2025-07-17T09:05:51.2774552Z via ``extra_sycl_cflags``, just like with ``extra_cflags`` for C++. 2025-07-17T09:05:51.2774862Z SYCL compiler is expected to be found via system PATH environment 2025-07-17T09:05:51.2775116Z variable. 2025-07-17T09:05:51.2775208Z 2025-07-17T09:05:51.2775270Z Args: 2025-07-17T09:05:51.2775490Z name: The name of the extension to build. This MUST be the same as the 2025-07-17T09:05:51.2775756Z name of the pybind11 module! 2025-07-17T09:05:51.2776034Z sources: A list of relative or absolute paths to C++ source files. 2025-07-17T09:05:51.2776366Z extra_cflags: optional list of compiler flags to forward to the build. 2025-07-17T09:05:51.2776841Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2025-07-17T09:05:51.2777114Z when building CUDA sources. 2025-07-17T09:05:51.2777360Z extra_sycl_cflags: optional list of compiler flags to forward to SYCL 2025-07-17T09:05:51.2777635Z compiler when building SYCL sources. 2025-07-17T09:05:51.2778005Z extra_ldflags: optional list of linker flags to forward to the build. 2025-07-17T09:05:51.2778335Z extra_include_paths: optional list of include directories to forward 2025-07-17T09:05:51.2778597Z to the build. 2025-07-17T09:05:51.2778817Z build_directory: optional path to use as build workspace. 2025-07-17T09:05:51.2779104Z verbose: If ``True``, turns on verbose logging of load steps. 2025-07-17T09:05:51.2779414Z with_cuda: Determines whether CUDA headers and libraries are added to 2025-07-17T09:05:51.2779716Z the build. If set to ``None`` (default), this value is 2025-07-17T09:05:51.2780017Z automatically determined based on the existence of ``.cu`` or 2025-07-17T09:05:51.2780310Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2025-07-17T09:05:51.2780549Z and libraries to be included. 2025-07-17T09:05:51.2780810Z with_sycl: Determines whether SYCL headers and libraries are added to 2025-07-17T09:05:51.2781088Z the build. If set to ``None`` (default), this value is 2025-07-17T09:05:51.2781380Z automatically determined based on the existence of ``.sycl`` in 2025-07-17T09:05:51.2781673Z ``sources``. Set it to `True`` to force SYCL headers and 2025-07-17T09:05:51.2781915Z libraries to be included. 2025-07-17T09:05:51.2782166Z is_python_module: If ``True`` (default), imports the produced shared 2025-07-17T09:05:51.2782462Z library as a Python module. If ``False``, behavior depends on 2025-07-17T09:05:51.2782712Z ``is_standalone``. 2025-07-17T09:05:51.2782955Z is_standalone: If ``False`` (default) loads the constructed extension 2025-07-17T09:05:51.2783260Z into the process as a plain dynamic library. If ``True``, build a 2025-07-17T09:05:51.2783513Z standalone executable. 2025-07-17T09:05:51.2783625Z 2025-07-17T09:05:51.2783693Z Returns: 2025-07-17T09:05:51.2783854Z If ``is_python_module`` is ``True``: 2025-07-17T09:05:51.2784102Z Returns the loaded PyTorch extension as a Python module. 2025-07-17T09:05:51.2784288Z 2025-07-17T09:05:51.2784425Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2025-07-17T09:05:51.2784737Z Returns nothing. (The shared library is loaded into the process as 2025-07-17T09:05:51.2784995Z a side effect.) 2025-07-17T09:05:51.2785107Z 2025-07-17T09:05:51.2785180Z If ``is_standalone`` is ``True``. 2025-07-17T09:05:51.2785508Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2025-07-17T09:05:51.2785797Z added to the PATH environment variable as a side effect.) 2025-07-17T09:05:51.2785977Z 2025-07-17T09:05:51.2786040Z Example: 2025-07-17T09:05:51.2786196Z >>> # xdoctest: +SKIP 2025-07-17T09:05:51.2786396Z >>> from torch.utils.cpp_extension import load 2025-07-17T09:05:51.2786611Z >>> module = load( 2025-07-17T09:05:51.2786790Z ... name='extension', 2025-07-17T09:05:51.2787007Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-07-17T09:05:51.2787252Z ... extra_cflags=['-O2'], 2025-07-17T09:05:51.2787433Z ... verbose=True) 2025-07-17T09:05:51.2787545Z 2025-07-17T09:05:51.2787691Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:51.2787912Z 2025-07-17T09:05:51.2788223Z msg = Cannot scrape callname=load_inline in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1890. 2025-07-17T09:05:51.2788739Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:51.2788964Z 2025-07-17T09:05:51.2789265Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2025-07-17T09:05:51.2789464Z 2025-07-17T09:05:51.2789607Z This function behaves exactly like :func:`load`, but takes its sources as 2025-07-17T09:05:51.2789951Z strings rather than filenames. These strings are stored to files in the 2025-07-17T09:05:51.2790286Z build directory, after which the behavior of :func:`load_inline` is 2025-07-17T09:05:51.2790549Z identical to :func:`load`. 2025-07-17T09:05:51.2790814Z 2025-07-17T09:05:51.2790877Z See `the 2025-07-17T09:05:51.2791154Z tests `_ 2025-07-17T09:05:51.2791493Z for good examples of using this function. 2025-07-17T09:05:51.2791622Z 2025-07-17T09:05:51.2791778Z Sources may omit two required parts of a typical non-inline C++ extension: 2025-07-17T09:05:51.2792129Z the necessary header includes, as well as the (pybind11) binding code. More 2025-07-17T09:05:51.2792478Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2025-07-17T09:05:51.2792812Z single ``.cpp`` file. This file is then prepended with ``#include 2025-07-17T09:05:51.2793059Z `` 2025-07-17T09:05:51.2793159Z 2025-07-17T09:05:51.2793300Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2025-07-17T09:05:51.2793640Z automatically generated for each function specified. ``functions`` can 2025-07-17T09:05:51.2793967Z either be a list of function names, or a dictionary mapping from function 2025-07-17T09:05:51.2794296Z names to docstrings. If a list is given, the name of each function is used 2025-07-17T09:05:51.2794556Z as its docstring. 2025-07-17T09:05:51.2794655Z 2025-07-17T09:05:51.2794780Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2025-07-17T09:05:51.2795084Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2025-07-17T09:05:51.2795383Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2025-07-17T09:05:51.2795711Z separately, but ultimately linked into a single library. Note that no 2025-07-17T09:05:51.2796052Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2025-07-17T09:05:51.2796380Z to a CUDA kernel, you must create a C++ function that calls it, and either 2025-07-17T09:05:51.2796702Z declare or define this C++ function in one of the ``cpp_sources`` (and 2025-07-17T09:05:51.2796970Z include its name in ``functions``). 2025-07-17T09:05:51.2797104Z 2025-07-17T09:05:51.2797241Z The sources in ``sycl_sources`` are concatenated into a separate ``.sycl`` 2025-07-17T09:05:51.2797557Z file and prepended with ``torch/types.h``, ``sycl/sycl.hpp`` includes. 2025-07-17T09:05:51.2797862Z The ``.cpp`` and ``.sycl`` files are compiled separately, but ultimately 2025-07-17T09:05:51.2798172Z linked into a single library. Note that no bindings are generated for 2025-07-17T09:05:51.2798491Z functions in ``sycl_sources`` per se. To bind to a SYCL kernel, you must 2025-07-17T09:05:51.2798817Z create a C++ function that calls it, and either declare or define this 2025-07-17T09:05:51.2799127Z C++ function in one of the ``cpp_sources`` (and include its name 2025-07-17T09:05:51.2799372Z in ``functions``). 2025-07-17T09:05:51.2799474Z 2025-07-17T09:05:51.2799476Z 2025-07-17T09:05:51.2799478Z 2025-07-17T09:05:51.2799596Z See :func:`load` for a description of arguments omitted below. 2025-07-17T09:05:51.2799775Z 2025-07-17T09:05:51.2799838Z Args: 2025-07-17T09:05:51.2800069Z cpp_sources: A string, or list of strings, containing C++ source code. 2025-07-17T09:05:51.2800392Z cuda_sources: A string, or list of strings, containing CUDA source code. 2025-07-17T09:05:51.2800719Z sycl_sources: A string, or list of strings, containing SYCL source code. 2025-07-17T09:05:51.2801033Z functions: A list of function names for which to generate function 2025-07-17T09:05:51.2801355Z bindings. If a dictionary is given, it should map function names to 2025-07-17T09:05:51.2801660Z docstrings (which are otherwise just the function names). 2025-07-17T09:05:51.2802138Z with_cuda: Determines whether CUDA headers and libraries are added to 2025-07-17T09:05:51.2802449Z the build. If set to ``None`` (default), this value is 2025-07-17T09:05:51.2802744Z automatically determined based on whether ``cuda_sources`` is 2025-07-17T09:05:51.2803028Z provided. Set it to ``True`` to force CUDA headers 2025-07-17T09:05:51.2803375Z and libraries to be included. 2025-07-17T09:05:51.2803642Z with_sycl: Determines whether SYCL headers and libraries are added to 2025-07-17T09:05:51.2803933Z the build. If set to ``None`` (default), this value is 2025-07-17T09:05:51.2804231Z automatically determined based on whether ``sycl_sources`` is 2025-07-17T09:05:51.2804512Z provided. Set it to ``True`` to force SYCL headers 2025-07-17T09:05:51.2804733Z and libraries to be included. 2025-07-17T09:05:51.2804984Z with_pytorch_error_handling: Determines whether pytorch error and 2025-07-17T09:05:51.2805305Z warning macros are handled by pytorch instead of pybind. To do 2025-07-17T09:05:51.2805624Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2025-07-17T09:05:51.2805941Z function. This redirection might cause issues in obscure cases 2025-07-17T09:05:51.2806244Z of cpp. This flag should be set to ``False`` when this redirect 2025-07-17T09:05:51.2806502Z causes issues. 2025-07-17T09:05:51.2806772Z no_implicit_headers: If ``True``, skips automatically adding headers, most notably 2025-07-17T09:05:51.2807138Z ``#include `` and ``#include `` lines. 2025-07-17T09:05:51.2807438Z Use this option to improve cold start times when you 2025-07-17T09:05:51.2807757Z already include the necessary headers in your source code. Default: ``False``. 2025-07-17T09:05:51.2807979Z 2025-07-17T09:05:51.2808044Z Example: 2025-07-17T09:05:51.2808237Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-07-17T09:05:51.2808504Z >>> from torch.utils.cpp_extension import load_inline 2025-07-17T09:05:51.2808734Z >>> source = """ 2025-07-17T09:05:51.2808926Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2025-07-17T09:05:51.2809155Z return x.sin() + y.sin(); 2025-07-17T09:05:51.2809343Z } 2025-07-17T09:05:51.2809487Z """ 2025-07-17T09:05:51.2809664Z >>> module = load_inline(name='inline_extension', 2025-07-17T09:05:51.2809908Z ... cpp_sources=[source], 2025-07-17T09:05:51.2810122Z ... functions=['sin_add']) 2025-07-17T09:05:51.2810266Z 2025-07-17T09:05:51.2810327Z .. note:: 2025-07-17T09:05:51.2810558Z Since load_inline will just-in-time compile the source code, please ensure 2025-07-17T09:05:51.2810908Z that you have the right toolchains installed in the runtime. For example, 2025-07-17T09:05:51.2811234Z when loading C++, make sure a C++ compiler is available. If you're loading 2025-07-17T09:05:51.2811580Z a CUDA extension, you will need to additionally install the corresponding CUDA 2025-07-17T09:05:51.2811944Z toolkit (nvcc and any other dependencies your code has). Compiling toolchains 2025-07-17T09:05:51.2812291Z are not included when you install torch and must be additionally installed. 2025-07-17T09:05:51.2812497Z 2025-07-17T09:05:51.2812657Z During compiling, by default, the Ninja backend uses #CPUS + 2 workers to build 2025-07-17T09:05:51.2813051Z the extension. This may use up too many resources on some systems. One 2025-07-17T09:05:51.2813382Z can control the number of workers by setting the `MAX_JOBS` environment 2025-07-17T09:05:51.2813657Z variable to a non-negative number. 2025-07-17T09:05:51.2813786Z 2025-07-17T09:05:51.2813948Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:51.2814155Z 2025-07-17T09:05:51.3080124Z msg = Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1218. 2025-07-17T09:05:51.3081252Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:51.3081483Z 2025-07-17T09:05:51.3081637Z Context passed to policy function during selective checkpointing. 2025-07-17T09:05:51.3081838Z 2025-07-17T09:05:51.3081996Z This class is used to pass relevant metadata to the policy function during 2025-07-17T09:05:51.3082542Z selective checkpointing. The metadata includes whether the current invocation 2025-07-17T09:05:51.3082884Z of the policy function is during recomputation or not. 2025-07-17T09:05:51.3083050Z 2025-07-17T09:05:51.3083123Z Example: 2025-07-17T09:05:51.3083289Z >>> # xdoctest: +SKIP(stub) 2025-07-17T09:05:51.3083462Z >>> 2025-07-17T09:05:51.3083686Z >>> def policy_fn(ctx, op, *args, **kwargs): 2025-07-17T09:05:51.3083921Z >>> print(ctx.is_recompute) 2025-07-17T09:05:51.3084115Z >>> 2025-07-17T09:05:51.3084375Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2025-07-17T09:05:51.3084665Z >>> 2025-07-17T09:05:51.3084841Z >>> out = torch.utils.checkpoint.checkpoint( 2025-07-17T09:05:51.3085059Z >>> fn, x, y, 2025-07-17T09:05:51.3085240Z >>> use_reentrant=False, 2025-07-17T09:05:51.3085433Z >>> context_fn=context_fn, 2025-07-17T09:05:51.3085616Z >>> ) 2025-07-17T09:05:51.3085720Z 2025-07-17T09:05:51.3085879Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:51.3086106Z 2025-07-17T09:05:51.3086510Z msg = Cannot scrape callname=create_selective_checkpoint_contexts in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1358. 2025-07-17T09:05:51.3087081Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:51.3087295Z 2025-07-17T09:05:51.3087455Z Helper to avoid recomputing certain ops during activation checkpointing. 2025-07-17T09:05:51.3087673Z 2025-07-17T09:05:51.3087821Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2025-07-17T09:05:51.3088114Z operations are recomputed during the backward pass. 2025-07-17T09:05:51.3088283Z 2025-07-17T09:05:51.3088341Z Args: 2025-07-17T09:05:51.3088512Z policy_fn_or_list (Callable or List): 2025-07-17T09:05:51.3088769Z - If a policy function is provided, it should accept a 2025-07-17T09:05:51.3089091Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2025-07-17T09:05:51.3089435Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2025-07-17T09:05:51.3089765Z indicating whether the execution of the op should be recomputed or not. 2025-07-17T09:05:51.3090097Z - If a list of operations is provided, it is equivalent to a policy 2025-07-17T09:05:51.3090403Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2025-07-17T09:05:51.3090721Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2025-07-17T09:05:51.3090992Z operations. 2025-07-17T09:05:51.3091222Z allow_cache_entry_mutation (bool, optional): By default, an error is 2025-07-17T09:05:51.3091542Z raised if any tensors cached by selective activation checkpoint are 2025-07-17T09:05:51.3091875Z mutated in order to ensure correctness. If set to `True`, this check 2025-07-17T09:05:51.3092127Z is disabled. 2025-07-17T09:05:51.3092294Z Returns: 2025-07-17T09:05:51.3092457Z A tuple of two context managers. 2025-07-17T09:05:51.3092600Z 2025-07-17T09:05:51.3092663Z Example: 2025-07-17T09:05:51.3092816Z >>> # xdoctest: +REQUIRES(LINUX) 2025-07-17T09:05:51.3093011Z >>> import functools 2025-07-17T09:05:51.3093189Z >>> 2025-07-17T09:05:51.3093364Z >>> x = torch.rand(10, 10, requires_grad=True) 2025-07-17T09:05:51.3093591Z >>> y = torch.rand(10, 10, requires_grad=True) 2025-07-17T09:05:51.3093791Z >>> 2025-07-17T09:05:51.3093944Z >>> ops_to_save = [ 2025-07-17T09:05:51.3094313Z >>> torch.ops.aten.mm.default, 2025-07-17T09:05:51.3094510Z >>> ] 2025-07-17T09:05:51.3094658Z >>> 2025-07-17T09:05:51.3094829Z >>> def policy_fn(ctx, op, *args, **kwargs): 2025-07-17T09:05:51.3095054Z >>> if op in ops_to_save: 2025-07-17T09:05:51.3095261Z >>> return CheckpointPolicy.MUST_SAVE 2025-07-17T09:05:51.3095470Z >>> else: 2025-07-17T09:05:51.3095768Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2025-07-17T09:05:51.3095990Z >>> 2025-07-17T09:05:51.3096234Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2025-07-17T09:05:51.3096515Z >>> 2025-07-17T09:05:51.3096662Z >>> # or equivalently 2025-07-17T09:05:51.3096939Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2025-07-17T09:05:51.3097222Z >>> 2025-07-17T09:05:51.3097374Z >>> def fn(x, y): 2025-07-17T09:05:51.3097608Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2025-07-17T09:05:51.3097865Z >>> 2025-07-17T09:05:51.3098045Z >>> out = torch.utils.checkpoint.checkpoint( 2025-07-17T09:05:51.3098259Z >>> fn, x, y, 2025-07-17T09:05:51.3098439Z >>> use_reentrant=False, 2025-07-17T09:05:51.3098636Z >>> context_fn=context_fn, 2025-07-17T09:05:51.3098818Z >>> ) 2025-07-17T09:05:51.3098912Z 2025-07-17T09:05:51.3099071Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:51.3099297Z 2025-07-17T09:05:51.3168192Z msg = Cannot scrape callname=register_pytree_node in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py line=134. 2025-07-17T09:05:51.3168807Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:51.3169146Z Register a container-like type as pytree node. 2025-07-17T09:05:51.3169314Z 2025-07-17T09:05:51.3169376Z Args: 2025-07-17T09:05:51.3169592Z cls (type): A Python type to treat as an internal pytree node. 2025-07-17T09:05:51.3169965Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2025-07-17T09:05:51.3170357Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2025-07-17T09:05:51.3170750Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2025-07-17T09:05:51.3171078Z passed to the ``unflatten_fn``. 2025-07-17T09:05:51.3171394Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2025-07-17T09:05:51.3171780Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2025-07-17T09:05:51.3172106Z The function should return an instance of ``cls``. 2025-07-17T09:05:51.3172435Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2025-07-17T09:05:51.3172774Z qualified name used when serializing the tree spec. 2025-07-17T09:05:51.3173131Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2025-07-17T09:05:51.3173547Z to convert the context of the pytree to a custom json dumpable representation. This is 2025-07-17T09:05:51.3173945Z used for json serialization, which is being used in :mod:`torch.export` right now. 2025-07-17T09:05:51.3174365Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2025-07-17T09:05:51.3174768Z how to convert the custom json dumpable representation of the context back to the 2025-07-17T09:05:51.3175151Z original context. This is used for json deserialization, which is being used in 2025-07-17T09:05:51.3175449Z :mod:`torch.export` right now. 2025-07-17T09:05:51.3175597Z 2025-07-17T09:05:51.3175677Z Example:: 2025-07-17T09:05:51.3175779Z 2025-07-17T09:05:51.3175855Z >>> # xdoctest: +SKIP 2025-07-17T09:05:51.3176079Z >>> # Registry a Python type with lambda functions 2025-07-17T09:05:51.3176582Z >>> register_pytree_node( 2025-07-17T09:05:51.3176774Z ... set, 2025-07-17T09:05:51.3176960Z ... lambda s: (sorted(s), None, None), 2025-07-17T09:05:51.3177193Z ... lambda children, _: set(children), 2025-07-17T09:05:51.3177393Z ... ) 2025-07-17T09:05:51.3177543Z 2025-07-17T09:05:51.3177909Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:51.3178129Z 2025-07-17T09:05:51.4512757Z msg = Cannot scrape callname=DistributedSampler in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/distributed.py line=18. 2025-07-17T09:05:51.4513430Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:51.4513837Z Sampler that restricts data loading to a subset of the dataset. 2025-07-17T09:05:51.4514041Z 2025-07-17T09:05:51.4514141Z It is especially useful in conjunction with 2025-07-17T09:05:51.4514505Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2025-07-17T09:05:51.4514882Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2025-07-17T09:05:51.4515254Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2025-07-17T09:05:51.4515548Z original dataset that is exclusive to it. 2025-07-17T09:05:51.4515688Z 2025-07-17T09:05:51.4515782Z .. note:: 2025-07-17T09:05:51.4516038Z Dataset is assumed to be of constant size and that any instance of it always 2025-07-17T09:05:51.4516343Z returns the same elements in the same order. 2025-07-17T09:05:51.4516487Z 2025-07-17T09:05:51.4516561Z Args: 2025-07-17T09:05:51.4516731Z dataset: Dataset used for sampling. 2025-07-17T09:05:51.4517022Z num_replicas (int, optional): Number of processes participating in 2025-07-17T09:05:51.4517384Z distributed training. By default, :attr:`world_size` is retrieved from the 2025-07-17T09:05:51.4517697Z current distributed group. 2025-07-17T09:05:51.4517973Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2025-07-17T09:05:51.4518317Z By default, :attr:`rank` is retrieved from the current distributed 2025-07-17T09:05:51.4518569Z group. 2025-07-17T09:05:51.4518807Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2025-07-17T09:05:51.4519101Z indices. 2025-07-17T09:05:51.4519319Z seed (int, optional): random seed used to shuffle the sampler if 2025-07-17T09:05:51.4519615Z :attr:`shuffle=True`. This number should be identical across all 2025-07-17T09:05:51.4530476Z processes in the distributed group. Default: ``0``. 2025-07-17T09:05:51.4530824Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2025-07-17T09:05:51.4531153Z tail of the data to make it evenly divisible across the number of 2025-07-17T09:05:51.4531486Z replicas. If ``False``, the sampler will add extra indices to make 2025-07-17T09:05:51.4531805Z the data evenly divisible across the replicas. Default: ``False``. 2025-07-17T09:05:51.4532000Z 2025-07-17T09:05:51.4532071Z .. warning:: 2025-07-17T09:05:51.4532310Z In distributed mode, calling the :meth:`set_epoch` method at 2025-07-17T09:05:51.4532659Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2025-07-17T09:05:51.4533046Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2025-07-17T09:05:51.4533358Z the same ordering will be always used. 2025-07-17T09:05:51.4533499Z 2025-07-17T09:05:51.4533575Z Example:: 2025-07-17T09:05:51.4533662Z 2025-07-17T09:05:51.4533746Z >>> # xdoctest: +SKIP 2025-07-17T09:05:51.4534004Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2025-07-17T09:05:51.4534323Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2025-07-17T09:05:51.4535121Z ... sampler=sampler) 2025-07-17T09:05:51.4535360Z >>> for epoch in range(start_epoch, n_epochs): 2025-07-17T09:05:51.4535591Z ... if is_distributed: 2025-07-17T09:05:51.4535806Z ... sampler.set_epoch(epoch) 2025-07-17T09:05:51.4536021Z ... train(loader) 2025-07-17T09:05:51.4536200Z 2025-07-17T09:05:51.4536637Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:51.4536853Z 2025-07-17T09:05:51.9757370Z msg = Cannot scrape callname=calculate_gain in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py line=142. 2025-07-17T09:05:51.9757995Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:51.9758381Z Return the recommended gain value for the given nonlinearity function. 2025-07-17T09:05:51.9758587Z 2025-07-17T09:05:51.9758680Z The values are as follows: 2025-07-17T09:05:51.9758861Z 2025-07-17T09:05:51.9758958Z ================= ==================================================== 2025-07-17T09:05:51.9759185Z nonlinearity gain 2025-07-17T09:05:51.9759387Z ================= ==================================================== 2025-07-17T09:05:51.9759607Z Linear / Identity :math:`1` 2025-07-17T09:05:51.9759807Z Conv{1,2,3}D :math:`1` 2025-07-17T09:05:51.9760003Z Sigmoid :math:`1` 2025-07-17T09:05:51.9760193Z Tanh :math:`\frac{5}{3}` 2025-07-17T09:05:51.9760406Z ReLU :math:`\sqrt{2}` 2025-07-17T09:05:51.9760660Z Leaky Relu :math:`\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}` 2025-07-17T09:05:51.9760927Z SELU :math:`\frac{3}{4}` 2025-07-17T09:05:51.9761140Z ================= ==================================================== 2025-07-17T09:05:51.9761282Z 2025-07-17T09:05:51.9761378Z .. warning:: 2025-07-17T09:05:51.9761606Z In order to implement `Self-Normalizing Neural Networks`_ , 2025-07-17T09:05:51.9761942Z you should use ``nonlinearity='linear'`` instead of ``nonlinearity='selu'``. 2025-07-17T09:05:51.9762265Z This gives the initial weights a variance of ``1 / N``, 2025-07-17T09:05:51.9762585Z which is necessary to induce a stable fixed point in the forward pass. 2025-07-17T09:05:51.9762923Z In contrast, the default gain for ``SELU`` sacrifices the normalization 2025-07-17T09:05:51.9763251Z effect for more stable gradient flow in rectangular layers. 2025-07-17T09:05:51.9763446Z 2025-07-17T09:05:51.9763509Z Args: 2025-07-17T09:05:51.9763729Z nonlinearity: the non-linear function (`nn.functional` name) 2025-07-17T09:05:51.9764037Z param: optional parameter for the non-linear function 2025-07-17T09:05:51.9764223Z 2025-07-17T09:05:51.9764290Z Examples: 2025-07-17T09:05:51.9764473Z >>> gain = nn.init.calculate_gain( 2025-07-17T09:05:51.9764693Z ... "leaky_relu", 0.2 2025-07-17T09:05:51.9764922Z ... ) # leaky_relu with negative_slope=0.2 2025-07-17T09:05:51.9765071Z 2025-07-17T09:05:51.9765402Z .. _Self-Normalizing Neural Networks: https://papers.nips.cc/paper/2017/hash/5d44ee6f2c3f71b73125876103c8f6c4-Abstract.html 2025-07-17T09:05:51.9765813Z 2025-07-17T09:05:51.9766062Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:51.9766275Z 2025-07-17T09:05:52.0348863Z msg = Cannot scrape callname=convert_conv2d_weight_memory_format in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/memory_format.py line=14. 2025-07-17T09:05:52.0349565Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.0349946Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2025-07-17T09:05:52.0350155Z 2025-07-17T09:05:52.0350329Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2025-07-17T09:05:52.0351268Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2025-07-17T09:05:52.0351658Z This function is used to facilitate the computation to adopt NHWC kernels, which 2025-07-17T09:05:52.0352081Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2025-07-17T09:05:52.0352338Z 2025-07-17T09:05:52.0352425Z .. note:: 2025-07-17T09:05:52.0352901Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2025-07-17T09:05:52.0353257Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2025-07-17T09:05:52.0353594Z layer with 4d weight will be affected by ``model.to``, which does not 2025-07-17T09:05:52.0353923Z necessarily benefit from conversion to specified ``memory_format``. 2025-07-17T09:05:52.0354266Z One place we are confident in is that NHWC(channels_last) conversion for 2025-07-17T09:05:52.0354599Z convolution in cuDNN, as it is beneficial to run convolution in NHWC, 2025-07-17T09:05:52.0354925Z even in cases where we have to apply permutation to input tensors. 2025-07-17T09:05:52.0355113Z 2025-07-17T09:05:52.0355262Z Hence our strategy here is to convert only the weight of convolution to 2025-07-17T09:05:52.0355550Z channels_last. This ensures that; 2025-07-17T09:05:52.0355830Z 1. Fast convolution kernels will be used, the benefit of which could 2025-07-17T09:05:52.0356177Z outweigh overhead of permutation (if input is not in the same format). 2025-07-17T09:05:52.0356525Z 2. No unnecessary permutations are applied on layers that do not benefit 2025-07-17T09:05:52.0356818Z from memory_format conversion. 2025-07-17T09:05:52.0356959Z 2025-07-17T09:05:52.0357091Z The optimal case is that, layers between convolution layers are channels 2025-07-17T09:05:52.0357433Z last compatible. Input tensor would be permuted to channels last when it 2025-07-17T09:05:52.0357775Z encounters the first convolution layer and stay in that memory format. 2025-07-17T09:05:52.0358116Z Hence following convolutions will not need to permute its input tensor. 2025-07-17T09:05:52.0358316Z 2025-07-17T09:05:52.0358461Z In case where a channels last incompatible layer is between convolution 2025-07-17T09:05:52.0358785Z layers, we need to permute the input tensor back to contiguous format 2025-07-17T09:05:52.0359119Z for that layer. The input tensor will go through the remaining layers in 2025-07-17T09:05:52.0359490Z contiguous format and be permuted to channels last when it encounters 2025-07-17T09:05:52.0359839Z another convolution layer. There's no point in propagating that 2025-07-17T09:05:52.0360170Z permutation to an earlier layer, as most layers are quite agnostic to 2025-07-17T09:05:52.0360441Z ``memory_format``. 2025-07-17T09:05:52.0360556Z 2025-07-17T09:05:52.0360713Z This claim might change when PyTorch supports fusion of permutation, as 2025-07-17T09:05:52.0361054Z there might have been a better spot to fuse the permutation other than 2025-07-17T09:05:52.0361339Z immediately before a convolution. 2025-07-17T09:05:52.0361491Z 2025-07-17T09:05:52.0361553Z Args: 2025-07-17T09:05:52.0361772Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2025-07-17T09:05:52.0362037Z ``nn.Module`` 2025-07-17T09:05:52.0362277Z memory_format: user specified ``memory_format``, 2025-07-17T09:05:52.0362553Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2025-07-17T09:05:52.0362734Z 2025-07-17T09:05:52.0362797Z Returns: 2025-07-17T09:05:52.0362993Z The original module with updated ``nn.Conv2d`` 2025-07-17T09:05:52.0363142Z 2025-07-17T09:05:52.0363216Z Example: 2025-07-17T09:05:52.0363403Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-07-17T09:05:52.0363662Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2025-07-17T09:05:52.0364059Z >>> input = torch.randint( 2025-07-17T09:05:52.0364287Z ... 1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda" 2025-07-17T09:05:52.0364512Z ... ) 2025-07-17T09:05:52.0364674Z >>> model = nn.Sequential( 2025-07-17T09:05:52.0364878Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2025-07-17T09:05:52.0365096Z >>> # This is identical to: 2025-07-17T09:05:52.0365492Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2025-07-17T09:05:52.0365824Z >>> model = nn.utils.convert_conv2d_weight_memory_format( 2025-07-17T09:05:52.0366072Z ... model, torch.channels_last 2025-07-17T09:05:52.0366271Z ... ) 2025-07-17T09:05:52.0366431Z >>> out = model(input) 2025-07-17T09:05:52.0366609Z 2025-07-17T09:05:52.0366858Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.0367066Z 2025-07-17T09:05:52.0367466Z msg = Cannot scrape callname=convert_conv3d_weight_memory_format in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/memory_format.py line=93. 2025-07-17T09:05:52.0368045Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.0368396Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2025-07-17T09:05:52.0368759Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2025-07-17T09:05:52.0369154Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2025-07-17T09:05:52.0369539Z This function is used to facilitate the computation to adopt NHWC kernels, which 2025-07-17T09:05:52.0369944Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2025-07-17T09:05:52.0370184Z 2025-07-17T09:05:52.0370258Z .. note:: 2025-07-17T09:05:52.0370496Z Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive 2025-07-17T09:05:52.0370837Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2025-07-17T09:05:52.0371165Z layer with 4d weight will be affected by ``model.to``, which does not 2025-07-17T09:05:52.0371491Z necessarily benefit from conversion to specified ``memory_format``. 2025-07-17T09:05:52.0371834Z One place we are confident in is that NDHWC(channels_last_3d) conversion for 2025-07-17T09:05:52.0372180Z convolution in cuDNN, as it is beneficial to run convolution in NDHWC, 2025-07-17T09:05:52.0372500Z even in cases where we have to apply permutation to input tensors. 2025-07-17T09:05:52.0372689Z 2025-07-17T09:05:52.0372824Z Hence our strategy here is to convert only the weight of convolution to 2025-07-17T09:05:52.0373101Z channels_last_3d. This ensures that; 2025-07-17T09:05:52.0373367Z 1. Fast convolution kernels will be used, the benefit of which could 2025-07-17T09:05:52.0373711Z outweigh overhead of permutation (if input is not in the same format). 2025-07-17T09:05:52.0374080Z 2. No unnecessary permutations are applied on layers that do not benefit 2025-07-17T09:05:52.0374363Z from memory_format conversion. 2025-07-17T09:05:52.0374490Z 2025-07-17T09:05:52.0374631Z The optimal case is that, layers between convolution layers are channels 2025-07-17T09:05:52.0374970Z last compatible. Input tensor would be permuted to channels last when it 2025-07-17T09:05:52.0375308Z encounters the first convolution layer and stay in that memory format. 2025-07-17T09:05:52.0375655Z Hence following convolutions will not need to permute its input tensor. 2025-07-17T09:05:52.0375855Z 2025-07-17T09:05:52.0375993Z In case where a channels last incompatible layer is between convolution 2025-07-17T09:05:52.0376312Z layers, we need to permute the input tensor back to contiguous format 2025-07-17T09:05:52.0376628Z for that layer. The input tensor will go through the remaining layers in 2025-07-17T09:05:52.0377101Z contiguous format and be permuted to channels last when it encounters 2025-07-17T09:05:52.0377457Z another convolution layer. There's no point in propagating that 2025-07-17T09:05:52.0377770Z permutation to an earlier layer, as most layers are quite agnostic to 2025-07-17T09:05:52.0378044Z ``memory_format``. 2025-07-17T09:05:52.0378150Z 2025-07-17T09:05:52.0378410Z This claim might change when PyTorch supports fusion of permutation, as 2025-07-17T09:05:52.0378742Z there might have been a better spot to fuse the permutation other than 2025-07-17T09:05:52.0379070Z immediately before a convolution. 2025-07-17T09:05:52.0379220Z 2025-07-17T09:05:52.0379283Z Args: 2025-07-17T09:05:52.0379503Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2025-07-17T09:05:52.0379756Z ``nn.Module`` 2025-07-17T09:05:52.0379994Z memory_format: user specified ``memory_format``, 2025-07-17T09:05:52.0380268Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2025-07-17T09:05:52.0380441Z 2025-07-17T09:05:52.0380503Z Returns: 2025-07-17T09:05:52.0380694Z The original module with updated ``nn.Conv3d`` 2025-07-17T09:05:52.0380851Z 2025-07-17T09:05:52.0380919Z Example: 2025-07-17T09:05:52.0381102Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-07-17T09:05:52.0381364Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2025-07-17T09:05:52.0381604Z >>> input = torch.randint( 2025-07-17T09:05:52.0381840Z ... 1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda" 2025-07-17T09:05:52.0382047Z ... ) 2025-07-17T09:05:52.0382210Z >>> model = nn.Sequential( 2025-07-17T09:05:52.0382418Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2025-07-17T09:05:52.0382642Z >>> # This is identical to: 2025-07-17T09:05:52.0382923Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2025-07-17T09:05:52.0383255Z >>> model = nn.utils.convert_conv3d_weight_memory_format( 2025-07-17T09:05:52.0383509Z ... model, torch.channels_last_3d 2025-07-17T09:05:52.0383710Z ... ) 2025-07-17T09:05:52.0383869Z >>> out = model(input) 2025-07-17T09:05:52.0384048Z 2025-07-17T09:05:52.0384284Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.0384493Z 2025-07-17T09:05:52.0455574Z msg = Cannot scrape callname=pad_packed_sequence in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py line=342. 2025-07-17T09:05:52.0456180Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.0456524Z Pad a packed batch of variable length sequences. 2025-07-17T09:05:52.0456686Z 2025-07-17T09:05:52.0456817Z It is an inverse operation to :func:`pack_padded_sequence`. 2025-07-17T09:05:52.0457032Z 2025-07-17T09:05:52.0457215Z The returned Tensor's data will be of size ``T x B x *`` (if :attr:`batch_first` is ``False``) 2025-07-17T09:05:52.0457600Z or ``B x T x *`` (if :attr:`batch_first` is ``True``) , where ``T`` is the length of the longest 2025-07-17T09:05:52.0457878Z sequence and ``B`` is the batch size. 2025-07-17T09:05:52.0458027Z 2025-07-17T09:05:52.0458092Z Example: 2025-07-17T09:05:52.0458349Z >>> from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence 2025-07-17T09:05:52.0458670Z >>> seq = torch.tensor([[1, 2, 0], [3, 0, 0], [4, 5, 6]]) 2025-07-17T09:05:52.0458902Z >>> lens = [2, 1, 3] 2025-07-17T09:05:52.0459106Z >>> packed = pack_padded_sequence( 2025-07-17T09:05:52.0459350Z ... seq, lens, batch_first=True, enforce_sorted=False 2025-07-17T09:05:52.0459575Z ... ) 2025-07-17T09:05:52.0459734Z >>> packed 2025-07-17T09:05:52.0459984Z PackedSequence(data=tensor([4, 1, 3, 5, 2, 6]), batch_sizes=tensor([3, 2, 1]), 2025-07-17T09:05:52.0460610Z sorted_indices=tensor([2, 0, 1]), unsorted_indices=tensor([1, 2, 0])) 2025-07-17T09:05:52.0460962Z >>> seq_unpacked, lens_unpacked = pad_packed_sequence(packed, batch_first=True) 2025-07-17T09:05:52.0461242Z >>> seq_unpacked 2025-07-17T09:05:52.0461414Z tensor([[1, 2, 0], 2025-07-17T09:05:52.0461592Z [3, 0, 0], 2025-07-17T09:05:52.0461934Z [4, 5, 6]]) 2025-07-17T09:05:52.0462120Z >>> lens_unpacked 2025-07-17T09:05:52.0462298Z tensor([2, 1, 3]) 2025-07-17T09:05:52.0462407Z 2025-07-17T09:05:52.0462476Z .. note:: 2025-07-17T09:05:52.0462673Z :attr:`total_length` is useful to implement the 2025-07-17T09:05:52.0462980Z ``pack sequence -> recurrent network -> unpack sequence`` pattern in a 2025-07-17T09:05:52.0463324Z :class:`~torch.nn.Module` wrapped in :class:`~torch.nn.DataParallel`. 2025-07-17T09:05:52.0463659Z See :ref:`this FAQ section ` for 2025-07-17T09:05:52.0463936Z details. 2025-07-17T09:05:52.0464029Z 2025-07-17T09:05:52.0464097Z Args: 2025-07-17T09:05:52.0464266Z sequence (PackedSequence): batch to pad 2025-07-17T09:05:52.0464557Z batch_first (bool, optional): if ``True``, the output will be in ``B x T x *`` 2025-07-17T09:05:52.0464847Z format, ``T x B x *`` otherwise. 2025-07-17T09:05:52.0465111Z padding_value (float, optional): values for padded elements. 2025-07-17T09:05:52.0465526Z total_length (int, optional): if not ``None``, the output will be padded to 2025-07-17T09:05:52.0465876Z have length :attr:`total_length`. This method will throw :class:`ValueError` 2025-07-17T09:05:52.0466195Z if :attr:`total_length` is less than the max sequence length in 2025-07-17T09:05:52.0466443Z :attr:`sequence`. 2025-07-17T09:05:52.0466561Z 2025-07-17T09:05:52.0466631Z Returns: 2025-07-17T09:05:52.0466843Z Tuple of Tensor containing the padded sequence, and a Tensor 2025-07-17T09:05:52.0467143Z containing the list of lengths of each sequence in the batch. 2025-07-17T09:05:52.0467460Z Batch elements will be re-ordered as they were ordered originally when 2025-07-17T09:05:52.0467787Z the batch was passed to ``pack_padded_sequence`` or ``pack_sequence``. 2025-07-17T09:05:52.0468034Z 2025-07-17T09:05:52.0468261Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.0468476Z 2025-07-17T09:05:52.0571675Z msg = Cannot scrape callname=ln_structured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=979. 2025-07-17T09:05:52.0572285Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.0572721Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2025-07-17T09:05:52.0572979Z 2025-07-17T09:05:52.0573163Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2025-07-17T09:05:52.0573508Z by removing the specified ``amount`` of (currently unpruned) channels 2025-07-17T09:05:52.0573811Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2025-07-17T09:05:52.0574108Z Modifies module in place (and also return the modified module) 2025-07-17T09:05:52.0574360Z by: 2025-07-17T09:05:52.0574452Z 2025-07-17T09:05:52.0574592Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-07-17T09:05:52.0574920Z binary mask applied to the parameter ``name`` by the pruning method. 2025-07-17T09:05:52.0575244Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-07-17T09:05:52.0575574Z original (unpruned) parameter is stored in a new parameter named 2025-07-17T09:05:52.0575847Z ``name+'_orig'``. 2025-07-17T09:05:52.0575957Z 2025-07-17T09:05:52.0576027Z Args: 2025-07-17T09:05:52.0576231Z module (nn.Module): module containing the tensor to prune 2025-07-17T09:05:52.0576817Z name (str): parameter name within ``module`` on which pruning 2025-07-17T09:05:52.0577070Z will act. 2025-07-17T09:05:52.0577298Z amount (int or float): quantity of parameters to prune. 2025-07-17T09:05:52.0577583Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-07-17T09:05:52.0578035Z fraction of parameters to prune. If ``int``, it represents the 2025-07-17T09:05:52.0578318Z absolute number of parameters to prune. 2025-07-17T09:05:52.0578597Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2025-07-17T09:05:52.0578894Z entries for argument ``p`` in :func:`torch.norm`. 2025-07-17T09:05:52.0579183Z dim (int): index of the dim along which we define channels to prune. 2025-07-17T09:05:52.0579523Z importance_scores (torch.Tensor): tensor of importance scores (of same 2025-07-17T09:05:52.0579856Z shape as module parameter) used to compute mask for pruning. 2025-07-17T09:05:52.0580185Z The values in this tensor indicate the importance of the corresponding 2025-07-17T09:05:52.0580472Z elements in the parameter being pruned. 2025-07-17T09:05:52.0580764Z If unspecified or None, the module parameter will be used in its place. 2025-07-17T09:05:52.0580963Z 2025-07-17T09:05:52.0581032Z Returns: 2025-07-17T09:05:52.0581261Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-07-17T09:05:52.0581454Z 2025-07-17T09:05:52.0581523Z Examples: 2025-07-17T09:05:52.0581702Z >>> from torch.nn.utils import prune 2025-07-17T09:05:52.0581910Z >>> m = prune.ln_structured( 2025-07-17T09:05:52.0582170Z ... nn.Conv2d(5, 3, 2), "weight", amount=0.3, dim=1, n=float("-inf") 2025-07-17T09:05:52.0582420Z ... ) 2025-07-17T09:05:52.0582579Z 2025-07-17T09:05:52.0582816Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.0583040Z 2025-07-17T09:05:52.0583383Z msg = Cannot scrape callname=global_unstructured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=1026. 2025-07-17T09:05:52.0583909Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.0584135Z 2025-07-17T09:05:52.0584388Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2025-07-17T09:05:52.0584703Z 2025-07-17T09:05:52.0584781Z Modifies modules in place by: 2025-07-17T09:05:52.0584919Z 2025-07-17T09:05:52.0585052Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-07-17T09:05:52.0585486Z binary mask applied to the parameter ``name`` by the pruning method. 2025-07-17T09:05:52.0585810Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-07-17T09:05:52.0586130Z original (unpruned) parameter is stored in a new parameter named 2025-07-17T09:05:52.0586398Z ``name+'_orig'``. 2025-07-17T09:05:52.0586514Z 2025-07-17T09:05:52.0586575Z Args: 2025-07-17T09:05:52.0586795Z parameters (Iterable of (module, name) tuples): parameters of 2025-07-17T09:05:52.0587114Z the model to prune in a global fashion, i.e. by aggregating all 2025-07-17T09:05:52.0587428Z weights prior to deciding which ones to prune. module must be of 2025-07-17T09:05:52.0587729Z type :class:`nn.Module`, and name must be a string. 2025-07-17T09:05:52.0588028Z pruning_method (function): a valid pruning function from this module, 2025-07-17T09:05:52.0588340Z or a custom one implemented by the user that satisfies the 2025-07-17T09:05:52.0588648Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2025-07-17T09:05:52.0588994Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2025-07-17T09:05:52.0589327Z the corresponding parameter's importance scores tensor. The tensor 2025-07-17T09:05:52.0589801Z should be the same shape as the parameter, and is used for computing 2025-07-17T09:05:52.0590064Z mask for pruning. 2025-07-17T09:05:52.0590305Z If unspecified or None, the parameter will be used in place of its 2025-07-17T09:05:52.0590569Z importance scores. 2025-07-17T09:05:52.0590771Z kwargs: other keyword arguments such as: 2025-07-17T09:05:52.0591163Z amount (int or float): quantity of parameters to prune across the 2025-07-17T09:05:52.0591437Z specified parameters. 2025-07-17T09:05:52.0591681Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-07-17T09:05:52.0591985Z fraction of parameters to prune. If ``int``, it represents the 2025-07-17T09:05:52.0592268Z absolute number of parameters to prune. 2025-07-17T09:05:52.0592411Z 2025-07-17T09:05:52.0592476Z Raises: 2025-07-17T09:05:52.0592659Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2025-07-17T09:05:52.0592826Z 2025-07-17T09:05:52.0592893Z Note: 2025-07-17T09:05:52.0593124Z Since global structured pruning doesn't make much sense unless the 2025-07-17T09:05:52.0593442Z norm is normalized by the size of the parameter, we now limit the 2025-07-17T09:05:52.0593715Z scope of global pruning to unstructured methods. 2025-07-17T09:05:52.0593876Z 2025-07-17T09:05:52.0593940Z Examples: 2025-07-17T09:05:52.0594114Z >>> from torch.nn.utils import prune 2025-07-17T09:05:52.0594342Z >>> from collections import OrderedDict 2025-07-17T09:05:52.0594555Z >>> net = nn.Sequential( 2025-07-17T09:05:52.0594742Z ... OrderedDict( 2025-07-17T09:05:52.0594914Z ... [ 2025-07-17T09:05:52.0595082Z ... ("first", nn.Linear(10, 4)), 2025-07-17T09:05:52.0595306Z ... ("second", nn.Linear(4, 1)), 2025-07-17T09:05:52.0595510Z ... ] 2025-07-17T09:05:52.0595660Z ... ) 2025-07-17T09:05:52.0595812Z ... ) 2025-07-17T09:05:52.0595972Z >>> parameters_to_prune = ( 2025-07-17T09:05:52.0596181Z ... (net.first, "weight"), 2025-07-17T09:05:52.0596380Z ... (net.second, "weight"), 2025-07-17T09:05:52.0596568Z ... ) 2025-07-17T09:05:52.0596725Z >>> prune.global_unstructured( 2025-07-17T09:05:52.0596916Z ... parameters_to_prune, 2025-07-17T09:05:52.0597125Z ... pruning_method=prune.L1Unstructured, 2025-07-17T09:05:52.0597342Z ... amount=10, 2025-07-17T09:05:52.0597500Z ... ) 2025-07-17T09:05:52.0597725Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2025-07-17T09:05:52.0597991Z tensor(10) 2025-07-17T09:05:52.0598085Z 2025-07-17T09:05:52.0598088Z 2025-07-17T09:05:52.0598241Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.0598460Z 2025-07-17T09:05:52.0598756Z msg = Cannot scrape callname=custom_from_mask in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=1149. 2025-07-17T09:05:52.0599263Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.0599737Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2025-07-17T09:05:52.0600018Z 2025-07-17T09:05:52.0600160Z Modifies module in place (and also return the modified module) by: 2025-07-17T09:05:52.0600347Z 2025-07-17T09:05:52.0600491Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-07-17T09:05:52.0600829Z binary mask applied to the parameter ``name`` by the pruning method. 2025-07-17T09:05:52.0601160Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-07-17T09:05:52.0601482Z original (unpruned) parameter is stored in a new parameter named 2025-07-17T09:05:52.0601744Z ``name+'_orig'``. 2025-07-17T09:05:52.0601847Z 2025-07-17T09:05:52.0601914Z Args: 2025-07-17T09:05:52.0602106Z module (nn.Module): module containing the tensor to prune 2025-07-17T09:05:52.0602525Z name (str): parameter name within ``module`` on which pruning 2025-07-17T09:05:52.0602767Z will act. 2025-07-17T09:05:52.0602979Z mask (Tensor): binary mask to be applied to the parameter. 2025-07-17T09:05:52.0603165Z 2025-07-17T09:05:52.0603226Z Returns: 2025-07-17T09:05:52.0603452Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-07-17T09:05:52.0603649Z 2025-07-17T09:05:52.0603712Z Examples: 2025-07-17T09:05:52.0615611Z >>> from torch.nn.utils import prune 2025-07-17T09:05:52.0615879Z >>> m = prune.custom_from_mask( 2025-07-17T09:05:52.0616131Z ... nn.Linear(5, 3), name="bias", mask=torch.tensor([0, 1, 0]) 2025-07-17T09:05:52.0616369Z ... ) 2025-07-17T09:05:52.0616531Z >>> print(m.bias_mask) 2025-07-17T09:05:52.0616725Z tensor([0., 1., 0.]) 2025-07-17T09:05:52.0616835Z 2025-07-17T09:05:52.0616895Z 2025-07-17T09:05:52.0617130Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.0617371Z 2025-07-17T09:05:52.0702881Z msg = Cannot scrape callname=register_parametrization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrize.py line=424. 2025-07-17T09:05:52.0703560Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.0703906Z Register a parametrization to a tensor in a module. 2025-07-17T09:05:52.0704081Z 2025-07-17T09:05:52.0704280Z Assume that ``tensor_name="weight"`` for simplicity. When accessing ``module.weight``, 2025-07-17T09:05:52.0704685Z the module will return the parametrized version ``parametrization(module.weight)``. 2025-07-17T09:05:52.0705087Z If the original tensor requires a gradient, the backward pass will differentiate 2025-07-17T09:05:52.0705573Z through :attr:`parametrization`, and the optimizer will update the tensor accordingly. 2025-07-17T09:05:52.0705816Z 2025-07-17T09:05:52.0706003Z The first time that a module registers a parametrization, this function will add an attribute 2025-07-17T09:05:52.0706405Z ``parametrizations`` to the module of type :class:`~ParametrizationList`. 2025-07-17T09:05:52.0706619Z 2025-07-17T09:05:52.0706777Z The list of parametrizations on the tensor ``weight`` will be accessible under 2025-07-17T09:05:52.0707083Z ``module.parametrizations.weight``. 2025-07-17T09:05:52.0707239Z 2025-07-17T09:05:52.0707331Z The original tensor will be accessible under 2025-07-17T09:05:52.0707591Z ``module.parametrizations.weight.original``. 2025-07-17T09:05:52.0707747Z 2025-07-17T09:05:52.0707904Z Parametrizations may be concatenated by registering several parametrizations 2025-07-17T09:05:52.0708205Z on the same attribute. 2025-07-17T09:05:52.0708324Z 2025-07-17T09:05:52.0708465Z The training mode of a registered parametrization is updated on registration 2025-07-17T09:05:52.0708764Z to match the training mode of the host module 2025-07-17T09:05:52.0708906Z 2025-07-17T09:05:52.0709100Z Parametrized parameters and buffers have an inbuilt caching system that can be activated 2025-07-17T09:05:52.0709428Z using the context manager :func:`cached`. 2025-07-17T09:05:52.0709563Z 2025-07-17T09:05:52.0709718Z A :attr:`parametrization` may optionally implement a method with signature 2025-07-17T09:05:52.0709918Z 2025-07-17T09:05:52.0710008Z .. code-block:: python 2025-07-17T09:05:52.0710117Z 2025-07-17T09:05:52.0710265Z def right_inverse(self, X: Tensor) -> Union[Tensor, Sequence[Tensor]] 2025-07-17T09:05:52.0710455Z 2025-07-17T09:05:52.0710628Z This method is called on the unparametrized tensor when the first parametrization 2025-07-17T09:05:52.0710985Z is registered to compute the initial value of the original tensor. 2025-07-17T09:05:52.0711364Z If this method is not implemented, the original tensor will be just the unparametrized tensor. 2025-07-17T09:05:52.0711603Z 2025-07-17T09:05:52.0711799Z If all the parametrizations registered on a tensor implement `right_inverse` it is possible 2025-07-17T09:05:52.0712554Z to initialize a parametrized tensor by assigning to it, as shown in the example below. 2025-07-17T09:05:52.0712786Z 2025-07-17T09:05:52.0712935Z It is possible for the first parametrization to depend on several inputs. 2025-07-17T09:05:52.0713280Z This may be implemented returning a tuple of tensors from ``right_inverse`` 2025-07-17T09:05:52.0713778Z (see the example implementation of a ``RankOne`` parametrization below). 2025-07-17T09:05:52.0713999Z 2025-07-17T09:05:52.0714199Z In this case, the unconstrained tensors are also located under ``module.parametrizations.weight`` 2025-07-17T09:05:52.0714548Z with names ``original0``, ``original1``,... 2025-07-17T09:05:52.0714696Z 2025-07-17T09:05:52.0714763Z .. note:: 2025-07-17T09:05:52.0714860Z 2025-07-17T09:05:52.0715020Z If unsafe=False (default) both the forward and right_inverse methods will be called 2025-07-17T09:05:52.0715350Z once to perform a number of consistency checks. 2025-07-17T09:05:52.0715683Z If unsafe=True, then right_inverse will be called if the tensor is not parametrized, 2025-07-17T09:05:52.0715989Z and nothing will be called otherwise. 2025-07-17T09:05:52.0716134Z 2025-07-17T09:05:52.0716204Z .. note:: 2025-07-17T09:05:52.0716297Z 2025-07-17T09:05:52.0716428Z In most situations, ``right_inverse`` will be a function such that 2025-07-17T09:05:52.0716713Z ``forward(right_inverse(X)) == X`` (see 2025-07-17T09:05:52.0717055Z `right inverse `_). 2025-07-17T09:05:52.0717453Z Sometimes, when the parametrization is not surjective, it may be reasonable 2025-07-17T09:05:52.0717740Z to relax this. 2025-07-17T09:05:52.0717843Z 2025-07-17T09:05:52.0717920Z .. warning:: 2025-07-17T09:05:52.0718012Z 2025-07-17T09:05:52.0718192Z If a parametrization depends on several inputs, :func:`~register_parametrization` 2025-07-17T09:05:52.0718586Z will register a number of new parameters. If such parametrization is registered 2025-07-17T09:05:52.0718960Z after the optimizer is created, these new parameters will need to be added manually 2025-07-17T09:05:52.0719327Z to the optimizer. See :meth:`torch.Optimizer.add_param_group`. 2025-07-17T09:05:52.0719520Z 2025-07-17T09:05:52.0719580Z Args: 2025-07-17T09:05:52.0719808Z module (nn.Module): module on which to register the parametrization 2025-07-17T09:05:52.0720134Z tensor_name (str): name of the parameter or buffer on which to register 2025-07-17T09:05:52.0720410Z the parametrization 2025-07-17T09:05:52.0720664Z parametrization (nn.Module): the parametrization to register 2025-07-17T09:05:52.0720916Z Keyword args: 2025-07-17T09:05:52.0721149Z unsafe (bool): a boolean flag that denotes whether the parametrization 2025-07-17T09:05:52.0721470Z may change the dtype and shape of the tensor. Default: `False` 2025-07-17T09:05:52.0721816Z Warning: the parametrization is not checked for consistency upon registration. 2025-07-17T09:05:52.0722128Z Enable this flag at your own risk. 2025-07-17T09:05:52.0722266Z 2025-07-17T09:05:52.0722339Z Raises: 2025-07-17T09:05:52.0722601Z ValueError: if the module does not have a parameter or a buffer named :attr:`tensor_name` 2025-07-17T09:05:52.0722834Z 2025-07-17T09:05:52.0722910Z Examples: 2025-07-17T09:05:52.0723097Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK) 2025-07-17T09:05:52.0723339Z >>> import torch 2025-07-17T09:05:52.0723528Z >>> import torch.nn as nn 2025-07-17T09:05:52.0723765Z >>> import torch.nn.utils.parametrize as P 2025-07-17T09:05:52.0723992Z >>> 2025-07-17T09:05:52.0724158Z >>> class Symmetric(nn.Module): 2025-07-17T09:05:52.0724373Z >>> def forward(self, X): 2025-07-17T09:05:52.0724630Z >>> return X.triu() + X.triu(1).T # Return a symmetric matrix 2025-07-17T09:05:52.0725009Z >>> 2025-07-17T09:05:52.0725170Z >>> def right_inverse(self, A): 2025-07-17T09:05:52.0725385Z >>> return A.triu() 2025-07-17T09:05:52.0725563Z >>> 2025-07-17T09:05:52.0725720Z >>> m = nn.Linear(5, 5) 2025-07-17T09:05:52.0725958Z >>> P.register_parametrization(m, "weight", Symmetric()) 2025-07-17T09:05:52.0726394Z >>> print(torch.allclose(m.weight, m.weight.T)) # m.weight is now symmetric 2025-07-17T09:05:52.0726675Z True 2025-07-17T09:05:52.0726838Z >>> A = torch.rand(5, 5) 2025-07-17T09:05:52.0727040Z >>> A = A + A.T # A is now symmetric 2025-07-17T09:05:52.0727305Z >>> m.weight = A # Initialize the weight to be the symmetric matrix A 2025-07-17T09:05:52.0727577Z >>> print(torch.allclose(m.weight, A)) 2025-07-17T09:05:52.0727780Z True 2025-07-17T09:05:52.0727864Z 2025-07-17T09:05:52.0727946Z >>> class RankOne(nn.Module): 2025-07-17T09:05:52.0728156Z >>> def forward(self, x, y): 2025-07-17T09:05:52.0728379Z >>> # Form a rank 1 matrix multiplying two vectors 2025-07-17T09:05:52.0728628Z >>> return x.unsqueeze(-1) @ y.unsqueeze(-2) 2025-07-17T09:05:52.0728844Z >>> 2025-07-17T09:05:52.0729008Z >>> def right_inverse(self, Z): 2025-07-17T09:05:52.0729233Z >>> # Project Z onto the rank 1 matrices 2025-07-17T09:05:52.0729489Z >>> U, S, Vh = torch.linalg.svd(Z, full_matrices=False) 2025-07-17T09:05:52.0729737Z >>> # Return rescaled singular vectors 2025-07-17T09:05:52.0729963Z >>> s0_sqrt = S[0].sqrt().unsqueeze(-1) 2025-07-17T09:05:52.0730205Z >>> return U[..., :, 0] * s0_sqrt, Vh[..., 0, :] * s0_sqrt 2025-07-17T09:05:52.0730422Z >>> 2025-07-17T09:05:52.0730611Z >>> linear_rank_one = P.register_parametrization( 2025-07-17T09:05:52.0730857Z ... nn.Linear(4, 4), "weight", RankOne() 2025-07-17T09:05:52.0731059Z ... ) 2025-07-17T09:05:52.0731273Z >>> print(torch.linalg.matrix_rank(linear_rank_one.weight).item()) 2025-07-17T09:05:52.0731528Z 1 2025-07-17T09:05:52.0731609Z 2025-07-17T09:05:52.0731669Z 2025-07-17T09:05:52.0731905Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.0732118Z 2025-07-17T09:05:52.0873652Z msg = Cannot scrape callname=DistributedDataParallel.join in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py line=1766. 2025-07-17T09:05:52.0874343Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.0874583Z 2025-07-17T09:05:52.0874733Z Context manager for training with uneven inputs across processes in DDP. 2025-07-17T09:05:52.0874952Z 2025-07-17T09:05:52.0875102Z This context manager will keep track of already-joined DDP processes, 2025-07-17T09:05:52.0875452Z and "shadow" the forward and backward passes by inserting collective 2025-07-17T09:05:52.0875812Z communication operations to match with the ones created by non-joined 2025-07-17T09:05:52.0876177Z DDP processes. This will ensure each collective call has a corresponding 2025-07-17T09:05:52.0876518Z call by already-joined DDP processes, preventing hangs or errors that 2025-07-17T09:05:52.0876853Z would otherwise happen when training with uneven inputs across 2025-07-17T09:05:52.0877195Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2025-07-17T09:05:52.0877535Z specified to be ``True``, all trainers will throw an error once one rank 2025-07-17T09:05:52.0877858Z runs out of inputs, allowing these errors to be caught and handled 2025-07-17T09:05:52.0878127Z according to application logic. 2025-07-17T09:05:52.0878251Z 2025-07-17T09:05:52.0878401Z Once all DDP processes have joined, the context manager will broadcast 2025-07-17T09:05:52.0878732Z the model corresponding to the last joined process to all processes to 2025-07-17T09:05:52.0879272Z ensure the model is the same across all processes 2025-07-17T09:05:52.0879505Z (which is guaranteed by DDP). 2025-07-17T09:05:52.0879640Z 2025-07-17T09:05:52.0879771Z To use this to enable training with uneven inputs across processes, 2025-07-17T09:05:52.0880111Z simply wrap this context manager around your training loop. No further 2025-07-17T09:05:52.0880433Z modifications to the model or data loading is required. 2025-07-17T09:05:52.0880611Z 2025-07-17T09:05:52.0880836Z .. warning:: 2025-07-17T09:05:52.0881069Z If the model or training loop this context manager is wrapped around 2025-07-17T09:05:52.0881381Z has additional distributed collective operations, such as 2025-07-17T09:05:52.0881687Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2025-07-17T09:05:52.0882004Z ``throw_on_early_termination`` must be enabled. This is because this 2025-07-17T09:05:52.0882329Z context manager is not aware of non-DDP collective communication. 2025-07-17T09:05:52.0882633Z This flag will cause all ranks to throw when any one rank 2025-07-17T09:05:52.0882933Z exhausts inputs, allowing these errors to be caught and recovered 2025-07-17T09:05:52.0883191Z from across all ranks. 2025-07-17T09:05:52.0883303Z 2025-07-17T09:05:52.0883372Z Args: 2025-07-17T09:05:52.0883580Z divide_by_initial_world_size (bool): If ``True``, will divide 2025-07-17T09:05:52.0883881Z gradients by the initial ``world_size`` DDP training was launched 2025-07-17T09:05:52.0884178Z with. If ``False``, will compute the effective world size 2025-07-17T09:05:52.0884452Z (number of ranks that have not depleted their inputs yet) and 2025-07-17T09:05:52.0884729Z divide gradients by that during allreduce. Set 2025-07-17T09:05:52.0884999Z ``divide_by_initial_world_size=True`` to ensure every input 2025-07-17T09:05:52.0885302Z sample including the uneven inputs have equal weight in terms of 2025-07-17T09:05:52.0885597Z how much they contribute to the global gradient. This is 2025-07-17T09:05:52.0885885Z achieved by always dividing the gradient by the initial 2025-07-17T09:05:52.0886185Z ``world_size`` even when we encounter uneven inputs. If you set 2025-07-17T09:05:52.0886471Z this to ``False``, we divide the gradient by the remaining 2025-07-17T09:05:52.0886765Z number of nodes. This ensures parity with training on a smaller 2025-07-17T09:05:52.0887066Z ``world_size`` although it also means the uneven inputs would 2025-07-17T09:05:52.0887353Z contribute more towards the global gradient. Typically, you 2025-07-17T09:05:52.0887643Z would want to set this to ``True`` for cases where the last few 2025-07-17T09:05:52.0887924Z inputs of your training job are uneven. In extreme cases, where 2025-07-17T09:05:52.0888215Z there is a large discrepancy in the number of inputs, setting 2025-07-17T09:05:52.0888486Z this to ``False`` might provide better results. 2025-07-17T09:05:52.0888801Z enable (bool): Whether to enable uneven input detection or not. Pass 2025-07-17T09:05:52.0889106Z in ``enable=False`` to disable in cases where you know that 2025-07-17T09:05:52.0889398Z inputs are even across participating processes. Default is 2025-07-17T09:05:52.0889642Z ``True``. 2025-07-17T09:05:52.0889852Z throw_on_early_termination (bool): Whether to throw an error 2025-07-17T09:05:52.0890158Z or continue training when at least one rank has exhausted 2025-07-17T09:05:52.0890455Z inputs. If ``True``, will throw upon the first rank reaching end 2025-07-17T09:05:52.0890750Z of data. If ``False``, will continue training with a smaller 2025-07-17T09:05:52.0891044Z effective world size until all ranks are joined. Note that if 2025-07-17T09:05:52.0891316Z this flag is specified, then the flag 2025-07-17T09:05:52.0891571Z ``divide_by_initial_world_size`` would be ignored. Default 2025-07-17T09:05:52.0891797Z is ``False``. 2025-07-17T09:05:52.0892031Z 2025-07-17T09:05:52.0894676Z 2025-07-17T09:05:52.0894765Z Example:: 2025-07-17T09:05:52.0894871Z 2025-07-17T09:05:52.0894947Z >>> # xdoctest: +SKIP("Distributed") 2025-07-17T09:05:52.0895161Z >>> import torch 2025-07-17T09:05:52.0895337Z >>> import torch.distributed as dist 2025-07-17T09:05:52.0895547Z >>> import os 2025-07-17T09:05:52.0895725Z >>> import torch.multiprocessing as mp 2025-07-17T09:05:52.0896091Z >>> import torch.nn as nn 2025-07-17T09:05:52.0896291Z >>> # On each spawned worker 2025-07-17T09:05:52.0896485Z >>> def worker(rank): 2025-07-17T09:05:52.0896718Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2025-07-17T09:05:52.0896973Z >>> torch.cuda.set_device(rank) 2025-07-17T09:05:52.0897195Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2025-07-17T09:05:52.0897465Z >>> model = torch.nn.parallel.DistributedDataParallel( 2025-07-17T09:05:52.0897733Z >>> model, device_ids=[rank], output_device=rank 2025-07-17T09:05:52.0897957Z >>> ) 2025-07-17T09:05:52.0898191Z >>> # Rank 1 gets one more input than rank 0. 2025-07-17T09:05:52.0898466Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2025-07-17T09:05:52.0898730Z >>> with model.join(): 2025-07-17T09:05:52.0898921Z >>> for _ in range(5): 2025-07-17T09:05:52.0899112Z >>> for inp in inputs: 2025-07-17T09:05:52.0899332Z >>> loss = model(inp).sum() 2025-07-17T09:05:52.0899543Z >>> loss.backward() 2025-07-17T09:05:52.0899795Z >>> # Without the join() API, the below synchronization will hang 2025-07-17T09:05:52.0900077Z >>> # blocking for rank 1's allreduce to complete. 2025-07-17T09:05:52.0900317Z >>> torch.cuda.synchronize(device=rank) 2025-07-17T09:05:52.0900463Z 2025-07-17T09:05:52.0900623Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.0900836Z 2025-07-17T09:05:52.0901284Z msg = Cannot scrape callname=DistributedDataParallel._register_fused_optim in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py line=2057. 2025-07-17T09:05:52.0901896Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.0902123Z 2025-07-17T09:05:52.0902306Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2025-07-17T09:05:52.0902555Z 2025-07-17T09:05:52.0902683Z Registers an optimizer with DDP such that the optimization for a 2025-07-17T09:05:52.0903000Z parameter will run immediately when that parameter's gradient is 2025-07-17T09:05:52.0903314Z finished with reduction, instead of waiting for all parameters' 2025-07-17T09:05:52.0903641Z gradients to finish reduction. This can result in a training speedup 2025-07-17T09:05:52.0903968Z depending on your workload since the optimizer can run while gradient 2025-07-17T09:05:52.0904300Z reduction for other parameters are still ongoing. In addition, this has 2025-07-17T09:05:52.0904642Z the potential to reduce peak memory consumption during training, as it 2025-07-17T09:05:52.0904966Z only needs to load the per-parameter optimizer states of a single 2025-07-17T09:05:52.0905380Z parameter at a time, instead of loading all per-parameter optimizer 2025-07-17T09:05:52.0905628Z states at once. 2025-07-17T09:05:52.0905729Z 2025-07-17T09:05:52.0905790Z Args: 2025-07-17T09:05:52.0906010Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2025-07-17T09:05:52.0906262Z as a fused optimizer. 2025-07-17T09:05:52.0906490Z *args (Sequence[Any]): Arguments to forward to `optim`. 2025-07-17T09:05:52.0906790Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2025-07-17T09:05:52.0907119Z to optimize, similar to `params` argument of traditional `torch.optim` 2025-07-17T09:05:52.0907444Z Optimizers. If this is omitted, all DDP model parameters will be 2025-07-17T09:05:52.0907781Z optimized. 2025-07-17T09:05:52.0908000Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2025-07-17T09:05:52.0908319Z 2025-07-17T09:05:52.0908399Z .. warning :: 2025-07-17T09:05:52.0908627Z _register_fused_optim should only be called once on a DDP instance, 2025-07-17T09:05:52.0908953Z and registering multiple fused optimizers for the same DDP model 2025-07-17T09:05:52.0909352Z is not currently supported. Please ping 2025-07-17T09:05:52.0909653Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-07-17T09:05:52.0909919Z for your use case. 2025-07-17T09:05:52.0910030Z 2025-07-17T09:05:52.0910093Z .. warning :: 2025-07-17T09:05:52.0910310Z _register_fused_optim and register_comm_hook currently do not 2025-07-17T09:05:52.0910633Z compose together, meaning that custom DDP communication hooks are 2025-07-17T09:05:52.0910948Z not supported with overlapped optimizers. Please ping 2025-07-17T09:05:52.0911258Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-07-17T09:05:52.0911531Z for your use case. 2025-07-17T09:05:52.0911626Z 2025-07-17T09:05:52.0911704Z .. warning :: 2025-07-17T09:05:52.0911938Z Gradient accumulation and DDP `no_sync` are currently not supported 2025-07-17T09:05:52.0912221Z with overlapped optimizer. Please ping 2025-07-17T09:05:52.0912501Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-07-17T09:05:52.0912754Z for your use case. 2025-07-17T09:05:52.0912863Z 2025-07-17T09:05:52.0912931Z Example:: 2025-07-17T09:05:52.0913020Z 2025-07-17T09:05:52.0913108Z >>> # xdoctest: +SKIP("No rendezvous handler") 2025-07-17T09:05:52.0913439Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2025-07-17T09:05:52.0913809Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2025-07-17T09:05:52.0914052Z >>> lr = 1e-2 2025-07-17T09:05:52.0914220Z >>> betas = (0.9, 0.99) 2025-07-17T09:05:52.0914397Z >>> eps = 1e-6 2025-07-17T09:05:52.0914633Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2025-07-17T09:05:52.0914923Z >>> # Example with subset of parameters 2025-07-17T09:05:52.0915151Z >>> params_to_opt = [list(net.parameters())[0]] 2025-07-17T09:05:52.0915381Z >>> net._register_fused_optim( 2025-07-17T09:05:52.0915654Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2025-07-17T09:05:52.0915907Z ... ) 2025-07-17T09:05:52.0915996Z 2025-07-17T09:05:52.0916146Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.0916367Z 2025-07-17T09:05:52.1037388Z msg = Cannot scrape callname=SyncBatchNorm in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py line=601. 2025-07-17T09:05:52.1038026Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.1038373Z Applies Batch Normalization over a N-Dimensional input. 2025-07-17T09:05:52.1038561Z 2025-07-17T09:05:52.1038795Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2025-07-17T09:05:52.1039226Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2025-07-17T09:05:52.1039581Z Internal Covariate Shift `__ . 2025-07-17T09:05:52.1039774Z 2025-07-17T09:05:52.1039845Z .. math:: 2025-07-17T09:05:52.1039955Z 2025-07-17T09:05:52.1040098Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2025-07-17T09:05:52.1040329Z 2025-07-17T09:05:52.1040474Z The mean and standard-deviation are calculated per-dimension over all 2025-07-17T09:05:52.1040821Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2025-07-17T09:05:52.1041172Z are learnable parameter vectors of size `C` (where `C` is the input size). 2025-07-17T09:05:52.1041494Z By default, the elements of :math:`\gamma` are sampled from 2025-07-17T09:05:52.1042148Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2025-07-17T09:05:52.1042489Z The standard-deviation is calculated via the biased estimator, equivalent to 2025-07-17T09:05:52.1042792Z `torch.var(input, unbiased=False)`. 2025-07-17T09:05:52.1042941Z 2025-07-17T09:05:52.1043087Z Also by default, during training this layer keeps running estimates of its 2025-07-17T09:05:52.1043584Z computed mean and variance, which are then used for normalization during 2025-07-17T09:05:52.1043946Z evaluation. The running estimates are kept with a default :attr:`momentum` 2025-07-17T09:05:52.1044216Z of 0.1. 2025-07-17T09:05:52.1044304Z 2025-07-17T09:05:52.1044455Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2025-07-17T09:05:52.1044794Z keep running estimates, and batch statistics are instead used during 2025-07-17T09:05:52.1045069Z evaluation time as well. 2025-07-17T09:05:52.1045194Z 2025-07-17T09:05:52.1045268Z .. note:: 2025-07-17T09:05:52.1045489Z This :attr:`momentum` argument is different from one used in optimizer 2025-07-17T09:05:52.1045824Z classes and the conventional notion of momentum. Mathematically, the 2025-07-17T09:05:52.1046110Z update rule for running statistics here is 2025-07-17T09:05:52.1046435Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2025-07-17T09:05:52.1046789Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2025-07-17T09:05:52.1047047Z new observed value. 2025-07-17T09:05:52.1047171Z 2025-07-17T09:05:52.1047377Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2025-07-17T09:05:52.1047787Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2025-07-17T09:05:52.1048118Z Normalization or Spatio-temporal Batch Normalization. 2025-07-17T09:05:52.1048287Z 2025-07-17T09:05:52.1048392Z Currently :class:`SyncBatchNorm` only supports 2025-07-17T09:05:52.1048735Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2025-07-17T09:05:52.1049103Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2025-07-17T09:05:52.1049424Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2025-07-17T09:05:52.1049684Z Network with DDP. 2025-07-17T09:05:52.1049792Z 2025-07-17T09:05:52.1049861Z Args: 2025-07-17T09:05:52.1050062Z num_features: :math:`C` from an expected input of size 2025-07-17T09:05:52.1050302Z :math:`(N, C, +)` 2025-07-17T09:05:52.1050540Z eps: a value added to the denominator for numerical stability. 2025-07-17T09:05:52.1050794Z Default: ``1e-5`` 2025-07-17T09:05:52.1051029Z momentum: the value used for the running_mean and running_var 2025-07-17T09:05:52.1051341Z computation. Can be set to ``None`` for cumulative moving average 2025-07-17T09:05:52.1051626Z (i.e. simple average). Default: 0.1 2025-07-17T09:05:52.1051895Z affine: a boolean value that when set to ``True``, this module has 2025-07-17T09:05:52.1052190Z learnable affine parameters. Default: ``True`` 2025-07-17T09:05:52.1052484Z track_running_stats: a boolean value that when set to ``True``, this 2025-07-17T09:05:52.1052826Z module tracks the running mean and variance, and when set to ``False``, 2025-07-17T09:05:52.1053164Z this module does not track such statistics, and initializes statistics 2025-07-17T09:05:52.1053488Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2025-07-17T09:05:52.1053828Z When these buffers are ``None``, this module always uses batch statistics. 2025-07-17T09:05:52.1054130Z in both training and eval modes. Default: ``True`` 2025-07-17T09:05:52.1054426Z process_group: synchronization of stats happen within each process group 2025-07-17T09:05:52.1054863Z individually. Default behavior is synchronization across the whole 2025-07-17T09:05:52.1055193Z world 2025-07-17T09:05:52.1055294Z 2025-07-17T09:05:52.1055358Z Shape: 2025-07-17T09:05:52.1055523Z - Input: :math:`(N, C, +)` 2025-07-17T09:05:52.1055752Z - Output: :math:`(N, C, +)` (same shape as input) 2025-07-17T09:05:52.1055910Z 2025-07-17T09:05:52.1056080Z .. note:: 2025-07-17T09:05:52.1056325Z Synchronization of batchnorm statistics occurs only while training, i.e. 2025-07-17T09:05:52.1056662Z synchronization is disabled when ``model.eval()`` is set or if 2025-07-17T09:05:52.1056928Z ``self.training`` is otherwise ``False``. 2025-07-17T09:05:52.1057067Z 2025-07-17T09:05:52.1057147Z Examples:: 2025-07-17T09:05:52.1057238Z 2025-07-17T09:05:52.1057318Z >>> # xdoctest: +SKIP 2025-07-17T09:05:52.1057510Z >>> # With Learnable Parameters 2025-07-17T09:05:52.1057728Z >>> m = nn.SyncBatchNorm(100) 2025-07-17T09:05:52.1057930Z >>> # creating process group (optional) 2025-07-17T09:05:52.1058167Z >>> # ranks is a list of int identifying rank ids. 2025-07-17T09:05:52.1058401Z >>> ranks = list(range(8)) 2025-07-17T09:05:52.1058613Z >>> r1, r2 = ranks[:4], ranks[4:] 2025-07-17T09:05:52.1058855Z >>> # Note: every rank calls into new_group for every 2025-07-17T09:05:52.1059123Z >>> # process group created, even if that rank is not 2025-07-17T09:05:52.1059356Z >>> # part of the group. 2025-07-17T09:05:52.1059640Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2025-07-17T09:05:52.1059986Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2025-07-17T09:05:52.1060258Z >>> # Without Learnable Parameters 2025-07-17T09:05:52.1060529Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2025-07-17T09:05:52.1060809Z >>> input = torch.randn(20, 100, 35, 45, 10) 2025-07-17T09:05:52.1061020Z >>> output = m(input) 2025-07-17T09:05:52.1061153Z 2025-07-17T09:05:52.1061237Z >>> # network is nn.BatchNorm layer 2025-07-17T09:05:52.1061541Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2025-07-17T09:05:52.1061880Z >>> # only single gpu per process is currently supported 2025-07-17T09:05:52.1062176Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2025-07-17T09:05:52.1062452Z >>> sync_bn_network, 2025-07-17T09:05:52.1062672Z >>> device_ids=[args.local_rank], 2025-07-17T09:05:52.1062905Z >>> output_device=args.local_rank) 2025-07-17T09:05:52.1063112Z 2025-07-17T09:05:52.1074084Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.1074331Z 2025-07-17T09:05:52.1074756Z msg = Cannot scrape callname=SyncBatchNorm.convert_sync_batchnorm in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py line=825. 2025-07-17T09:05:52.1075364Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.1075784Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2025-07-17T09:05:52.1076028Z 2025-07-17T09:05:52.1076106Z Args: 2025-07-17T09:05:52.1076366Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2025-07-17T09:05:52.1076727Z process_group (optional): process group to scope synchronization, 2025-07-17T09:05:52.1077010Z default is the whole world 2025-07-17T09:05:52.1077145Z 2025-07-17T09:05:52.1077221Z Returns: 2025-07-17T09:05:52.1077477Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2025-07-17T09:05:52.1077837Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2025-07-17T09:05:52.1078399Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2025-07-17T09:05:52.1078663Z instead. 2025-07-17T09:05:52.1078775Z 2025-07-17T09:05:52.1078847Z Example:: 2025-07-17T09:05:52.1078954Z 2025-07-17T09:05:52.1079037Z >>> # Network with nn.BatchNorm layer 2025-07-17T09:05:52.1079289Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-07-17T09:05:52.1079665Z >>> module = torch.nn.Sequential( 2025-07-17T09:05:52.1079886Z >>> torch.nn.Linear(20, 100), 2025-07-17T09:05:52.1080115Z >>> torch.nn.BatchNorm1d(100), 2025-07-17T09:05:52.1080333Z >>> ).cuda() 2025-07-17T09:05:52.1080551Z >>> # creating process group (optional) 2025-07-17T09:05:52.1080808Z >>> # ranks is a list of int identifying rank ids. 2025-07-17T09:05:52.1081044Z >>> ranks = list(range(8)) 2025-07-17T09:05:52.1081261Z >>> r1, r2 = ranks[:4], ranks[4:] 2025-07-17T09:05:52.1081495Z >>> # Note: every rank calls into new_group for every 2025-07-17T09:05:52.1081757Z >>> # process group created, even if that rank is not 2025-07-17T09:05:52.1081970Z >>> # part of the group. 2025-07-17T09:05:52.1082165Z >>> # xdoctest: +SKIP("distributed") 2025-07-17T09:05:52.1082448Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2025-07-17T09:05:52.1082788Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2025-07-17T09:05:52.1083157Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2025-07-17T09:05:52.1083396Z 2025-07-17T09:05:52.1083460Z 2025-07-17T09:05:52.1083708Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.1083922Z 2025-07-17T09:05:52.1289915Z msg = Cannot scrape callname=Transformer.forward in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py line=186. 2025-07-17T09:05:52.1290599Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.1290955Z Take in and process masked source/target sequences. 2025-07-17T09:05:52.1291141Z 2025-07-17T09:05:52.1291218Z .. note:: 2025-07-17T09:05:52.1291332Z 2025-07-17T09:05:52.1291576Z If a boolean tensor is provided for any of the [src/tgt/memory]_mask arguments, positions with a ``True`` value are 2025-07-17T09:05:52.1291977Z not allowed to participate in the attention, 2025-07-17T09:05:52.1292266Z which is the opposite of the definition for :attr:`attn_mask` 2025-07-17T09:05:52.1292582Z in :func:`torch.nn.functional.scaled_dot_product_attention`. 2025-07-17T09:05:52.1292763Z 2025-07-17T09:05:52.1292840Z Args: 2025-07-17T09:05:52.1293034Z src: the sequence to the encoder (required). 2025-07-17T09:05:52.1293274Z tgt: the sequence to the decoder (required). 2025-07-17T09:05:52.1293538Z src_mask: the additive mask for the src sequence (optional). 2025-07-17T09:05:52.1293830Z tgt_mask: the additive mask for the tgt sequence (optional). 2025-07-17T09:05:52.1294138Z memory_mask: the additive mask for the encoder output (optional). 2025-07-17T09:05:52.1294484Z src_key_padding_mask: the Tensor mask for src keys per batch (optional). 2025-07-17T09:05:52.1294828Z tgt_key_padding_mask: the Tensor mask for tgt keys per batch (optional). 2025-07-17T09:05:52.1295184Z memory_key_padding_mask: the Tensor mask for memory keys per batch (optional). 2025-07-17T09:05:52.1295538Z src_is_causal: If specified, applies a causal mask as ``src_mask``. 2025-07-17T09:05:52.1295841Z Default: ``None``; try to detect a causal mask. 2025-07-17T09:05:52.1296075Z Warning: 2025-07-17T09:05:52.1296602Z ``src_is_causal`` provides a hint that ``src_mask`` is 2025-07-17T09:05:52.1296970Z the causal mask. Providing incorrect hints can result in 2025-07-17T09:05:52.1297261Z incorrect execution, including forward and backward 2025-07-17T09:05:52.1297512Z compatibility. 2025-07-17T09:05:52.1297768Z tgt_is_causal: If specified, applies a causal mask as ``tgt_mask``. 2025-07-17T09:05:52.1298201Z Default: ``None``; try to detect a causal mask. 2025-07-17T09:05:52.1298427Z Warning: 2025-07-17T09:05:52.1298634Z ``tgt_is_causal`` provides a hint that ``tgt_mask`` is 2025-07-17T09:05:52.1298885Z the causal mask. Providing incorrect hints can result in 2025-07-17T09:05:52.1299158Z incorrect execution, including forward and backward 2025-07-17T09:05:52.1299390Z compatibility. 2025-07-17T09:05:52.1299617Z memory_is_causal: If specified, applies a causal mask as 2025-07-17T09:05:52.1299872Z ``memory_mask``. 2025-07-17T09:05:52.1300075Z Default: ``False``. 2025-07-17T09:05:52.1300268Z Warning: 2025-07-17T09:05:52.1300468Z ``memory_is_causal`` provides a hint that 2025-07-17T09:05:52.1300731Z ``memory_mask`` is the causal mask. Providing incorrect 2025-07-17T09:05:52.1301009Z hints can result in incorrect execution, including 2025-07-17T09:05:52.1301270Z forward and backward compatibility. 2025-07-17T09:05:52.1301415Z 2025-07-17T09:05:52.1301489Z Shape: 2025-07-17T09:05:52.1301739Z - src: :math:`(S, E)` for unbatched input, :math:`(S, N, E)` if `batch_first=False` or 2025-07-17T09:05:52.1302046Z `(N, S, E)` if `batch_first=True`. 2025-07-17T09:05:52.1302346Z - tgt: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or 2025-07-17T09:05:52.1302648Z `(N, T, E)` if `batch_first=True`. 2025-07-17T09:05:52.1302925Z - src_mask: :math:`(S, S)` or :math:`(N\cdot\text{num\_heads}, S, S)`. 2025-07-17T09:05:52.1303219Z - tgt_mask: :math:`(T, T)` or :math:`(N\cdot\text{num\_heads}, T, T)`. 2025-07-17T09:05:52.1303484Z - memory_mask: :math:`(T, S)`. 2025-07-17T09:05:52.1303785Z - src_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`. 2025-07-17T09:05:52.1304145Z - tgt_key_padding_mask: :math:`(T)` for unbatched input otherwise :math:`(N, T)`. 2025-07-17T09:05:52.1304506Z - memory_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`. 2025-07-17T09:05:52.1304719Z 2025-07-17T09:05:52.1304926Z Note: [src/tgt/memory]_mask ensures that position :math:`i` is allowed to attend the unmasked 2025-07-17T09:05:52.1305402Z positions. If a BoolTensor is provided, positions with ``True`` 2025-07-17T09:05:52.1305773Z are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor 2025-07-17T09:05:52.1306111Z is provided, it will be added to the attention weight. 2025-07-17T09:05:52.1306471Z [src/tgt/memory]_key_padding_mask provides specified elements in the key to be ignored by 2025-07-17T09:05:52.1306855Z the attention. If a BoolTensor is provided, the positions with the 2025-07-17T09:05:52.1307237Z value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged. 2025-07-17T09:05:52.1307481Z 2025-07-17T09:05:52.1307649Z - output: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or 2025-07-17T09:05:52.1307957Z `(N, T, E)` if `batch_first=True`. 2025-07-17T09:05:52.1308097Z 2025-07-17T09:05:52.1308260Z Note: Due to the multi-head attention architecture in the transformer model, 2025-07-17T09:05:52.1308615Z the output sequence length of a transformer is same as the input sequence 2025-07-17T09:05:52.1309052Z (i.e. target) length of the decoder. 2025-07-17T09:05:52.1309200Z 2025-07-17T09:05:52.1309388Z where :math:`S` is the source sequence length, :math:`T` is the target sequence length, :math:`N` is the 2025-07-17T09:05:52.1309729Z batch size, :math:`E` is the feature number 2025-07-17T09:05:52.1309885Z 2025-07-17T09:05:52.1309951Z Examples: 2025-07-17T09:05:52.1310259Z >>> # xdoctest: +SKIP 2025-07-17T09:05:52.1310482Z >>> output = transformer_model( 2025-07-17T09:05:52.1310729Z ... src, tgt, src_mask=src_mask, tgt_mask=tgt_mask 2025-07-17T09:05:52.1310954Z ... ) 2025-07-17T09:05:52.1311114Z 2025-07-17T09:05:52.1311354Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.1311564Z 2025-07-17T09:05:52.1483984Z msg = Cannot scrape callname=Unflatten in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py line=60. 2025-07-17T09:05:52.1484631Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.1484862Z 2025-07-17T09:05:52.1485072Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2025-07-17T09:05:52.1485331Z 2025-07-17T09:05:52.1485516Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2025-07-17T09:05:52.1485913Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2025-07-17T09:05:52.1486117Z 2025-07-17T09:05:52.1486323Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2025-07-17T09:05:52.1486751Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2025-07-17T09:05:52.1487093Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2025-07-17T09:05:52.1487261Z 2025-07-17T09:05:52.1487333Z Shape: 2025-07-17T09:05:52.1487570Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2025-07-17T09:05:52.1487919Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2025-07-17T09:05:52.1488266Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2025-07-17T09:05:52.1488550Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2025-07-17T09:05:52.1488707Z 2025-07-17T09:05:52.1488777Z Args: 2025-07-17T09:05:52.1488966Z dim (Union[int, str]): Dimension to be unflattened 2025-07-17T09:05:52.1489340Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2025-07-17T09:05:52.1489610Z 2025-07-17T09:05:52.1489675Z Examples: 2025-07-17T09:05:52.1489840Z >>> input = torch.randn(2, 50) 2025-07-17T09:05:52.1490046Z >>> # With tuple of ints 2025-07-17T09:05:52.1490240Z >>> m = nn.Sequential( 2025-07-17T09:05:52.1490426Z >>> nn.Linear(50, 50), 2025-07-17T09:05:52.1490626Z >>> nn.Unflatten(1, (2, 5, 5)) 2025-07-17T09:05:52.1490827Z >>> ) 2025-07-17T09:05:52.1490984Z >>> output = m(input) 2025-07-17T09:05:52.1491179Z >>> output.size() 2025-07-17T09:05:52.1491345Z torch.Size([2, 2, 5, 5]) 2025-07-17T09:05:52.1491534Z >>> # With torch.Size 2025-07-17T09:05:52.1491709Z >>> m = nn.Sequential( 2025-07-17T09:05:52.1491888Z >>> nn.Linear(50, 50), 2025-07-17T09:05:52.1492087Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2025-07-17T09:05:52.1492296Z >>> ) 2025-07-17T09:05:52.1492445Z >>> output = m(input) 2025-07-17T09:05:52.1492616Z >>> output.size() 2025-07-17T09:05:52.1492783Z torch.Size([2, 2, 5, 5]) 2025-07-17T09:05:52.1492982Z >>> # With namedshape (tuple of tuples) 2025-07-17T09:05:52.1493218Z >>> input = torch.randn(2, 50, names=("N", "features")) 2025-07-17T09:05:52.1493499Z >>> unflatten = nn.Unflatten("features", (("C", 2), ("H", 5), ("W", 5))) 2025-07-17T09:05:52.1493765Z >>> output = unflatten(input) 2025-07-17T09:05:52.1494219Z >>> output.size() 2025-07-17T09:05:52.1494465Z torch.Size([2, 2, 5, 5]) 2025-07-17T09:05:52.1494581Z 2025-07-17T09:05:52.1494730Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.1494947Z 2025-07-17T09:05:52.1682052Z msg = Cannot scrape callname=MaxUnpool2d in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py line=406. 2025-07-17T09:05:52.1683072Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.1683434Z Computes a partial inverse of :class:`MaxPool2d`. 2025-07-17T09:05:52.1683607Z 2025-07-17T09:05:52.1683777Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2025-07-17T09:05:52.1684007Z 2025-07-17T09:05:52.1684148Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2025-07-17T09:05:52.1684511Z including the indices of the maximal values and computes a partial inverse 2025-07-17T09:05:52.1684832Z in which all non-maximal values are set to zero. 2025-07-17T09:05:52.1685014Z 2025-07-17T09:05:52.1685079Z Note: 2025-07-17T09:05:52.1685366Z This operation may behave nondeterministically when the input indices has repeat values. 2025-07-17T09:05:52.1685859Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2025-07-17T09:05:52.1686147Z 2025-07-17T09:05:52.1686313Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2025-07-17T09:05:52.1686641Z sizes. Hence, the inversion process can get ambiguous. 2025-07-17T09:05:52.1686941Z To accommodate this, you can provide the needed output size 2025-07-17T09:05:52.1687272Z as an additional argument :attr:`output_size` in the forward call. 2025-07-17T09:05:52.1687556Z See the Inputs and Example below. 2025-07-17T09:05:52.1687703Z 2025-07-17T09:05:52.1687769Z Args: 2025-07-17T09:05:52.1687978Z kernel_size (int or tuple): Size of the max pooling window. 2025-07-17T09:05:52.1688275Z stride (int or tuple): Stride of the max pooling window. 2025-07-17T09:05:52.1688536Z It is set to :attr:`kernel_size` by default. 2025-07-17T09:05:52.1688804Z padding (int or tuple): Padding that was added to the input 2025-07-17T09:05:52.1688971Z 2025-07-17T09:05:52.1689045Z Inputs: 2025-07-17T09:05:52.1689226Z - `input`: the input Tensor to invert 2025-07-17T09:05:52.1689507Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2025-07-17T09:05:52.1689800Z - `output_size` (optional): the targeted output size 2025-07-17T09:05:52.1689970Z 2025-07-17T09:05:52.1690029Z Shape: 2025-07-17T09:05:52.1690232Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2025-07-17T09:05:52.1690536Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2025-07-17T09:05:52.1690768Z 2025-07-17T09:05:52.1690836Z .. math:: 2025-07-17T09:05:52.1691113Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2025-07-17T09:05:52.1691343Z 2025-07-17T09:05:52.1691402Z .. math:: 2025-07-17T09:05:52.1691638Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2025-07-17T09:05:52.1691849Z 2025-07-17T09:05:52.1691964Z or as given by :attr:`output_size` in the call operator 2025-07-17T09:05:52.1692131Z 2025-07-17T09:05:52.1692195Z Example:: 2025-07-17T09:05:52.1692293Z 2025-07-17T09:05:52.1692400Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2025-07-17T09:05:52.1692657Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2025-07-17T09:05:52.1692894Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2025-07-17T09:05:52.1693116Z [ 5., 6., 7., 8.], 2025-07-17T09:05:52.1693435Z [ 9., 10., 11., 12.], 2025-07-17T09:05:52.1693725Z [13., 14., 15., 16.]]]]) 2025-07-17T09:05:52.1693939Z >>> output, indices = pool(input) 2025-07-17T09:05:52.1694147Z >>> unpool(output, indices) 2025-07-17T09:05:52.1694343Z tensor([[[[ 0., 0., 0., 0.], 2025-07-17T09:05:52.1694538Z [ 0., 6., 0., 8.], 2025-07-17T09:05:52.1694831Z [ 0., 0., 0., 0.], 2025-07-17T09:05:52.1695027Z [ 0., 14., 0., 16.]]]]) 2025-07-17T09:05:52.1695281Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2025-07-17T09:05:52.1695570Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2025-07-17T09:05:52.1695801Z [ 6., 7., 8., 9., 10.], 2025-07-17T09:05:52.1696002Z [11., 12., 13., 14., 15.], 2025-07-17T09:05:52.1696206Z [16., 17., 18., 19., 20.]]]]) 2025-07-17T09:05:52.1696423Z >>> output, indices = pool(input) 2025-07-17T09:05:52.1696680Z >>> # This call will not work without specifying output_size 2025-07-17T09:05:52.1696959Z >>> unpool(output, indices, output_size=input.size()) 2025-07-17T09:05:52.1697191Z tensor([[[[ 0., 0., 0., 0., 0.], 2025-07-17T09:05:52.1697389Z [ 0., 7., 0., 9., 0.], 2025-07-17T09:05:52.1697585Z [ 0., 0., 0., 0., 0.], 2025-07-17T09:05:52.1697780Z [ 0., 17., 0., 19., 0.]]]]) 2025-07-17T09:05:52.1697912Z 2025-07-17T09:05:52.1697915Z 2025-07-17T09:05:52.1697975Z 2025-07-17T09:05:52.1698211Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.1698433Z 2025-07-17T09:05:52.1787146Z msg = Cannot scrape callname=EmbeddingBag in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py line=272. 2025-07-17T09:05:52.1787764Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.1788206Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2025-07-17T09:05:52.1788474Z 2025-07-17T09:05:52.1788672Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2025-07-17T09:05:52.1789013Z and with 2D inputs, this class 2025-07-17T09:05:52.1789159Z 2025-07-17T09:05:52.1789348Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2025-07-17T09:05:52.1789766Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2025-07-17T09:05:52.1790197Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2025-07-17T09:05:52.1790434Z 2025-07-17T09:05:52.1790637Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2025-07-17T09:05:52.1790965Z operations. 2025-07-17T09:05:52.1791069Z 2025-07-17T09:05:52.1791225Z EmbeddingBag also supports per-sample weights as an argument to the forward 2025-07-17T09:05:52.1791589Z pass. This scales the output of the Embedding before performing a weighted 2025-07-17T09:05:52.1791952Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2025-07-17T09:05:52.1792312Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2025-07-17T09:05:52.1792591Z :attr:`per_sample_weights`. 2025-07-17T09:05:52.1792716Z 2025-07-17T09:05:52.1792785Z Args: 2025-07-17T09:05:52.1792985Z num_embeddings (int): size of the dictionary of embeddings 2025-07-17T09:05:52.1793275Z embedding_dim (int): the size of each embedding vector 2025-07-17T09:05:52.1793624Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2025-07-17T09:05:52.1794139Z is renormalized to have norm :attr:`max_norm`. 2025-07-17T09:05:52.1794589Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2025-07-17T09:05:52.1795042Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2025-07-17T09:05:52.1795553Z the words in the mini-batch. Default ``False``. 2025-07-17T09:05:52.1795840Z Note: this option is not supported when ``mode="max"``. 2025-07-17T09:05:52.1796171Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2025-07-17T09:05:52.1796522Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2025-07-17T09:05:52.1796855Z into consideration. ``"mean"`` computes the average of the values 2025-07-17T09:05:52.1797171Z in the bag, ``"max"`` computes the max value over each bag. 2025-07-17T09:05:52.1797434Z Default: ``"mean"`` 2025-07-17T09:05:52.1797765Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2025-07-17T09:05:52.1798174Z Notes for more details regarding sparse gradients. Note: this option is not 2025-07-17T09:05:52.1798484Z supported when ``mode="max"``. 2025-07-17T09:05:52.1798846Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2025-07-17T09:05:52.1799262Z is equivalent to the size of `indices`. This matches the CSR format. 2025-07-17T09:05:52.1799650Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2025-07-17T09:05:52.1800073Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2025-07-17T09:05:52.1800447Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2025-07-17T09:05:52.1800810Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2025-07-17T09:05:52.1801179Z zeros, but can be updated to another value to be used as the padding vector. 2025-07-17T09:05:52.1801539Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2025-07-17T09:05:52.1801825Z reduction. 2025-07-17T09:05:52.1801955Z 2025-07-17T09:05:52.1802021Z Attributes: 2025-07-17T09:05:52.1802296Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2025-07-17T09:05:52.1802635Z initialized from :math:`\mathcal{N}(0, 1)`. 2025-07-17T09:05:52.1802797Z 2025-07-17T09:05:52.1802874Z Examples:: 2025-07-17T09:05:52.1802973Z 2025-07-17T09:05:52.1803080Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2025-07-17T09:05:52.1803348Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2025-07-17T09:05:52.1803596Z >>> # a batch of 2 samples of 4 indices each 2025-07-17T09:05:52.1803877Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2025-07-17T09:05:52.1804178Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2025-07-17T09:05:52.1804436Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-07-17T09:05:52.1804684Z >>> embedding_sum(input, offsets) 2025-07-17T09:05:52.1804913Z tensor([[-0.8861, -5.4350, -0.0523], 2025-07-17T09:05:52.1805120Z [ 1.1306, -2.5798, -1.0044]]) 2025-07-17T09:05:52.1805245Z 2025-07-17T09:05:52.1805402Z >>> # Example with padding_idx 2025-07-17T09:05:52.1805682Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2025-07-17T09:05:52.1806038Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2025-07-17T09:05:52.1806304Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2025-07-17T09:05:52.1806536Z >>> embedding_sum(input, offsets) 2025-07-17T09:05:52.1806841Z tensor([[ 0.0000, 0.0000, 0.0000], 2025-07-17T09:05:52.1807042Z [-0.7082, 3.2145, -2.6251]]) 2025-07-17T09:05:52.1807171Z 2025-07-17T09:05:52.1807282Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2025-07-17T09:05:52.1807551Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2025-07-17T09:05:52.1807803Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2025-07-17T09:05:52.1808044Z embedding.weight, 2025-07-17T09:05:52.1808263Z padding_idx=embedding.padding_idx, 2025-07-17T09:05:52.1808489Z mode='sum') 2025-07-17T09:05:52.1808672Z 2025-07-17T09:05:52.1808907Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.1809130Z 2025-07-17T09:05:52.2260848Z msg = Cannot scrape callname=TripletMarginWithDistanceLoss in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py line=1718. 2025-07-17T09:05:52.2261609Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.2261981Z Creates a criterion that measures the triplet loss given input 2025-07-17T09:05:52.2262311Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2025-07-17T09:05:52.2262633Z positive, and negative examples, respectively), and a nonnegative, 2025-07-17T09:05:52.2262991Z real-valued function ("distance function") used to compute the relationship 2025-07-17T09:05:52.2263341Z between the anchor and positive example ("positive distance") and the 2025-07-17T09:05:52.2263653Z anchor and negative example ("negative distance"). 2025-07-17T09:05:52.2263850Z 2025-07-17T09:05:52.2263990Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2025-07-17T09:05:52.2264256Z can be described as: 2025-07-17T09:05:52.2264377Z 2025-07-17T09:05:52.2264453Z .. math:: 2025-07-17T09:05:52.2264658Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2025-07-17T09:05:52.2264932Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2025-07-17T09:05:52.2265103Z 2025-07-17T09:05:52.2265261Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2025-07-17T09:05:52.2265752Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2025-07-17T09:05:52.2266142Z and :math:`margin` is a nonnegative margin representing the minimum difference 2025-07-17T09:05:52.2266497Z between the positive and negative distances that is required for the loss to 2025-07-17T09:05:52.2266854Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2025-07-17T09:05:52.2267142Z that the distance function can handle. 2025-07-17T09:05:52.2267282Z 2025-07-17T09:05:52.2267370Z If :attr:`reduction` is not ``'none'`` 2025-07-17T09:05:52.2267577Z (default ``'mean'``), then: 2025-07-17T09:05:52.2267694Z 2025-07-17T09:05:52.2267768Z .. math:: 2025-07-17T09:05:52.2267912Z \ell(x, y) = 2025-07-17T09:05:52.2268089Z \begin{cases} 2025-07-17T09:05:52.2268320Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2025-07-17T09:05:52.2268621Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2025-07-17T09:05:52.2268864Z \end{cases} 2025-07-17T09:05:52.2268965Z 2025-07-17T09:05:52.2269106Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2025-07-17T09:05:52.2269455Z loss for input tensors using the :math:`l_p` distance as the distance function. 2025-07-17T09:05:52.2270174Z 2025-07-17T09:05:52.2270241Z Args: 2025-07-17T09:05:52.2270599Z distance_function (Callable, optional): A nonnegative, real-valued function that 2025-07-17T09:05:52.2271024Z quantifies the closeness of two tensors. If not specified, 2025-07-17T09:05:52.2271373Z `nn.PairwiseDistance` will be used. Default: ``None`` 2025-07-17T09:05:52.2271902Z margin (float, optional): A nonnegative margin representing the minimum difference 2025-07-17T09:05:52.2272293Z between the positive and negative distances required for the loss to be 0. Larger 2025-07-17T09:05:52.2272697Z margins penalize cases where the negative examples are not distant enough from the 2025-07-17T09:05:52.2273044Z anchors, relative to the positives. Default: :math:`1`. 2025-07-17T09:05:52.2273383Z swap (bool, optional): Whether to use the distance swap described in the paper 2025-07-17T09:05:52.2273752Z `Learning shallow convolutional feature descriptors with triplet losses` by 2025-07-17T09:05:52.2274119Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2025-07-17T09:05:52.2274483Z negative example than the anchor is, swaps the positive example and the anchor in 2025-07-17T09:05:52.2274786Z the loss computation. Default: ``False``. 2025-07-17T09:05:52.2275109Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2025-07-17T09:05:52.2275454Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2025-07-17T09:05:52.2275746Z ``'mean'``: the sum of the output will be divided by the number of 2025-07-17T09:05:52.2276079Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2025-07-17T09:05:52.2276298Z 2025-07-17T09:05:52.2276301Z 2025-07-17T09:05:52.2276364Z Shape: 2025-07-17T09:05:52.2276604Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2025-07-17T09:05:52.2276908Z as supported by the distance function. 2025-07-17T09:05:52.2277214Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2025-07-17T09:05:52.2277491Z otherwise. 2025-07-17T09:05:52.2277590Z 2025-07-17T09:05:52.2277664Z Examples: 2025-07-17T09:05:52.2277751Z 2025-07-17T09:05:52.2277832Z >>> # Initialize embeddings 2025-07-17T09:05:52.2278041Z >>> embedding = nn.Embedding(1000, 128) 2025-07-17T09:05:52.2278259Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2025-07-17T09:05:52.2278518Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2025-07-17T09:05:52.2278753Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2025-07-17T09:05:52.2278969Z >>> anchor = embedding(anchor_ids) 2025-07-17T09:05:52.2279184Z >>> positive = embedding(positive_ids) 2025-07-17T09:05:52.2279397Z >>> negative = embedding(negative_ids) 2025-07-17T09:05:52.2279592Z >>> 2025-07-17T09:05:52.2279764Z >>> # Built-in Distance Function 2025-07-17T09:05:52.2279970Z >>> triplet_loss = \ 2025-07-17T09:05:52.2280261Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2025-07-17T09:05:52.2280599Z >>> output = triplet_loss(anchor, positive, negative) 2025-07-17T09:05:52.2280832Z >>> output.backward() 2025-07-17T09:05:52.2281001Z >>> 2025-07-17T09:05:52.2281165Z >>> # Custom Distance Function 2025-07-17T09:05:52.2281376Z >>> def l_infinity(x1, x2): 2025-07-17T09:05:52.2281603Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2025-07-17T09:05:52.2281823Z >>> 2025-07-17T09:05:52.2282031Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2025-07-17T09:05:52.2282279Z >>> triplet_loss = ( 2025-07-17T09:05:52.2282565Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2025-07-17T09:05:52.2282893Z >>> output = triplet_loss(anchor, positive, negative) 2025-07-17T09:05:52.2283200Z >>> output.backward() 2025-07-17T09:05:52.2283429Z >>> 2025-07-17T09:05:52.2283580Z >>> # Custom Distance Function (Lambda) 2025-07-17T09:05:52.2283794Z >>> triplet_loss = ( 2025-07-17T09:05:52.2283987Z >>> nn.TripletMarginWithDistanceLoss( 2025-07-17T09:05:52.2284273Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2025-07-17T09:05:52.2284685Z >>> output = triplet_loss(anchor, positive, negative) 2025-07-17T09:05:52.2284919Z >>> output.backward() 2025-07-17T09:05:52.2285036Z 2025-07-17T09:05:52.2285101Z Reference: 2025-07-17T09:05:52.2285379Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2025-07-17T09:05:52.2285785Z https://bmva-archive.org.uk/bmvc/2016/papers/paper119/index.html 2025-07-17T09:05:52.2286051Z 2025-07-17T09:05:52.2286286Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2025-07-17T09:05:52.2286504Z 2025-07-17T09:05:52.2286833Z msg = Cannot scrape callname=CTCLoss in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py line=1852. 2025-07-17T09:05:52.2287337Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.2287657Z The Connectionist Temporal Classification loss. 2025-07-17T09:05:52.2287816Z 2025-07-17T09:05:52.2288045Z Calculates loss between a continuous (unsegmented) time series and a target sequence. CTCLoss sums over the 2025-07-17T09:05:52.2288547Z probability of possible alignments of input to target, producing a loss value which is differentiable 2025-07-17T09:05:52.2289010Z with respect to each input node. The alignment of input to target is assumed to be "many-to-one", which 2025-07-17T09:05:52.2289450Z limits the length of the target sequence such that it must be :math:`\leq` the input length. 2025-07-17T09:05:52.2289687Z 2025-07-17T09:05:52.2289759Z Args: 2025-07-17T09:05:52.2289954Z blank (int, optional): blank label. Default :math:`0`. 2025-07-17T09:05:52.2290281Z reduction (str, optional): Specifies the reduction to apply to the output: 2025-07-17T09:05:52.2290615Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2025-07-17T09:05:52.2290927Z ``'mean'``: the output losses will be divided by the target lengths and 2025-07-17T09:05:52.2291278Z then the mean over the batch is taken, ``'sum'``: the output losses will be summed. 2025-07-17T09:05:52.2291566Z Default: ``'mean'`` 2025-07-17T09:05:52.2291763Z zero_infinity (bool, optional): 2025-07-17T09:05:52.2292028Z Whether to zero infinite losses and the associated gradients. 2025-07-17T09:05:52.2292284Z Default: ``False`` 2025-07-17T09:05:52.2292521Z Infinite losses mainly occur when the inputs are too short 2025-07-17T09:05:52.2292776Z to be aligned to the targets. 2025-07-17T09:05:52.2292911Z 2025-07-17T09:05:52.2292976Z Shape: 2025-07-17T09:05:52.2293184Z - Log_probs: Tensor of size :math:`(T, N, C)` or :math:`(T, C)`, 2025-07-17T09:05:52.2293454Z where :math:`T = \text{input length}`, 2025-07-17T09:05:52.2293685Z :math:`N = \text{batch size}`, and 2025-07-17T09:05:52.2293930Z :math:`C = \text{number of classes (including blank)}`. 2025-07-17T09:05:52.2294238Z The logarithmized probabilities of the outputs (e.g. obtained with 2025-07-17T09:05:52.2294533Z :func:`torch.nn.functional.log_softmax`). 2025-07-17T09:05:52.2294762Z - Targets: Tensor of size :math:`(N, S)` or 2025-07-17T09:05:52.2295012Z :math:`(\operatorname{sum}(\text{target\_lengths}))`, 2025-07-17T09:05:52.2295265Z where :math:`N = \text{batch size}` and 2025-07-17T09:05:52.2295516Z :math:`S = \text{max target length, if shape is } (N, S)`. 2025-07-17T09:05:52.2295811Z It represents the target sequences. Each element in the target 2025-07-17T09:05:52.2296235Z sequence is a class index. And the target index cannot be blank (default=0). 2025-07-17T09:05:52.2296606Z In the :math:`(N, S)` form, targets are padded to the 2025-07-17T09:05:52.2296860Z length of the longest sequence, and stacked. 2025-07-17T09:05:52.2297132Z In the :math:`(\operatorname{sum}(\text{target\_lengths}))` form, 2025-07-17T09:05:52.2297509Z the targets are assumed to be un-padded and 2025-07-17T09:05:52.2297775Z concatenated within 1 dimension. 2025-07-17T09:05:52.2298044Z - Input_lengths: Tuple or tensor of size :math:`(N)` or :math:`()`, 2025-07-17T09:05:52.2298359Z where :math:`N = \text{batch size}`. It represents the lengths of the 2025-07-17T09:05:52.2298672Z inputs (must each be :math:`\leq T`). And the lengths are specified 2025-07-17T09:05:52.2299000Z for each sequence to achieve masking under the assumption that sequences 2025-07-17T09:05:52.2299271Z are padded to equal lengths. 2025-07-17T09:05:52.2299539Z - Target_lengths: Tuple or tensor of size :math:`(N)` or :math:`()`, 2025-07-17T09:05:52.2299864Z where :math:`N = \text{batch size}`. It represents lengths of the targets. 2025-07-17T09:05:52.2300192Z Lengths are specified for each sequence to achieve masking under the 2025-07-17T09:05:52.2300546Z assumption that sequences are padded to equal lengths. If target shape is 2025-07-17T09:05:52.2300871Z :math:`(N,S)`, target_lengths are effectively the stop index 2025-07-17T09:05:52.2301202Z :math:`s_n` for each target sequence, such that ``target_n = targets[n,0:s_n]`` for 2025-07-17T09:05:52.2301529Z each target in a batch. Lengths must each be :math:`\leq S` 2025-07-17T09:05:52.2301861Z If the targets are given as a 1d tensor that is the concatenation of individual 2025-07-17T09:05:52.2302215Z targets, the target_lengths must add up to the total length of the tensor. 2025-07-17T09:05:52.2302556Z - Output: scalar if :attr:`reduction` is ``'mean'`` (default) or 2025-07-17T09:05:52.2302880Z ``'sum'``. If :attr:`reduction` is ``'none'``, then :math:`(N)` if input is batched or 2025-07-17T09:05:52.2303000Z :math:`()` if input is unbatched, where :math:`N = \text{batch size}`. 2025-07-17T09:05:52.2303004Z 2025-07-17T09:05:52.2303080Z Examples: 2025-07-17T09:05:52.2303083Z 2025-07-17T09:05:52.2303164Z >>> # Target are to be padded 2025-07-17T09:05:52.2303257Z >>> T = 50 # Input sequence length 2025-07-17T09:05:52.2303347Z >>> C = 20 # Number of classes (including blank) 2025-07-17T09:05:52.2303428Z >>> N = 16 # Batch size 2025-07-17T09:05:52.2303571Z >>> S = 30 # Target sequence length of longest target in batch (padding length) 2025-07-17T09:05:52.2303696Z >>> S_min = 10 # Minimum target length, for demonstration purposes 2025-07-17T09:05:52.2303757Z >>> 2025-07-17T09:05:52.2303885Z >>> # Initialize random batch of input vectors, for *size = (T,N,C) 2025-07-17T09:05:52.2304017Z >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_() 2025-07-17T09:05:52.2304085Z >>> 2025-07-17T09:05:52.2304202Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-07-17T09:05:52.2304348Z >>> target = torch.randint(low=1, high=C, size=(N, S), dtype=torch.long) 2025-07-17T09:05:52.2304411Z >>> 2025-07-17T09:05:52.2304544Z >>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long) 2025-07-17T09:05:52.2304630Z >>> target_lengths = torch.randint( 2025-07-17T09:05:52.2304699Z ... low=S_min, 2025-07-17T09:05:52.2304772Z ... high=S, 2025-07-17T09:05:52.2304839Z ... size=(N,), 2025-07-17T09:05:52.2304924Z ... dtype=torch.long, 2025-07-17T09:05:52.2304984Z ... ) 2025-07-17T09:05:52.2305068Z >>> ctc_loss = nn.CTCLoss() 2025-07-17T09:05:52.2305254Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-07-17T09:05:52.2305434Z >>> loss.backward() 2025-07-17T09:05:52.2305490Z >>> 2025-07-17T09:05:52.2305559Z >>> 2025-07-17T09:05:52.2305637Z >>> # Target are to be un-padded 2025-07-17T09:05:52.2305727Z >>> T = 50 # Input sequence length 2025-07-17T09:05:52.2305815Z >>> C = 20 # Number of classes (including blank) 2025-07-17T09:05:52.2306039Z >>> N = 16 # Batch size 2025-07-17T09:05:52.2306102Z >>> 2025-07-17T09:05:52.2306239Z >>> # Initialize random batch of input vectors, for *size = (T,N,C) 2025-07-17T09:05:52.2306369Z >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_() 2025-07-17T09:05:52.2306496Z >>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long) 2025-07-17T09:05:52.2306567Z >>> 2025-07-17T09:05:52.2306685Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-07-17T09:05:52.2306839Z >>> target_lengths = torch.randint(low=1, high=T, size=(N,), dtype=torch.long) 2025-07-17T09:05:52.2306915Z >>> target = torch.randint( 2025-07-17T09:05:52.2306988Z ... low=1, 2025-07-17T09:05:52.2307049Z ... high=C, 2025-07-17T09:05:52.2307136Z ... size=(sum(target_lengths),), 2025-07-17T09:05:52.2307208Z ... dtype=torch.long, 2025-07-17T09:05:52.2307276Z ... ) 2025-07-17T09:05:52.2307348Z >>> ctc_loss = nn.CTCLoss() 2025-07-17T09:05:52.2307470Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-07-17T09:05:52.2307539Z >>> loss.backward() 2025-07-17T09:05:52.2307608Z >>> 2025-07-17T09:05:52.2307669Z >>> 2025-07-17T09:05:52.2307788Z >>> # Target are to be un-padded and unbatched (effectively N=1) 2025-07-17T09:05:52.2307870Z >>> T = 50 # Input sequence length 2025-07-17T09:05:52.2307955Z >>> C = 20 # Number of classes (including blank) 2025-07-17T09:05:52.2308027Z >>> 2025-07-17T09:05:52.2308146Z >>> # Initialize random batch of input vectors, for *size = (T,C) 2025-07-17T09:05:52.2308252Z >>> # xdoctest: +SKIP("FIXME: error in doctest") 2025-07-17T09:05:52.2308373Z >>> input = torch.randn(T, C).log_softmax(1).detach().requires_grad_() 2025-07-17T09:05:52.2308486Z >>> input_lengths = torch.tensor(T, dtype=torch.long) 2025-07-17T09:05:52.2308549Z >>> 2025-07-17T09:05:52.2308670Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-07-17T09:05:52.2308807Z >>> target_lengths = torch.randint(low=1, high=T, size=(), dtype=torch.long) 2025-07-17T09:05:52.2308886Z >>> target = torch.randint( 2025-07-17T09:05:52.2308946Z ... low=1, 2025-07-17T09:05:52.2309018Z ... high=C, 2025-07-17T09:05:52.2309092Z ... size=(target_lengths,), 2025-07-17T09:05:52.2309168Z ... dtype=torch.long, 2025-07-17T09:05:52.2309233Z ... ) 2025-07-17T09:05:52.2309316Z >>> ctc_loss = nn.CTCLoss() 2025-07-17T09:05:52.2309436Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-07-17T09:05:52.2309509Z >>> loss.backward() 2025-07-17T09:05:52.2309521Z 2025-07-17T09:05:52.2309585Z Reference: 2025-07-17T09:05:52.2309698Z A. Graves et al.: Connectionist Temporal Classification: 2025-07-17T09:05:52.2309854Z Labelling Unsegmented Sequence Data with Recurrent Neural Networks: 2025-07-17T09:05:52.2309965Z https://www.cs.toronto.edu/~graves/icml_2006.pdf 2025-07-17T09:05:52.2309968Z 2025-07-17T09:05:52.2310044Z Note: 2025-07-17T09:05:52.2310200Z In order to use CuDNN, the following must be satisfied: :attr:`targets` must be 2025-07-17T09:05:52.2310364Z in concatenated format, all :attr:`input_lengths` must be `T`. :math:`blank=0`, 2025-07-17T09:05:52.2310498Z :attr:`target_lengths` :math:`\leq 256`, the integer arguments must be of 2025-07-17T09:05:52.2310661Z dtype :attr:`torch.int32`. 2025-07-17T09:05:52.2310738Z 2025-07-17T09:05:52.2310898Z The regular implementation uses the (more common in PyTorch) `torch.long` dtype. 2025-07-17T09:05:52.2310901Z 2025-07-17T09:05:52.2310910Z 2025-07-17T09:05:52.2310971Z Note: 2025-07-17T09:05:52.2311123Z In some circumstances when using the CUDA backend with CuDNN, this operator 2025-07-17T09:05:52.2311389Z may select a nondeterministic algorithm to increase performance. If this is 2025-07-17T09:05:52.2311546Z undesirable, you can try to make the operation deterministic (potentially at 2025-07-17T09:05:52.2311704Z a performance cost) by setting ``torch.backends.cudnn.deterministic = 2025-07-17T09:05:52.2311768Z True``. 2025-07-17T09:05:52.2311899Z Please see the notes on :doc:`/notes/randomness` for background. 2025-07-17T09:05:52.2311959Z 2025-07-17T09:05:52.2312120Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.2312125Z 2025-07-17T09:05:52.2614765Z msg = Cannot scrape callname=RelaxedBernoulli in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/relaxed_bernoulli.py line=120. 2025-07-17T09:05:52.2615010Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.2615015Z 2025-07-17T09:05:52.2615161Z Creates a RelaxedBernoulli distribution, parametrized by 2025-07-17T09:05:52.2615326Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2025-07-17T09:05:52.2615469Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2025-07-17T09:05:52.2615602Z so the values are in (0, 1), and has reparametrizable samples. 2025-07-17T09:05:52.2615606Z 2025-07-17T09:05:52.2615692Z Example:: 2025-07-17T09:05:52.2615704Z 2025-07-17T09:05:52.2615805Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-07-17T09:05:52.2615897Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2025-07-17T09:05:52.2616004Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2025-07-17T09:05:52.2616076Z >>> m.sample() 2025-07-17T09:05:52.2616169Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2025-07-17T09:05:52.2616172Z 2025-07-17T09:05:52.2616232Z Args: 2025-07-17T09:05:52.2616340Z temperature (Tensor): relaxation temperature 2025-07-17T09:05:52.2616461Z probs (Number, Tensor): the probability of sampling `1` 2025-07-17T09:05:52.2616585Z logits (Number, Tensor): the log-odds of sampling `1` 2025-07-17T09:05:52.2616590Z 2025-07-17T09:05:52.2616749Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.2616752Z 2025-07-17T09:05:52.4709005Z msg = Cannot scrape callname=LowRankMultivariateNormal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/lowrank_multivariate_normal.py line=56. 2025-07-17T09:05:52.4709211Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.4709231Z 2025-07-17T09:05:52.4709437Z Creates a multivariate normal distribution with covariance matrix having a low-rank form 2025-07-17T09:05:52.4709590Z parameterized by :attr:`cov_factor` and :attr:`cov_diag`:: 2025-07-17T09:05:52.4709594Z 2025-07-17T09:05:52.4709725Z covariance_matrix = cov_factor @ cov_factor.T + cov_diag 2025-07-17T09:05:52.4709728Z 2025-07-17T09:05:52.4709796Z Example: 2025-07-17T09:05:52.4709919Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK) 2025-07-17T09:05:52.4710021Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-07-17T09:05:52.4710117Z >>> m = LowRankMultivariateNormal( 2025-07-17T09:05:52.4710255Z ... torch.zeros(2), torch.tensor([[1.0], [0.0]]), torch.ones(2) 2025-07-17T09:05:52.4710326Z ... ) 2025-07-17T09:05:52.4710504Z >>> m.sample() # normally distributed with mean=`[0,0]`, cov_factor=`[[1],[0]]`, cov_diag=`[1,1]` 2025-07-17T09:05:52.4710595Z tensor([-0.2102, -0.5429]) 2025-07-17T09:05:52.4710599Z 2025-07-17T09:05:52.4710887Z Args: 2025-07-17T09:05:52.4711049Z loc (Tensor): mean of the distribution with shape `batch_shape + event_shape` 2025-07-17T09:05:52.4711343Z cov_factor (Tensor): factor part of low-rank form of covariance matrix with shape 2025-07-17T09:05:52.4711440Z `batch_shape + event_shape + (rank,)` 2025-07-17T09:05:52.4711589Z cov_diag (Tensor): diagonal part of low-rank form of covariance matrix with shape 2025-07-17T09:05:52.4711880Z `batch_shape + event_shape` 2025-07-17T09:05:52.4711884Z 2025-07-17T09:05:52.4711944Z Note: 2025-07-17T09:05:52.4712115Z The computation for determinant and inverse of covariance matrix is avoided when 2025-07-17T09:05:52.4712260Z `cov_factor.shape[1] << cov_factor.shape[0]` thanks to `Woodbury matrix identity 2025-07-17T09:05:52.4712412Z `_ and 2025-07-17T09:05:52.4712588Z `matrix determinant lemma `_. 2025-07-17T09:05:52.4712748Z Thanks to these formulas, we just need to compute the determinant and inverse of 2025-07-17T09:05:52.4712839Z the small size "capacitance" matrix:: 2025-07-17T09:05:52.4712843Z 2025-07-17T09:05:52.4712976Z capacitance = I + cov_factor.T @ inv(cov_diag) @ cov_factor 2025-07-17T09:05:52.4712979Z 2025-07-17T09:05:52.4713135Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.4713138Z 2025-07-17T09:05:52.4756434Z msg = Cannot scrape callname=RelaxedOneHotCategorical in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/relaxed_categorical.py line=109. 2025-07-17T09:05:52.4756646Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.4756659Z 2025-07-17T09:05:52.4756810Z Creates a RelaxedOneHotCategorical distribution parametrized by 2025-07-17T09:05:52.4756951Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2025-07-17T09:05:52.4757107Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2025-07-17T09:05:52.4757228Z its samples are on simplex, and are reparametrizable. 2025-07-17T09:05:52.4757242Z 2025-07-17T09:05:52.4757316Z Example:: 2025-07-17T09:05:52.4757320Z 2025-07-17T09:05:52.4757413Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-07-17T09:05:52.4757532Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2025-07-17T09:05:52.4757628Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2025-07-17T09:05:52.4757710Z >>> m.sample() 2025-07-17T09:05:52.4757790Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2025-07-17T09:05:52.4757793Z 2025-07-17T09:05:52.4757866Z Args: 2025-07-17T09:05:52.4757955Z temperature (Tensor): relaxation temperature 2025-07-17T09:05:52.4758052Z probs (Tensor): event probabilities 2025-07-17T09:05:52.4758168Z logits (Tensor): unnormalized log probability for each event 2025-07-17T09:05:52.4758172Z 2025-07-17T09:05:52.4758341Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.4758348Z 2025-07-17T09:05:52.4881875Z msg = Cannot scrape callname=MixtureSameFamily in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/mixture_same_family.py line=15. 2025-07-17T09:05:52.4882111Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.4882116Z 2025-07-17T09:05:52.4882295Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2025-07-17T09:05:52.4882457Z distribution where all component are from different parameterizations of 2025-07-17T09:05:52.4882604Z the same distribution type. It is parameterized by a `Categorical` 2025-07-17T09:05:52.4882734Z "selecting distribution" (over `k` component) and a component 2025-07-17T09:05:52.4882876Z distribution, i.e., a `Distribution` with a rightmost batch shape 2025-07-17T09:05:52.4882982Z (equal to `[k]`) which indexes each (batch of) component. 2025-07-17T09:05:52.4883223Z 2025-07-17T09:05:52.4883307Z Examples:: 2025-07-17T09:05:52.4883386Z 2025-07-17T09:05:52.4883472Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T09:05:52.4883605Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2025-07-17T09:05:52.4883685Z >>> # weighted normal distributions 2025-07-17T09:05:52.4883768Z >>> mix = D.Categorical(torch.ones(5,)) 2025-07-17T09:05:52.4883873Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2025-07-17T09:05:52.4884117Z >>> gmm = MixtureSameFamily(mix, comp) 2025-07-17T09:05:52.4884133Z 2025-07-17T09:05:52.4884256Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2025-07-17T09:05:52.4884347Z >>> # weighted bivariate normal distributions 2025-07-17T09:05:52.4884431Z >>> mix = D.Categorical(torch.ones(5,)) 2025-07-17T09:05:52.4884509Z >>> comp = D.Independent(D.Normal( 2025-07-17T09:05:52.4884612Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2025-07-17T09:05:52.4884694Z >>> gmm = MixtureSameFamily(mix, comp) 2025-07-17T09:05:52.4884702Z 2025-07-17T09:05:52.4884849Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2025-07-17T09:05:52.4884980Z >>> # consisting of 5 random weighted bivariate normal distributions 2025-07-17T09:05:52.4885073Z >>> mix = D.Categorical(torch.rand(3,5)) 2025-07-17T09:05:52.4885150Z >>> comp = D.Independent(D.Normal( 2025-07-17T09:05:52.4885253Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2025-07-17T09:05:52.4885329Z >>> gmm = MixtureSameFamily(mix, comp) 2025-07-17T09:05:52.4885332Z 2025-07-17T09:05:52.4885406Z Args: 2025-07-17T09:05:52.4885537Z mixture_distribution: `torch.distributions.Categorical`-like 2025-07-17T09:05:52.4885668Z instance. Manages the probability of selecting component. 2025-07-17T09:05:52.4885778Z The number of categories must match the rightmost batch 2025-07-17T09:05:52.4885938Z dimension of the `component_distribution`. Must have either 2025-07-17T09:05:52.4886040Z scalar `batch_shape` or `batch_shape` matching 2025-07-17T09:05:52.4886145Z `component_distribution.batch_shape[:-1]` 2025-07-17T09:05:52.4886276Z component_distribution: `torch.distributions.Distribution`-like 2025-07-17T09:05:52.4886395Z instance. Right-most batch dimension indexes component. 2025-07-17T09:05:52.4886398Z 2025-07-17T09:05:52.4886556Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.4886561Z 2025-07-17T09:05:52.5111504Z msg = Cannot scrape callname=record_function in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py line=734. 2025-07-17T09:05:52.5112101Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.5112575Z Context manager/function decorator that adds a label to a code block/function when running autograd profiler. 2025-07-17T09:05:52.5112998Z Label will only appear if CPU activity tracing is enabled. 2025-07-17T09:05:52.5113190Z 2025-07-17T09:05:52.5113292Z It is useful when tracing the code profile. 2025-07-17T09:05:52.5113451Z 2025-07-17T09:05:52.5113524Z Args: 2025-07-17T09:05:52.5113718Z name (str): Label assigned to the block of code. 2025-07-17T09:05:52.5113991Z node_id (int): ID of node, for distributed profiling. Unset in 2025-07-17T09:05:52.5114253Z non-distributed cases. 2025-07-17T09:05:52.5114387Z 2025-07-17T09:05:52.5114460Z Example: 2025-07-17T09:05:52.5114673Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD_PROFILER) 2025-07-17T09:05:52.5114934Z >>> x = torch.randn((1, 1), requires_grad=True) 2025-07-17T09:05:52.5115195Z >>> with torch.autograd.profiler.profile() as prof: 2025-07-17T09:05:52.5115429Z ... y = x**2 2025-07-17T09:05:52.5115649Z ... with torch.autograd.profiler.record_function( 2025-07-17T09:05:52.5115885Z ... "label-z" 2025-07-17T09:05:52.5116078Z ... ): # label the block 2025-07-17T09:05:52.5116416Z ... z = y**3 2025-07-17T09:05:52.5116678Z ... y.backward() 2025-07-17T09:05:52.5116860Z >>> # xdoctest: +IGNORE_WANT 2025-07-17T09:05:52.5117084Z >>> # NOTE: some columns were removed for brevity 2025-07-17T09:05:52.5117378Z >>> print(prof.key_averages().table(sort_by="self_cpu_time_total")) 2025-07-17T09:05:52.5117864Z ----------------------------------- --------------- --------------- --------------- 2025-07-17T09:05:52.5118192Z Name Self CPU total % CPU time avg Number of Calls 2025-07-17T09:05:52.5118515Z ----------------------------------- --------------- --------------- --------------- 2025-07-17T09:05:52.5118790Z pow 60.77% 47.470us 3 2025-07-17T09:05:52.5119026Z mul 21.73% 25.465us 2 2025-07-17T09:05:52.5119282Z PowBackward0 12.03% 121.891us 1 2025-07-17T09:05:52.5119611Z torch::autograd::AccumulateGrad 2.70% 6.324us 1 2025-07-17T09:05:52.5119914Z label-z 2.13% 12.421us 1 2025-07-17T09:05:52.5120196Z torch::autograd::GraphRoot 0.64% 1.503us 1 2025-07-17T09:05:52.5120510Z ----------------------------------- --------------- --------------- --------------- 2025-07-17T09:05:52.5120781Z Self CPU time total: 234.344us 2025-07-17T09:05:52.5120995Z CUDA time total: 0.000us 2025-07-17T09:05:52.5121116Z 2025-07-17T09:05:52.5121188Z 2025-07-17T09:05:52.5121427Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.5121640Z 2025-07-17T09:05:52.5676944Z msg = Cannot scrape callname=DeviceMesh.__getitem__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py line=685. 2025-07-17T09:05:52.5677601Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-07-17T09:05:52.5677830Z 2025-07-17T09:05:52.5678037Z Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh. 2025-07-17T09:05:52.5678439Z The submesh created consists of the dimensions and the communicators indicated by 2025-07-17T09:05:52.5678727Z ``mesh_dim_names`` 2025-07-17T09:05:52.5678826Z 2025-07-17T09:05:52.5678913Z Args: 2025-07-17T09:05:52.5679145Z mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the 2025-07-17T09:05:52.5679470Z mesh dimension of the DeviceMesh to create the submesh for. 2025-07-17T09:05:52.5679720Z Returns: 2025-07-17T09:05:52.5679879Z A :class:`DeviceMesh` object 2025-07-17T09:05:52.5680010Z 2025-07-17T09:05:52.5680182Z The following program runs on each process/rank in an SPMD manner in a world size of 8. 2025-07-17T09:05:52.5680489Z In the first example: 2025-07-17T09:05:52.5680754Z Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]). 2025-07-17T09:05:52.5681130Z Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of DeviceMesh:([4, 5, 6, 7]). 2025-07-17T09:05:52.5681483Z Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of DeviceMesh:([0, 4]). 2025-07-17T09:05:52.5681821Z Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of DeviceMesh:([1, 5]). 2025-07-17T09:05:52.5682159Z Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of DeviceMesh:([2, 6]). 2025-07-17T09:05:52.5682476Z Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of DeviceMesh:([3, 7]). 2025-07-17T09:05:52.5682661Z 2025-07-17T09:05:52.5682743Z In the second example: 2025-07-17T09:05:52.5683012Z Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]). 2025-07-17T09:05:52.5683379Z Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]). 2025-07-17T09:05:52.5684247Z Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]). 2025-07-17T09:05:52.5684774Z Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]). 2025-07-17T09:05:52.5685002Z 2025-07-17T09:05:52.5685077Z Example:: 2025-07-17T09:05:52.5685174Z 2025-07-17T09:05:52.5685248Z >>> # xdoctest: +SKIP("no rank") 2025-07-17T09:05:52.5685679Z >>> from torch.distributed.device_mesh import DeviceMesh 2025-07-17T09:05:52.5685916Z >>> 2025-07-17T09:05:52.5686131Z >>> # Initialize a 2D device mesh as (2, 4) to represent the topology 2025-07-17T09:05:52.5686419Z >>> # of cross-host(dim 0), and within-host (dim 1). 2025-07-17T09:05:52.5686731Z >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2025-07-17T09:05:52.5687029Z >>> tp_mesh = mesh_2d["tp"] 2025-07-17T09:05:52.5687227Z >>> dp_mesh = mesh_2d["dp"] 2025-07-17T09:05:52.5687400Z >>> 2025-07-17T09:05:52.5687563Z >>> # Initialize a 3D mesh. 2025-07-17T09:05:52.5687863Z >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp")) 2025-07-17T09:05:52.5688270Z >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh. 2025-07-17T09:05:52.5688599Z >>> dp_cp_mesh = mesh_3d["dp", "cp"] 2025-07-17T09:05:52.5688806Z >>> cp_dp_mesh = mesh_3d["cp", "dp"] 2025-07-17T09:05:52.5688950Z 2025-07-17T09:05:52.5689335Z 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)) 2025-07-17T09:05:52.5689768Z 2025-07-17T09:05:52.5689918Z mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2025-07-17T09:05:52.5690208Z ^ 2025-07-17T09:05:52.5903332Z msg = Cannot scrape callname=batch_isend_irecv in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=2706. 2025-07-17T09:05:52.5904014Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.5904260Z 2025-07-17T09:05:52.5904422Z Send or Receive a batch of tensors asynchronously and return a list of requests. 2025-07-17T09:05:52.5904654Z 2025-07-17T09:05:52.5904819Z Process each of the operations in ``p2p_op_list`` and return the corresponding 2025-07-17T09:05:52.5905160Z requests. NCCL, Gloo, and UCC backend are currently supported. 2025-07-17T09:05:52.5905467Z 2025-07-17T09:05:52.5905529Z Args: 2025-07-17T09:05:52.5905767Z p2p_op_list: A list of point-to-point operations(type of each operator is 2025-07-17T09:05:52.5906112Z ``torch.distributed.P2POp``). The order of the isend/irecv in the list 2025-07-17T09:05:52.5906450Z matters and it needs to match with corresponding isend/irecv on the 2025-07-17T09:05:52.5906723Z remote end. 2025-07-17T09:05:52.5906820Z 2025-07-17T09:05:52.5906895Z Returns: 2025-07-17T09:05:52.5907137Z A list of distributed request objects returned by calling the corresponding 2025-07-17T09:05:52.5907427Z op in the op_list. 2025-07-17T09:05:52.5907532Z 2025-07-17T09:05:52.5907608Z Examples: 2025-07-17T09:05:52.5907771Z >>> # xdoctest: +SKIP("no rank") 2025-07-17T09:05:52.5908036Z >>> send_tensor = torch.arange(2, dtype=torch.float32) + 2 * rank 2025-07-17T09:05:52.5908316Z >>> recv_tensor = torch.randn(2, dtype=torch.float32) 2025-07-17T09:05:52.5908627Z >>> send_op = dist.P2POp(dist.isend, send_tensor, (rank + 1) % world_size) 2025-07-17T09:05:52.5908899Z >>> recv_op = dist.P2POp( 2025-07-17T09:05:52.5909146Z ... dist.irecv, recv_tensor, (rank - 1 + world_size) % world_size 2025-07-17T09:05:52.5909388Z ... ) 2025-07-17T09:05:52.5909567Z >>> reqs = batch_isend_irecv([send_op, recv_op]) 2025-07-17T09:05:52.5909803Z >>> for req in reqs: 2025-07-17T09:05:52.5910167Z >>> req.wait() 2025-07-17T09:05:52.5910411Z >>> recv_tensor 2025-07-17T09:05:52.5910578Z tensor([2, 3]) # Rank 0 2025-07-17T09:05:52.5910762Z tensor([0, 1]) # Rank 1 2025-07-17T09:05:52.5910868Z 2025-07-17T09:05:52.5911055Z .. note:: Note that when this API is used with the NCCL PG backend, users must set 2025-07-17T09:05:52.5911408Z the current GPU device with `torch.cuda.set_device`, otherwise it will 2025-07-17T09:05:52.5911848Z lead to unexpected hang issues. 2025-07-17T09:05:52.5911996Z 2025-07-17T09:05:52.5912124Z In addition, if this API is the first collective call in the ``group`` 2025-07-17T09:05:52.5912455Z passed to ``dist.P2POp``, all ranks of the ``group`` must participate in 2025-07-17T09:05:52.5912787Z this API call; otherwise, the behavior is undefined. If this API call is 2025-07-17T09:05:52.5913117Z not the first collective call in the ``group``, batched P2P operations 2025-07-17T09:05:52.5913436Z involving only a subset of ranks of the ``group`` are allowed. 2025-07-17T09:05:52.5913634Z 2025-07-17T09:05:52.5913787Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.5914008Z 2025-07-17T09:05:52.5914356Z msg = Cannot scrape callname=all_reduce in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=2838. 2025-07-17T09:05:52.5914902Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.5915124Z 2025-07-17T09:05:52.5915288Z Reduces the tensor data across all machines in a way that all get the final result. 2025-07-17T09:05:52.5915512Z 2025-07-17T09:05:52.5915643Z After the call ``tensor`` is going to be bitwise identical in all processes. 2025-07-17T09:05:52.5915840Z 2025-07-17T09:05:52.5915917Z Complex tensors are supported. 2025-07-17T09:05:52.5916044Z 2025-07-17T09:05:52.5916106Z Args: 2025-07-17T09:05:52.5916321Z tensor (Tensor): Input and output of the collective. The function 2025-07-17T09:05:52.5916597Z operates in-place. 2025-07-17T09:05:52.5916799Z op (optional): One of the values from 2025-07-17T09:05:52.5917025Z ``torch.distributed.ReduceOp`` 2025-07-17T09:05:52.5917290Z enum. Specifies an operation used for element-wise reductions. 2025-07-17T09:05:52.5917616Z group (ProcessGroup, optional): The process group to work on. If None, 2025-07-17T09:05:52.5917906Z the default process group will be used. 2025-07-17T09:05:52.5918173Z async_op (bool, optional): Whether this op should be an async op 2025-07-17T09:05:52.5918358Z 2025-07-17T09:05:52.5918420Z Returns: 2025-07-17T09:05:52.5918603Z Async work handle, if async_op is set to True. 2025-07-17T09:05:52.5918853Z None, if not async_op or if not part of the group 2025-07-17T09:05:52.5919016Z 2025-07-17T09:05:52.5919078Z Examples: 2025-07-17T09:05:52.5919248Z >>> # xdoctest: +SKIP("no rank") 2025-07-17T09:05:52.5919487Z >>> # All tensors below are of torch.int64 type. 2025-07-17T09:05:52.5919732Z >>> # We have 2 process groups, 2 ranks. 2025-07-17T09:05:52.5919956Z >>> device = torch.device(f"cuda:{rank}") 2025-07-17T09:05:52.5920239Z >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank 2025-07-17T09:05:52.5920509Z >>> tensor 2025-07-17T09:05:52.5920687Z tensor([1, 2], device='cuda:0') # Rank 0 2025-07-17T09:05:52.5920900Z tensor([3, 4], device='cuda:1') # Rank 1 2025-07-17T09:05:52.5921128Z >>> dist.all_reduce(tensor, op=ReduceOp.SUM) 2025-07-17T09:05:52.5921350Z >>> tensor 2025-07-17T09:05:52.5921531Z tensor([4, 6], device='cuda:0') # Rank 0 2025-07-17T09:05:52.5921740Z tensor([4, 6], device='cuda:1') # Rank 1 2025-07-17T09:05:52.5921878Z 2025-07-17T09:05:52.5921972Z >>> # All tensors below are of torch.cfloat type. 2025-07-17T09:05:52.5922198Z >>> # We have 2 process groups, 2 ranks. 2025-07-17T09:05:52.5922406Z >>> tensor = torch.tensor( 2025-07-17T09:05:52.5922628Z ... [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device 2025-07-17T09:05:52.5923000Z ... ) + 2 * rank * (1 + 1j) 2025-07-17T09:05:52.5923198Z >>> tensor 2025-07-17T09:05:52.5923374Z tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0 2025-07-17T09:05:52.5923603Z tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1 2025-07-17T09:05:52.5923823Z >>> dist.all_reduce(tensor, op=ReduceOp.SUM) 2025-07-17T09:05:52.5924020Z >>> tensor 2025-07-17T09:05:52.5924292Z tensor([4.+4.j, 6.+6.j], device='cuda:0') # Rank 0 2025-07-17T09:05:52.5924526Z tensor([4.+4.j, 6.+6.j], device='cuda:1') # Rank 1 2025-07-17T09:05:52.5924665Z 2025-07-17T09:05:52.5924668Z 2025-07-17T09:05:52.5924827Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.5925037Z 2025-07-17T09:05:52.5925399Z msg = Cannot scrape callname=gather_object in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=3198. 2025-07-17T09:05:52.5925937Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.5926159Z 2025-07-17T09:05:52.5926302Z Gathers picklable objects from the whole group in a single process. 2025-07-17T09:05:52.5926861Z 2025-07-17T09:05:52.5927102Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2025-07-17T09:05:52.5927468Z object must be picklable in order to be gathered. 2025-07-17T09:05:52.5927678Z 2025-07-17T09:05:52.5940290Z Args: 2025-07-17T09:05:52.5940523Z obj (Any): Input object. Must be picklable. 2025-07-17T09:05:52.5940847Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2025-07-17T09:05:52.5941190Z should be correctly sized as the size of the group for this 2025-07-17T09:05:52.5941520Z collective and will contain the output. Must be ``None`` on non-dst 2025-07-17T09:05:52.5941801Z ranks. (default is ``None``) 2025-07-17T09:05:52.5942134Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). 2025-07-17T09:05:52.5942523Z (If both ``dst`` and ``group_dst`` are None, default is global rank 0) 2025-07-17T09:05:52.5942863Z group: (ProcessGroup, optional): The process group to work on. If None, 2025-07-17T09:05:52.5943190Z the default process group will be used. Default is ``None``. 2025-07-17T09:05:52.5943581Z group_dst (int, optional): Destination rank on ``group``. Invalid to specify both ``dst`` and ``group_dst`` 2025-07-17T09:05:52.5943850Z 2025-07-17T09:05:52.5943917Z Returns: 2025-07-17T09:05:52.5944130Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2025-07-17T09:05:52.5944389Z output of the collective. 2025-07-17T09:05:52.5944524Z 2025-07-17T09:05:52.5944665Z .. note:: Note that this API differs slightly from the gather collective 2025-07-17T09:05:52.5944999Z since it does not provide an async_op handle and thus will be a blocking 2025-07-17T09:05:52.5945267Z call. 2025-07-17T09:05:52.5945454Z 2025-07-17T09:05:52.5945615Z .. note:: For NCCL-based processed groups, internal tensor representations 2025-07-17T09:05:52.5945952Z of objects must be moved to the GPU device before communication takes 2025-07-17T09:05:52.5946243Z place. In this case, the device used is given by 2025-07-17T09:05:52.5946542Z ``torch.cuda.current_device()`` and it is the user's responsibility to 2025-07-17T09:05:52.5946885Z ensure that this is set so that each rank has an individual GPU, via 2025-07-17T09:05:52.5947156Z ``torch.cuda.set_device()``. 2025-07-17T09:05:52.5947281Z 2025-07-17T09:05:52.5947355Z .. warning:: 2025-07-17T09:05:52.5947590Z Object collectives have a number of serious performance and scalability 2025-07-17T09:05:52.5947920Z limitations. See :ref:`object_collectives` for details. 2025-07-17T09:05:52.5948102Z 2025-07-17T09:05:52.5948164Z .. warning:: 2025-07-17T09:05:52.5948389Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2025-07-17T09:05:52.5948838Z known to be insecure. It is possible to construct malicious pickle data 2025-07-17T09:05:52.5949249Z which will execute arbitrary code during unpickling. Only call this 2025-07-17T09:05:52.5949514Z function with data you trust. 2025-07-17T09:05:52.5949647Z 2025-07-17T09:05:52.5949710Z .. warning:: 2025-07-17T09:05:52.5949937Z Calling :func:`gather_object` with GPU tensors is not well supported 2025-07-17T09:05:52.5950406Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2025-07-17T09:05:52.5950727Z pickled. Please consider using :func:`gather` instead. 2025-07-17T09:05:52.5950896Z 2025-07-17T09:05:52.5950975Z Example:: 2025-07-17T09:05:52.5951165Z >>> # xdoctest: +SKIP("need process group init") 2025-07-17T09:05:52.5951442Z >>> # Note: Process group initialization omitted on each rank. 2025-07-17T09:05:52.5951716Z >>> import torch.distributed as dist 2025-07-17T09:05:52.5951936Z >>> # Assumes world_size of 3. 2025-07-17T09:05:52.5952201Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2025-07-17T09:05:52.5952471Z >>> output = [None for _ in gather_objects] 2025-07-17T09:05:52.5952690Z >>> dist.gather_object( 2025-07-17T09:05:52.5952889Z ... gather_objects[dist.get_rank()], 2025-07-17T09:05:52.5953125Z ... output if dist.get_rank() == 0 else None, 2025-07-17T09:05:52.5953344Z ... dst=0 2025-07-17T09:05:52.5953505Z ... ) 2025-07-17T09:05:52.5953673Z >>> # On rank 0 2025-07-17T09:05:52.5953837Z >>> output 2025-07-17T09:05:52.5953993Z ['foo', 12, {1: 2}] 2025-07-17T09:05:52.5954107Z 2025-07-17T09:05:52.5954266Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.5954492Z 2025-07-17T09:05:52.5954860Z msg = Cannot scrape callname=all_gather in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=3794. 2025-07-17T09:05:52.5955404Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.5955627Z 2025-07-17T09:05:52.5955727Z Gathers tensors from the whole group in a list. 2025-07-17T09:05:52.5955887Z 2025-07-17T09:05:52.5955980Z Complex and uneven sized tensors are supported. 2025-07-17T09:05:52.5956139Z 2025-07-17T09:05:52.5956199Z Args: 2025-07-17T09:05:52.5956407Z tensor_list (list[Tensor]): Output list. It should contain 2025-07-17T09:05:52.5956735Z correctly-sized tensors to be used for output of the collective. 2025-07-17T09:05:52.5957032Z Uneven sized tensors are supported. 2025-07-17T09:05:52.5957303Z tensor (Tensor): Tensor to be broadcast from current process. 2025-07-17T09:05:52.5957631Z group (ProcessGroup, optional): The process group to work on. If None, 2025-07-17T09:05:52.5957930Z the default process group will be used. 2025-07-17T09:05:52.5958207Z async_op (bool, optional): Whether this op should be an async op 2025-07-17T09:05:52.5958389Z 2025-07-17T09:05:52.5958462Z Returns: 2025-07-17T09:05:52.5958649Z Async work handle, if async_op is set to True. 2025-07-17T09:05:52.5958905Z None, if not async_op or if not part of the group 2025-07-17T09:05:52.5959056Z 2025-07-17T09:05:52.5959130Z Examples: 2025-07-17T09:05:52.5959305Z >>> # xdoctest: +SKIP("need process group init") 2025-07-17T09:05:52.5959543Z >>> # All tensors below are of torch.int64 dtype. 2025-07-17T09:05:52.5959770Z >>> # We have 2 process groups, 2 ranks. 2025-07-17T09:05:52.5959999Z >>> device = torch.device(f"cuda:{rank}") 2025-07-17T09:05:52.5960216Z >>> tensor_list = [ 2025-07-17T09:05:52.5960452Z ... torch.zeros(2, dtype=torch.int64, device=device) for _ in range(2) 2025-07-17T09:05:52.5960711Z ... ] 2025-07-17T09:05:52.5960858Z >>> tensor_list 2025-07-17T09:05:52.5961081Z [tensor([0, 0], device='cuda:0'), tensor([0, 0], device='cuda:0')] # Rank 0 2025-07-17T09:05:52.5961391Z [tensor([0, 0], device='cuda:1'), tensor([0, 0], device='cuda:1')] # Rank 1 2025-07-17T09:05:52.5961794Z >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank 2025-07-17T09:05:52.5962116Z >>> tensor 2025-07-17T09:05:52.5962279Z tensor([1, 2], device='cuda:0') # Rank 0 2025-07-17T09:05:52.5962493Z tensor([3, 4], device='cuda:1') # Rank 1 2025-07-17T09:05:52.5962707Z >>> dist.all_gather(tensor_list, tensor) 2025-07-17T09:05:52.5962904Z >>> tensor_list 2025-07-17T09:05:52.5963227Z [tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0')] # Rank 0 2025-07-17T09:05:52.5963528Z [tensor([1, 2], device='cuda:1'), tensor([3, 4], device='cuda:1')] # Rank 1 2025-07-17T09:05:52.5963714Z 2025-07-17T09:05:52.5963812Z >>> # All tensors below are of torch.cfloat dtype. 2025-07-17T09:05:52.5964048Z >>> # We have 2 process groups, 2 ranks. 2025-07-17T09:05:52.5964253Z >>> tensor_list = [ 2025-07-17T09:05:52.5964491Z ... torch.zeros(2, dtype=torch.cfloat, device=device) for _ in range(2) 2025-07-17T09:05:52.5964742Z ... ] 2025-07-17T09:05:52.5964891Z >>> tensor_list 2025-07-17T09:05:52.5965137Z [tensor([0.+0.j, 0.+0.j], device='cuda:0'), tensor([0.+0.j, 0.+0.j], device='cuda:0')] # Rank 0 2025-07-17T09:05:52.5965487Z [tensor([0.+0.j, 0.+0.j], device='cuda:1'), tensor([0.+0.j, 0.+0.j], device='cuda:1')] # Rank 1 2025-07-17T09:05:52.5965770Z >>> tensor = torch.tensor( 2025-07-17T09:05:52.5965997Z ... [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device 2025-07-17T09:05:52.5966238Z ... ) + 2 * rank * (1 + 1j) 2025-07-17T09:05:52.5966415Z >>> tensor 2025-07-17T09:05:52.5966608Z tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0 2025-07-17T09:05:52.5966849Z tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1 2025-07-17T09:05:52.5967071Z >>> dist.all_gather(tensor_list, tensor) 2025-07-17T09:05:52.5967271Z >>> tensor_list 2025-07-17T09:05:52.5967497Z [tensor([1.+1.j, 2.+2.j], device='cuda:0'), tensor([3.+3.j, 4.+4.j], device='cuda:0')] # Rank 0 2025-07-17T09:05:52.5967837Z [tensor([1.+1.j, 2.+2.j], device='cuda:1'), tensor([3.+3.j, 4.+4.j], device='cuda:1')] # Rank 1 2025-07-17T09:05:52.5968040Z 2025-07-17T09:05:52.5968042Z 2025-07-17T09:05:52.5968204Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.5968416Z 2025-07-17T09:05:52.5993554Z msg = Cannot scrape callname=all_to_all_single in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=4500. 2025-07-17T09:05:52.5994225Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.5994477Z 2025-07-17T09:05:52.5994639Z Split input tensor and then scatter the split list to all processes in a group. 2025-07-17T09:05:52.5994859Z 2025-07-17T09:05:52.5995025Z Later the received tensors are concatenated from all the processes in the group 2025-07-17T09:05:52.5995326Z and returned as a single output tensor. 2025-07-17T09:05:52.5995464Z 2025-07-17T09:05:52.5995558Z Complex tensors are supported. 2025-07-17T09:05:52.5995687Z 2025-07-17T09:05:52.5995761Z Args: 2025-07-17T09:05:52.5995968Z output (Tensor): Gathered concatenated output tensor. 2025-07-17T09:05:52.5996234Z input (Tensor): Input tensor to scatter. 2025-07-17T09:05:52.5996528Z output_split_sizes: (list[Int], optional): Output split sizes for dim 0 2025-07-17T09:05:52.5996858Z if specified None or empty, dim 0 of ``output`` tensor must divide 2025-07-17T09:05:52.5997138Z equally by ``world_size``. 2025-07-17T09:05:52.5997406Z input_split_sizes: (list[Int], optional): Input split sizes for dim 0 2025-07-17T09:05:52.5997730Z if specified None or empty, dim 0 of ``input`` tensor must divide 2025-07-17T09:05:52.5997987Z equally by ``world_size``. 2025-07-17T09:05:52.5998246Z group (ProcessGroup, optional): The process group to work on. If None, 2025-07-17T09:05:52.5998541Z the default process group will be used. 2025-07-17T09:05:52.5998820Z async_op (bool, optional): Whether this op should be an async op. 2025-07-17T09:05:52.5999148Z 2025-07-17T09:05:52.5999301Z Returns: 2025-07-17T09:05:52.5999487Z Async work handle, if async_op is set to True. 2025-07-17T09:05:52.5999745Z None, if not async_op or if not part of the group. 2025-07-17T09:05:52.5999905Z 2025-07-17T09:05:52.5999986Z .. warning:: 2025-07-17T09:05:52.6000193Z `all_to_all_single` is experimental and subject to change. 2025-07-17T09:05:52.6000363Z 2025-07-17T09:05:52.6000438Z Examples: 2025-07-17T09:05:52.6000736Z >>> # xdoctest: +SKIP("Undefined rank") 2025-07-17T09:05:52.6000962Z >>> input = torch.arange(4) + rank * 4 2025-07-17T09:05:52.6001154Z >>> input 2025-07-17T09:05:52.6001329Z tensor([0, 1, 2, 3]) # Rank 0 2025-07-17T09:05:52.6001532Z tensor([4, 5, 6, 7]) # Rank 1 2025-07-17T09:05:52.6001724Z tensor([8, 9, 10, 11]) # Rank 2 2025-07-17T09:05:52.6001983Z tensor([12, 13, 14, 15]) # Rank 3 2025-07-17T09:05:52.6002203Z >>> output = torch.empty([4], dtype=torch.int64) 2025-07-17T09:05:52.6002443Z >>> dist.all_to_all_single(output, input) 2025-07-17T09:05:52.6002653Z >>> output 2025-07-17T09:05:52.6002815Z tensor([0, 4, 8, 12]) # Rank 0 2025-07-17T09:05:52.6003010Z tensor([1, 5, 9, 13]) # Rank 1 2025-07-17T09:05:52.6003185Z tensor([2, 6, 10, 14]) # Rank 2 2025-07-17T09:05:52.6003369Z tensor([3, 7, 11, 15]) # Rank 3 2025-07-17T09:05:52.6003488Z 2025-07-17T09:05:52.6003600Z >>> # Essentially, it is similar to following operation: 2025-07-17T09:05:52.6003858Z >>> scatter_list = list(input.chunk(world_size)) 2025-07-17T09:05:52.6004095Z >>> gather_list = list(output.chunk(world_size)) 2025-07-17T09:05:52.6004313Z >>> for i in range(world_size): 2025-07-17T09:05:52.6004610Z >>> dist.scatter(gather_list[i], scatter_list if i == rank else [], src = i) 2025-07-17T09:05:52.6004824Z 2025-07-17T09:05:52.6004907Z >>> # Another example with uneven split 2025-07-17T09:05:52.6005112Z >>> input 2025-07-17T09:05:52.6005300Z tensor([0, 1, 2, 3, 4, 5]) # Rank 0 2025-07-17T09:05:52.6005571Z tensor([10, 11, 12, 13, 14, 15, 16, 17, 18]) # Rank 1 2025-07-17T09:05:52.6005830Z tensor([20, 21, 22, 23, 24]) # Rank 2 2025-07-17T09:05:52.6006086Z tensor([30, 31, 32, 33, 34, 35, 36]) # Rank 3 2025-07-17T09:05:52.6006319Z >>> input_splits 2025-07-17T09:05:52.6006491Z [2, 2, 1, 1] # Rank 0 2025-07-17T09:05:52.6006729Z [3, 2, 2, 2] # Rank 1 2025-07-17T09:05:52.6006943Z [2, 1, 1, 1] # Rank 2 2025-07-17T09:05:52.6007160Z [2, 2, 2, 1] # Rank 3 2025-07-17T09:05:52.6007364Z >>> output_splits 2025-07-17T09:05:52.6007536Z [2, 3, 2, 2] # Rank 0 2025-07-17T09:05:52.6007749Z [2, 2, 1, 2] # Rank 1 2025-07-17T09:05:52.6007975Z [1, 2, 1, 2] # Rank 2 2025-07-17T09:05:52.6008196Z [1, 2, 1, 1] # Rank 3 2025-07-17T09:05:52.6008402Z >>> output = ... 2025-07-17T09:05:52.6008629Z >>> dist.all_to_all_single(output, input, output_splits, input_splits) 2025-07-17T09:05:52.6008880Z >>> output 2025-07-17T09:05:52.6009061Z tensor([ 0, 1, 10, 11, 12, 20, 21, 30, 31]) # Rank 0 2025-07-17T09:05:52.6009300Z tensor([ 2, 3, 13, 14, 22, 32, 33]) # Rank 1 2025-07-17T09:05:52.6009557Z tensor([ 4, 15, 16, 23, 34, 35]) # Rank 2 2025-07-17T09:05:52.6009809Z tensor([ 5, 17, 18, 24, 36]) # Rank 3 2025-07-17T09:05:52.6009960Z 2025-07-17T09:05:52.6009963Z 2025-07-17T09:05:52.6010151Z >>> # Another example with tensors of torch.cfloat type. 2025-07-17T09:05:52.6010443Z >>> input = torch.tensor( 2025-07-17T09:05:52.6010660Z ... [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat 2025-07-17T09:05:52.6010882Z ... ) + 4 * rank * (1 + 1j) 2025-07-17T09:05:52.6011049Z >>> input 2025-07-17T09:05:52.6011242Z tensor([1+1j, 2+2j, 3+3j, 4+4j]) # Rank 0 2025-07-17T09:05:52.6011646Z tensor([5+5j, 6+6j, 7+7j, 8+8j]) # Rank 1 2025-07-17T09:05:52.6011975Z tensor([9+9j, 10+10j, 11+11j, 12+12j]) # Rank 2 2025-07-17T09:05:52.6012257Z tensor([13+13j, 14+14j, 15+15j, 16+16j]) # Rank 3 2025-07-17T09:05:52.6012521Z >>> output = torch.empty([4], dtype=torch.int64) 2025-07-17T09:05:52.6012762Z >>> dist.all_to_all_single(output, input) 2025-07-17T09:05:52.6012958Z >>> output 2025-07-17T09:05:52.6013152Z tensor([1+1j, 5+5j, 9+9j, 13+13j]) # Rank 0 2025-07-17T09:05:52.6013424Z tensor([2+2j, 6+6j, 10+10j, 14+14j]) # Rank 1 2025-07-17T09:05:52.6013698Z tensor([3+3j, 7+7j, 11+11j, 15+15j]) # Rank 2 2025-07-17T09:05:52.6013965Z tensor([4+4j, 8+8j, 12+12j, 16+16j]) # Rank 3 2025-07-17T09:05:52.6014123Z 2025-07-17T09:05:52.6014293Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.6014493Z 2025-07-17T09:05:52.6014840Z msg = Cannot scrape callname=all_to_all in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=4642. 2025-07-17T09:05:52.6015378Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.6015595Z 2025-07-17T09:05:52.6015818Z Scatters list of input tensors to all processes in a group and return gathered list of tensors in output list. 2025-07-17T09:05:52.6016091Z 2025-07-17T09:05:52.6016181Z Complex tensors are supported. 2025-07-17T09:05:52.6016303Z 2025-07-17T09:05:52.6016373Z Args: 2025-07-17T09:05:52.6016595Z output_tensor_list (list[Tensor]): List of tensors to be gathered one 2025-07-17T09:05:52.6016853Z per rank. 2025-07-17T09:05:52.6017087Z input_tensor_list (list[Tensor]): List of tensors to scatter one per rank. 2025-07-17T09:05:52.6017415Z group (ProcessGroup, optional): The process group to work on. If None, 2025-07-17T09:05:52.6017705Z the default process group will be used. 2025-07-17T09:05:52.6017982Z async_op (bool, optional): Whether this op should be an async op. 2025-07-17T09:05:52.6018171Z 2025-07-17T09:05:52.6018237Z Returns: 2025-07-17T09:05:52.6018419Z Async work handle, if async_op is set to True. 2025-07-17T09:05:52.6018674Z None, if not async_op or if not part of the group. 2025-07-17T09:05:52.6018834Z 2025-07-17T09:05:52.6018904Z .. warning:: 2025-07-17T09:05:52.6019099Z `all_to_all` is experimental and subject to change. 2025-07-17T09:05:52.6019254Z 2025-07-17T09:05:52.6019326Z Examples: 2025-07-17T09:05:52.6019494Z >>> # xdoctest: +SKIP("Undefined rank") 2025-07-17T09:05:52.6019706Z >>> input = torch.arange(4) + rank * 4 2025-07-17T09:05:52.6019919Z >>> input = list(input.chunk(4)) 2025-07-17T09:05:52.6020117Z >>> input 2025-07-17T09:05:52.6020307Z [tensor([0]), tensor([1]), tensor([2]), tensor([3])] # Rank 0 2025-07-17T09:05:52.6020579Z [tensor([4]), tensor([5]), tensor([6]), tensor([7])] # Rank 1 2025-07-17T09:05:52.6020844Z [tensor([8]), tensor([9]), tensor([10]), tensor([11])] # Rank 2 2025-07-17T09:05:52.6021110Z [tensor([12]), tensor([13]), tensor([14]), tensor([15])] # Rank 3 2025-07-17T09:05:52.6021391Z >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4)) 2025-07-17T09:05:52.6021639Z >>> dist.all_to_all(output, input) 2025-07-17T09:05:52.6021841Z >>> output 2025-07-17T09:05:52.6022024Z [tensor([0]), tensor([4]), tensor([8]), tensor([12])] # Rank 0 2025-07-17T09:05:52.6022368Z [tensor([1]), tensor([5]), tensor([9]), tensor([13])] # Rank 1 2025-07-17T09:05:52.6022682Z [tensor([2]), tensor([6]), tensor([10]), tensor([14])] # Rank 2 2025-07-17T09:05:52.6022933Z [tensor([3]), tensor([7]), tensor([11]), tensor([15])] # Rank 3 2025-07-17T09:05:52.6023089Z 2025-07-17T09:05:52.6023200Z >>> # Essentially, it is similar to following operation: 2025-07-17T09:05:52.6023535Z >>> scatter_list = input 2025-07-17T09:05:52.6023709Z >>> gather_list = output 2025-07-17T09:05:52.6023896Z >>> for i in range(world_size): 2025-07-17T09:05:52.6024165Z >>> dist.scatter(gather_list[i], scatter_list if i == rank else [], src=i) 2025-07-17T09:05:52.6024365Z 2025-07-17T09:05:52.6024425Z >>> input 2025-07-17T09:05:52.6024612Z tensor([0, 1, 2, 3, 4, 5]) # Rank 0 2025-07-17T09:05:52.6024863Z tensor([10, 11, 12, 13, 14, 15, 16, 17, 18]) # Rank 1 2025-07-17T09:05:52.6025118Z tensor([20, 21, 22, 23, 24]) # Rank 2 2025-07-17T09:05:52.6025455Z tensor([30, 31, 32, 33, 34, 35, 36]) # Rank 3 2025-07-17T09:05:52.6025681Z >>> input_splits 2025-07-17T09:05:52.6025866Z [2, 2, 1, 1] # Rank 0 2025-07-17T09:05:52.6026087Z [3, 2, 2, 2] # Rank 1 2025-07-17T09:05:52.6026303Z [2, 1, 1, 1] # Rank 2 2025-07-17T09:05:52.6026517Z [2, 2, 2, 1] # Rank 3 2025-07-17T09:05:52.6026714Z >>> output_splits 2025-07-17T09:05:52.6026888Z [2, 3, 2, 2] # Rank 0 2025-07-17T09:05:52.6027102Z [2, 2, 1, 2] # Rank 1 2025-07-17T09:05:52.6027314Z [1, 2, 1, 2] # Rank 2 2025-07-17T09:05:52.6027525Z [1, 2, 1, 1] # Rank 3 2025-07-17T09:05:52.6027748Z >>> input = list(input.split(input_splits)) 2025-07-17T09:05:52.6027947Z >>> input 2025-07-17T09:05:52.6028160Z [tensor([0, 1]), tensor([2, 3]), tensor([4]), tensor([5])] # Rank 0 2025-07-17T09:05:52.6028467Z [tensor([10, 11, 12]), tensor([13, 14]), tensor([15, 16]), tensor([17, 18])] # Rank 1 2025-07-17T09:05:52.6028757Z [tensor([20, 21]), tensor([22]), tensor([23]), tensor([24])] # Rank 2 2025-07-17T09:05:52.6029055Z [tensor([30, 31]), tensor([32, 33]), tensor([34, 35]), tensor([36])] # Rank 3 2025-07-17T09:05:52.6029304Z >>> output = ... 2025-07-17T09:05:52.6029478Z >>> dist.all_to_all(output, input) 2025-07-17T09:05:52.6029671Z >>> output 2025-07-17T09:05:52.6029872Z [tensor([0, 1]), tensor([10, 11, 12]), tensor([20, 21]), tensor([30, 31])] # Rank 0 2025-07-17T09:05:52.6030166Z [tensor([2, 3]), tensor([13, 14]), tensor([22]), tensor([32, 33])] # Rank 1 2025-07-17T09:05:52.6030462Z [tensor([4]), tensor([15, 16]), tensor([23]), tensor([34, 35])] # Rank 2 2025-07-17T09:05:52.6030768Z [tensor([5]), tensor([17, 18]), tensor([24]), tensor([36])] # Rank 3 2025-07-17T09:05:52.6030951Z 2025-07-17T09:05:52.6031054Z >>> # Another example with tensors of torch.cfloat type. 2025-07-17T09:05:52.6031298Z >>> input = torch.tensor( 2025-07-17T09:05:52.6031509Z ... [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat 2025-07-17T09:05:52.6031737Z ... ) + 4 * rank * (1 + 1j) 2025-07-17T09:05:52.6031925Z >>> input = list(input.chunk(4)) 2025-07-17T09:05:52.6032115Z >>> input 2025-07-17T09:05:52.6032327Z [tensor([1+1j]), tensor([2+2j]), tensor([3+3j]), tensor([4+4j])] # Rank 0 2025-07-17T09:05:52.6032633Z [tensor([5+5j]), tensor([6+6j]), tensor([7+7j]), tensor([8+8j])] # Rank 1 2025-07-17T09:05:52.6033042Z [tensor([9+9j]), tensor([10+10j]), tensor([11+11j]), tensor([12+12j])] # Rank 2 2025-07-17T09:05:52.6033441Z [tensor([13+13j]), tensor([14+14j]), tensor([15+15j]), tensor([16+16j])] # Rank 3 2025-07-17T09:05:52.6033745Z >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4)) 2025-07-17T09:05:52.6033990Z >>> dist.all_to_all(output, input) 2025-07-17T09:05:52.6034170Z >>> output 2025-07-17T09:05:52.6034500Z [tensor([1+1j]), tensor([5+5j]), tensor([9+9j]), tensor([13+13j])] # Rank 0 2025-07-17T09:05:52.6034801Z [tensor([2+2j]), tensor([6+6j]), tensor([10+10j]), tensor([14+14j])] # Rank 1 2025-07-17T09:05:52.6035094Z [tensor([3+3j]), tensor([7+7j]), tensor([11+11j]), tensor([15+15j])] # Rank 2 2025-07-17T09:05:52.6035406Z [tensor([4+4j]), tensor([8+8j]), tensor([12+12j]), tensor([16+16j])] # Rank 3 2025-07-17T09:05:52.6035590Z 2025-07-17T09:05:52.6035593Z 2025-07-17T09:05:52.6035746Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.6035959Z 2025-07-17T09:05:52.6108530Z msg = Cannot scrape callname=__doc__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/launch.py line=2. 2025-07-17T09:05:52.6109119Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.6109362Z 2025-07-17T09:05:52.6109448Z Module ``torch.distributed.launch``. 2025-07-17T09:05:52.6109611Z 2025-07-17T09:05:52.6109772Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2025-07-17T09:05:52.6110094Z training processes on each of the training nodes. 2025-07-17T09:05:52.6110256Z 2025-07-17T09:05:52.6110328Z .. warning:: 2025-07-17T09:05:52.6110425Z 2025-07-17T09:05:52.6110580Z This module is going to be deprecated in favor of :ref:`torchrun `. 2025-07-17T09:05:52.6110797Z 2025-07-17T09:05:52.6110943Z The utility can be used for single-node distributed training, in which one or 2025-07-17T09:05:52.6111297Z more processes per node will be spawned. The utility can be used for either 2025-07-17T09:05:52.6111637Z CPU training or GPU training. If the utility is used for GPU training, 2025-07-17T09:05:52.6111977Z each distributed process will be operating on a single GPU. This can achieve 2025-07-17T09:05:52.6112324Z well-improved single-node training performance. It can also be used in 2025-07-17T09:05:52.6112685Z multi-node distributed training, by spawning up multiple processes on each node 2025-07-17T09:05:52.6113032Z for well-improved multi-node distributed training performance as well. 2025-07-17T09:05:52.6113363Z This will especially be beneficial for systems with multiple Infiniband 2025-07-17T09:05:52.6113717Z interfaces that have direct-GPU support, since all of them can be utilized for 2025-07-17T09:05:52.6114012Z aggregated communication bandwidth. 2025-07-17T09:05:52.6114143Z 2025-07-17T09:05:52.6114288Z In both cases of single-node distributed training or multi-node distributed 2025-07-17T09:05:52.6114629Z training, this utility will launch the given number of processes per node 2025-07-17T09:05:52.6114976Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2025-07-17T09:05:52.6115312Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2025-07-17T09:05:52.6115634Z and each process will be operating on a single GPU from *GPU 0 to 2025-07-17T09:05:52.6115893Z GPU (nproc_per_node - 1)*. 2025-07-17T09:05:52.6116015Z 2025-07-17T09:05:52.6116086Z **How to use this module:** 2025-07-17T09:05:52.6116203Z 2025-07-17T09:05:52.6116304Z 1. Single-Node multi-process distributed training 2025-07-17T09:05:52.6116464Z 2025-07-17T09:05:52.6116532Z :: 2025-07-17T09:05:52.6116619Z 2025-07-17T09:05:52.6116769Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-07-17T09:05:52.6117081Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2025-07-17T09:05:52.6117331Z arguments of your training script) 2025-07-17T09:05:52.6117755Z 2025-07-17T09:05:52.6117969Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2025-07-17T09:05:52.6118159Z 2025-07-17T09:05:52.6118162Z 2025-07-17T09:05:52.6118263Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2025-07-17T09:05:52.6118411Z 2025-07-17T09:05:52.6118476Z :: 2025-07-17T09:05:52.6118551Z 2025-07-17T09:05:52.6118827Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-07-17T09:05:52.6119137Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2025-07-17T09:05:52.6119430Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2025-07-17T09:05:52.6119716Z and all other arguments of your training script) 2025-07-17T09:05:52.6119872Z 2025-07-17T09:05:52.6119939Z Node 2: 2025-07-17T09:05:52.6120018Z 2025-07-17T09:05:52.6120088Z :: 2025-07-17T09:05:52.6120163Z 2025-07-17T09:05:52.6120306Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-07-17T09:05:52.6120603Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2025-07-17T09:05:52.6120875Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2025-07-17T09:05:52.6121145Z and all other arguments of your training script) 2025-07-17T09:05:52.6121302Z 2025-07-17T09:05:52.6121408Z 3. To look up what optional arguments this module offers: 2025-07-17T09:05:52.6121572Z 2025-07-17T09:05:52.6121635Z :: 2025-07-17T09:05:52.6121711Z 2025-07-17T09:05:52.6121800Z python -m torch.distributed.launch --help 2025-07-17T09:05:52.6121945Z 2025-07-17T09:05:52.6121948Z 2025-07-17T09:05:52.6122017Z **Important Notices:** 2025-07-17T09:05:52.6122126Z 2025-07-17T09:05:52.6122241Z 1. This utility and multi-process distributed (single-node or 2025-07-17T09:05:52.6122562Z multi-node) GPU training currently only achieves the best performance using 2025-07-17T09:05:52.6122910Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2025-07-17T09:05:52.6123188Z use for GPU training. 2025-07-17T09:05:52.6123290Z 2025-07-17T09:05:52.6123435Z 2. In your training program, you must parse the command-line argument: 2025-07-17T09:05:52.6123755Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2025-07-17T09:05:52.6124091Z If your training program uses GPUs, you should ensure that your code only 2025-07-17T09:05:52.6124406Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2025-07-17T09:05:52.6124580Z 2025-07-17T09:05:52.6124655Z Parsing the local_rank argument 2025-07-17T09:05:52.6124767Z 2025-07-17T09:05:52.6124830Z :: 2025-07-17T09:05:52.6124900Z 2025-07-17T09:05:52.6124967Z >>> # xdoctest: +SKIP 2025-07-17T09:05:52.6125157Z >>> import argparse 2025-07-17T09:05:52.6125357Z >>> parser = argparse.ArgumentParser() 2025-07-17T09:05:52.6125648Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2025-07-17T09:05:52.6125899Z >>> args = parser.parse_args() 2025-07-17T09:05:52.6126030Z 2025-07-17T09:05:52.6126113Z Set your device to local rank using either 2025-07-17T09:05:52.6126254Z 2025-07-17T09:05:52.6126313Z :: 2025-07-17T09:05:52.6126386Z 2025-07-17T09:05:52.6126523Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2025-07-17T09:05:52.6126703Z 2025-07-17T09:05:52.6126773Z or 2025-07-17T09:05:52.6126842Z 2025-07-17T09:05:52.6126913Z :: 2025-07-17T09:05:52.6126982Z 2025-07-17T09:05:52.6127087Z >>> with torch.cuda.device(args.local_rank): 2025-07-17T09:05:52.6127307Z >>> # your code to run 2025-07-17T09:05:52.6127482Z >>> ... 2025-07-17T09:05:52.6127569Z 2025-07-17T09:05:52.6127642Z .. versionchanged:: 2.0.0 2025-07-17T09:05:52.6127758Z 2025-07-17T09:05:52.6127906Z The launcher will passes the ``--local-rank=`` argument to your script. 2025-07-17T09:05:52.6128058Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2025-07-17T09:05:52.6128155Z previously used underscored ``--local_rank``. 2025-07-17T09:05:52.6128316Z 2025-07-17T09:05:52.6128474Z For backward compatibility, it may be necessary for users to handle both 2025-07-17T09:05:52.6128636Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2025-07-17T09:05:52.6128775Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2025-07-17T09:05:52.6129031Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2025-07-17T09:05:52.6129206Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2025-07-17T09:05:52.6129309Z including ``"--local-rank"`` should be sufficient. 2025-07-17T09:05:52.6129313Z 2025-07-17T09:05:52.6129452Z 3. In your training program, you are supposed to call the following function 2025-07-17T09:05:52.6129601Z at the beginning to start the distributed backend. It is strongly recommended 2025-07-17T09:05:52.6129760Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2025-07-17T09:05:52.6129897Z but ``env://`` is the one that is officially supported by this module. 2025-07-17T09:05:52.6129902Z 2025-07-17T09:05:52.6129962Z :: 2025-07-17T09:05:52.6129964Z 2025-07-17T09:05:52.6130103Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2025-07-17T09:05:52.6130208Z >>> init_method='env://') 2025-07-17T09:05:52.6130210Z 2025-07-17T09:05:52.6130369Z 4. In your training program, you can either use regular distributed functions 2025-07-17T09:05:52.6130522Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2025-07-17T09:05:52.6130658Z training program uses GPUs for training and you would like to use 2025-07-17T09:05:52.6130777Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2025-07-17T09:05:52.6130857Z here is how to configure it. 2025-07-17T09:05:52.6130861Z 2025-07-17T09:05:52.6130920Z :: 2025-07-17T09:05:52.6130923Z 2025-07-17T09:05:52.6131054Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2025-07-17T09:05:52.6131157Z >>> device_ids=[args.local_rank], 2025-07-17T09:05:52.6131249Z >>> output_device=args.local_rank) 2025-07-17T09:05:52.6131257Z 2025-07-17T09:05:52.6131403Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2025-07-17T09:05:52.6131555Z that your code will be operating on. This is generally the local rank of the 2025-07-17T09:05:52.6131698Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2025-07-17T09:05:52.6131834Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2025-07-17T09:05:52.6131910Z utility 2025-07-17T09:05:52.6131913Z 2025-07-17T09:05:52.6132058Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2025-07-17T09:05:52.6132198Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2025-07-17T09:05:52.6132329Z ``--use-env=True``. You must adjust the subprocess example above to replace 2025-07-17T09:05:52.6132463Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2025-07-17T09:05:52.6132572Z will not pass ``--local-rank`` when you specify this flag. 2025-07-17T09:05:52.6132575Z 2025-07-17T09:05:52.6132651Z .. warning:: 2025-07-17T09:05:52.6132654Z 2025-07-17T09:05:52.6132783Z ``local_rank`` is NOT globally unique: it is only unique per process 2025-07-17T09:05:52.6132914Z on a machine. Thus, don't use it to decide if you should, e.g., 2025-07-17T09:05:52.6132995Z write to a networked filesystem. See 2025-07-17T09:05:52.6133144Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2025-07-17T09:05:52.6133250Z how things can go wrong if you don't do this correctly. 2025-07-17T09:05:52.6133253Z 2025-07-17T09:05:52.6133255Z 2025-07-17T09:05:52.6133271Z 2025-07-17T09:05:52.6133273Z 2025-07-17T09:05:52.6133421Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.6133537Z 2025-07-17T09:05:52.6540873Z msg = Cannot scrape callname=ZeroRedundancyOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py line=284. 2025-07-17T09:05:52.6541082Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.6541138Z 2025-07-17T09:05:52.6541897Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2025-07-17T09:05:52.6541902Z 2025-07-17T09:05:52.6542083Z The sharing is done as described by `ZeRO `_. 2025-07-17T09:05:52.6542086Z 2025-07-17T09:05:52.6542191Z The local optimizer instance in each rank is only 2025-07-17T09:05:52.6542353Z responsible for updating approximately ``1 / world_size`` parameters and 2025-07-17T09:05:52.6542485Z hence only needs to keep ``1 / world_size`` optimizer states. After 2025-07-17T09:05:52.6542643Z parameters are updated locally, each rank will broadcast its parameters to 2025-07-17T09:05:52.6542774Z all other peers to keep all model replicas in the same state. 2025-07-17T09:05:52.6542902Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2025-07-17T09:05:52.6543076Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2025-07-17T09:05:52.6543148Z memory consumption. 2025-07-17T09:05:52.6543152Z 2025-07-17T09:05:52.6543333Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2025-07-17T09:05:52.6543472Z of parameters at each rank. Each parameter belongs to a single rank and is 2025-07-17T09:05:52.6543624Z not divided among ranks. The partition is arbitrary and might not match the 2025-07-17T09:05:52.6543715Z the parameter registration or usage order. 2025-07-17T09:05:52.6543719Z 2025-07-17T09:05:52.6543789Z Arguments: 2025-07-17T09:05:52.6543916Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2025-07-17T09:05:52.6544047Z or :class:`dict` s giving all parameters, which will be sharded 2025-07-17T09:05:52.6544119Z across ranks. 2025-07-17T09:05:52.6544123Z 2025-07-17T09:05:52.6544195Z Keyword Args: 2025-07-17T09:05:52.6544331Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2025-07-17T09:05:52.6544405Z optimizer. 2025-07-17T09:05:52.6544533Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2025-07-17T09:05:52.6544669Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2025-07-17T09:05:52.6544771Z :meth:`torch.distributed.init_process_group`). 2025-07-17T09:05:52.6544918Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2025-07-17T09:05:52.6545048Z packed into buckets to speed up communication, and ``param.data`` 2025-07-17T09:05:52.6545185Z fields point to bucket views at different offsets; if ``False``, 2025-07-17T09:05:52.6545395Z each individual parameter is communicated separately, and each 2025-07-17T09:05:52.6545513Z ``params.data`` stays intact (default: ``False``). 2025-07-17T09:05:52.6545636Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2025-07-17T09:05:52.6545769Z overlapped with :class:`DistributedDataParallel` 's gradient 2025-07-17T09:05:52.6545899Z synchronization; this requires (1) either a functional optimizer 2025-07-17T09:05:52.6546022Z for the ``optimizer_class`` argument or one with a functional 2025-07-17T09:05:52.6546134Z equivalent and (2) registering a DDP communication hook 2025-07-17T09:05:52.6546266Z constructed from one of the functions in ``ddp_zero_hook.py``; 2025-07-17T09:05:52.6546374Z parameters are packed into buckets matching those in 2025-07-17T09:05:52.6546482Z :class:`DistributedDataParallel`, meaning that the 2025-07-17T09:05:52.6546576Z ``parameters_as_bucket_view`` argument is ignored. 2025-07-17T09:05:52.6546695Z If ``False``, :meth:`step` runs disjointly after the backward pass 2025-07-17T09:05:52.6546968Z (per normal). 2025-07-17T09:05:52.6547041Z (default: ``False``) 2025-07-17T09:05:52.6547181Z **defaults: any trailing arguments, which are forwarded to the local 2025-07-17T09:05:52.6547247Z optimizer. 2025-07-17T09:05:52.6547250Z 2025-07-17T09:05:52.6547334Z Example:: 2025-07-17T09:05:52.6547337Z 2025-07-17T09:05:52.6547533Z >>> # xdoctest: +SKIP 2025-07-17T09:05:52.6547618Z >>> import torch.nn as nn 2025-07-17T09:05:52.6547750Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2025-07-17T09:05:52.6547886Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2025-07-17T09:05:52.6548032Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2025-07-17T09:05:52.6548126Z >>> ddp = DDP(model, device_ids=[rank]) 2025-07-17T09:05:52.6548210Z >>> opt = ZeroRedundancyOptimizer( 2025-07-17T09:05:52.6548292Z >>> ddp.parameters(), 2025-07-17T09:05:52.6548378Z >>> optimizer_class=torch.optim.Adam, 2025-07-17T09:05:52.6548450Z >>> lr=0.01 2025-07-17T09:05:52.6548510Z >>> ) 2025-07-17T09:05:52.6548595Z >>> ddp(inputs).sum().backward() 2025-07-17T09:05:52.6548658Z >>> opt.step() 2025-07-17T09:05:52.6548662Z 2025-07-17T09:05:52.6548732Z .. warning:: 2025-07-17T09:05:52.6548861Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2025-07-17T09:05:52.6548968Z passed-in parameters are the same dense type. 2025-07-17T09:05:52.6548980Z 2025-07-17T09:05:52.6549043Z .. warning:: 2025-07-17T09:05:52.6549175Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2025-07-17T09:05:52.6549317Z the way that overlapping :class:`DistributedDataParallel` with 2025-07-17T09:05:52.6549462Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2025-07-17T09:05:52.6549601Z two or three training iterations do not perform parameter updates in 2025-07-17T09:05:52.6549729Z the optimizer step, depending on if ``static_graph=False`` or 2025-07-17T09:05:52.6549855Z ``static_graph=True``, respectively. This is because it needs 2025-07-17T09:05:52.6549973Z information about the gradient bucketing strategy used by 2025-07-17T09:05:52.6550119Z :class:`DistributedDataParallel`, which is not finalized until the 2025-07-17T09:05:52.6550249Z second forward pass if ``static_graph=False`` or until the third 2025-07-17T09:05:52.6550384Z forward pass if ``static_graph=True``. To adjust for this, one option 2025-07-17T09:05:52.6550460Z is to prepend dummy inputs. 2025-07-17T09:05:52.6550463Z 2025-07-17T09:05:52.6550629Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2025-07-17T09:05:52.6550631Z 2025-07-17T09:05:52.6550785Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.6550788Z 2025-07-17T09:05:52.6577851Z msg = Cannot scrape callname=PostLocalSGDOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/post_localSGD_optimizer.py line=9. 2025-07-17T09:05:52.6578095Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.6578100Z 2025-07-17T09:05:52.6578348Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2025-07-17T09:05:52.6578470Z This optimizer runs local optimizer at every step. 2025-07-17T09:05:52.6578680Z After the warm-up stage, it averages parameters periodically after the local optimizer is applied. 2025-07-17T09:05:52.6578684Z 2025-07-17T09:05:52.6578757Z Args: 2025-07-17T09:05:52.6578836Z optim: The local optimizer. 2025-07-17T09:05:52.6578987Z averager: A model averager instance to run post-localSGD algorithm. 2025-07-17T09:05:52.6578990Z 2025-07-17T09:05:52.6579058Z Example:: 2025-07-17T09:05:52.6579061Z 2025-07-17T09:05:52.6579157Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T09:05:52.6579363Z >>> import torch 2025-07-17T09:05:52.6579519Z >>> import torch.distributed as dist 2025-07-17T09:05:52.6579686Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2025-07-17T09:05:52.6579762Z >>> import torch.nn as nn 2025-07-17T09:05:52.6579903Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2025-07-17T09:05:52.6580190Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2025-07-17T09:05:52.6580279Z >>> PostLocalSGDState, 2025-07-17T09:05:52.6580353Z >>> post_localSGD_hook, 2025-07-17T09:05:52.6580423Z >>> ) 2025-07-17T09:05:52.6580480Z >>> 2025-07-17T09:05:52.6580596Z >>> model = nn.parallel.DistributedDataParallel( 2025-07-17T09:05:52.6580696Z >>> module, device_ids=[rank], output_device=rank 2025-07-17T09:05:52.6580768Z >>> ) 2025-07-17T09:05:52.6580829Z >>> 2025-07-17T09:05:52.6580941Z >>> # Register a post-localSGD communication hook. 2025-07-17T09:05:52.6581126Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2025-07-17T09:05:52.6581242Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-07-17T09:05:52.6581300Z >>> 2025-07-17T09:05:52.6581438Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2025-07-17T09:05:52.6581589Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2025-07-17T09:05:52.6581707Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-07-17T09:05:52.6581843Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2025-07-17T09:05:52.6581935Z >>> opt = PostLocalSGDOptimizer( 2025-07-17T09:05:52.6582008Z >>> optim=local_optim, 2025-07-17T09:05:52.6582162Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2025-07-17T09:05:52.6582233Z >>> ) 2025-07-17T09:05:52.6582291Z >>> 2025-07-17T09:05:52.6582443Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2025-07-17T09:05:52.6582627Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2025-07-17T09:05:52.6582859Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2025-07-17T09:05:52.6582931Z >>> for step in range(0, 200): 2025-07-17T09:05:52.6583010Z >>> opt.zero_grad() 2025-07-17T09:05:52.6583096Z >>> loss = loss_fn(output, labels) 2025-07-17T09:05:52.6583179Z >>> loss.backward() 2025-07-17T09:05:52.6583247Z >>> opt.step() 2025-07-17T09:05:52.6583251Z 2025-07-17T09:05:52.6583415Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.6583419Z 2025-07-17T09:05:52.6594606Z msg = Cannot scrape callname=DistributedOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/optimizer.py line=129. 2025-07-17T09:05:52.6594833Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.6594850Z 2025-07-17T09:05:52.6595018Z DistributedOptimizer takes remote references to parameters scattered 2025-07-17T09:05:52.6595176Z across workers and applies the given optimizer locally for each parameter. 2025-07-17T09:05:52.6595180Z 2025-07-17T09:05:52.6595331Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2025-07-17T09:05:52.6595442Z to retrieve the gradients for specific parameters. 2025-07-17T09:05:52.6595445Z 2025-07-17T09:05:52.6595518Z Concurrent calls to 2025-07-17T09:05:52.6595656Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2025-07-17T09:05:52.6595758Z either from the same or different clients, will 2025-07-17T09:05:52.6595922Z be serialized on each worker -- as each worker's optimizer can only work 2025-07-17T09:05:52.6596051Z on one set of gradients at a time. However, there is no guarantee that 2025-07-17T09:05:52.6596208Z the full forward-backward-optimizer sequence will execute for one client 2025-07-17T09:05:52.6596598Z at a time. This means that the gradients being applied may not correspond 2025-07-17T09:05:52.6596740Z to the latest forward pass executed on a given worker. Also, there is no 2025-07-17T09:05:52.6596820Z guaranteed ordering across workers. 2025-07-17T09:05:52.6596825Z 2025-07-17T09:05:52.6596997Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2025-07-17T09:05:52.6597272Z by default, so that optimizer updates are not blocked by the Python Global 2025-07-17T09:05:52.6597436Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2025-07-17T09:05:52.6597575Z Model Parallel). This feature is currently enabled for most optimizers. You 2025-07-17T09:05:52.6597734Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2025-07-17T09:05:52.6597813Z for your own custom optimizers. 2025-07-17T09:05:52.6597816Z 2025-07-17T09:05:52.6597886Z Args: 2025-07-17T09:05:52.6598018Z optimizer_class (optim.Optimizer): the class of optimizer to 2025-07-17T09:05:52.6598107Z instantiate on each worker. 2025-07-17T09:05:52.6598236Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2025-07-17T09:05:52.6598311Z to optimize. 2025-07-17T09:05:52.6598442Z args: arguments to pass to the optimizer constructor on each worker. 2025-07-17T09:05:52.6598587Z kwargs: arguments to pass to the optimizer constructor on each worker. 2025-07-17T09:05:52.6598591Z 2025-07-17T09:05:52.6598660Z Example:: 2025-07-17T09:05:52.6598756Z >>> # xdoctest: +SKIP("distributed") 2025-07-17T09:05:52.6598867Z >>> import torch.distributed.autograd as dist_autograd 2025-07-17T09:05:52.6598959Z >>> import torch.distributed.rpc as rpc 2025-07-17T09:05:52.6599029Z >>> from torch import optim 2025-07-17T09:05:52.6599156Z >>> from torch.distributed.optim import DistributedOptimizer 2025-07-17T09:05:52.6599216Z >>> 2025-07-17T09:05:52.6599309Z >>> with dist_autograd.context() as context_id: 2025-07-17T09:05:52.6599395Z >>> # Forward pass. 2025-07-17T09:05:52.6599520Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2025-07-17T09:05:52.6599643Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2025-07-17T09:05:52.6599727Z >>> loss = rref1.to_here() + rref2.to_here() 2025-07-17T09:05:52.6599797Z >>> 2025-07-17T09:05:52.6599872Z >>> # Backward pass. 2025-07-17T09:05:52.6599979Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2025-07-17T09:05:52.6600039Z >>> 2025-07-17T09:05:52.6600111Z >>> # Optimizer. 2025-07-17T09:05:52.6600199Z >>> dist_optim = DistributedOptimizer( 2025-07-17T09:05:52.6600271Z >>> optim.SGD, 2025-07-17T09:05:52.6600339Z >>> [rref1, rref2], 2025-07-17T09:05:52.6600407Z >>> lr=0.05, 2025-07-17T09:05:52.6600466Z >>> ) 2025-07-17T09:05:52.6600550Z >>> dist_optim.step(context_id) 2025-07-17T09:05:52.6600555Z 2025-07-17T09:05:52.6600666Z __ https://github.com/pytorch/tutorials/pull/1465 2025-07-17T09:05:52.6600672Z 2025-07-17T09:05:52.6600839Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.6600842Z 2025-07-17T09:05:52.6936311Z msg = Cannot scrape callname=register_sharding in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/experimental/_register_sharding.py line=25. 2025-07-17T09:05:52.6936585Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.6936591Z 2025-07-17T09:05:52.6936772Z :meth:`register_sharding` is an experimental API that allows users to register sharding 2025-07-17T09:05:52.6936934Z strategies for an operator when the tensor inputs and outputs are DTensor. 2025-07-17T09:05:52.6937089Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``, 2025-07-17T09:05:52.6937247Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2) 2025-07-17T09:05:52.6937712Z when users would like to overwrite default sharding strategies of existing operators. 2025-07-17T09:05:52.6937829Z 2025-07-17T09:05:52.6937902Z Args: 2025-07-17T09:05:52.6937995Z op (Union[OpOverload, List[OpOverload]]): 2025-07-17T09:05:52.6938128Z An op or a list of ops to register the customized sharding function. 2025-07-17T09:05:52.6938132Z 2025-07-17T09:05:52.6938194Z Returns: 2025-07-17T09:05:52.6938537Z A function decorator which can be used to wrap a function that defines the sharding 2025-07-17T09:05:52.6938707Z strategy for the operator specified in ``op``. The defined sharding strategy will be 2025-07-17T09:05:52.6938885Z registered to DTensor and will override the default sharding strategy if DTensor has 2025-07-17T09:05:52.6939074Z already implemented the operator. The customized sharding function takes the same inputs 2025-07-17T09:05:52.6939237Z as the original op (except that if an arg is a :class:`torch.Tensor`, it will be 2025-07-17T09:05:52.6939411Z replaced by a tensor-like object that DTensor uses internally). The function should 2025-07-17T09:05:52.6939591Z return a sequence of 2-tuples, each specifying acceptable output placements and its 2025-07-17T09:05:52.6939676Z corresponding intput placements. 2025-07-17T09:05:52.6939680Z 2025-07-17T09:05:52.6939751Z Example: 2025-07-17T09:05:52.6939830Z >>> # xdoctest: +SKIP("distributed") 2025-07-17T09:05:52.6939933Z >>> @register_sharding(aten._softmax.default) 2025-07-17T09:05:52.6940041Z >>> def custom_softmax_sharding(x, dim, half_to_float): 2025-07-17T09:05:52.6940140Z >>> softmax_dim = dim if dim >= 0 else dim + x.ndim 2025-07-17T09:05:52.6940217Z >>> acceptable_shardings = [] 2025-07-17T09:05:52.6940288Z >>> 2025-07-17T09:05:52.6940407Z >>> all_replicate = ([Replicate()], [Replicate(), None, None]) 2025-07-17T09:05:52.6940506Z >>> acceptable_shardings.append(all_replicate) 2025-07-17T09:05:52.6940577Z >>> 2025-07-17T09:05:52.6940658Z >>> for sharding_dim in range(x.ndim): 2025-07-17T09:05:52.6940751Z >>> if sharding_dim != softmax_dim: 2025-07-17T09:05:52.6940820Z >>> all_sharded = ( 2025-07-17T09:05:52.6940903Z >>> [Shard(sharding_dim)], 2025-07-17T09:05:52.6940986Z >>> [Shard(sharding_dim), None, None], 2025-07-17T09:05:52.6941057Z >>> ) 2025-07-17T09:05:52.6941152Z >>> acceptable_shardings.append(all_sharded) 2025-07-17T09:05:52.6941222Z >>> 2025-07-17T09:05:52.6941299Z >>> return acceptable_shardings 2025-07-17T09:05:52.6941302Z 2025-07-17T09:05:52.6941461Z .. note:: This API is currently experimental and subject to change 2025-07-17T09:05:52.6941465Z 2025-07-17T09:05:52.6941623Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.6941626Z 2025-07-17T09:05:52.7044137Z msg = Cannot scrape callname=local_map in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/experimental/_func_map.py line=35. 2025-07-17T09:05:52.7044411Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.7044416Z 2025-07-17T09:05:52.7044592Z :meth:`local_map` is an experimental API that allows users to pass :class:`DTensor` s 2025-07-17T09:05:52.7044783Z to a function that is written to be applied on ``torch.Tensor`` s. It is done by extracting 2025-07-17T09:05:52.7044950Z the local components of :class:`DTensor`, call the function, and wrap the outputs to 2025-07-17T09:05:52.7045065Z :class:`DTensor` according to the ``out_placements``. 2025-07-17T09:05:52.7045069Z 2025-07-17T09:05:52.7045133Z Args: 2025-07-17T09:05:52.7045280Z func (Callable): the function to be applied on each local shard of 2025-07-17T09:05:52.7045354Z :class:`DTensor` s. 2025-07-17T09:05:52.7045511Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2025-07-17T09:05:52.7045914Z the desired placements of the :class:`DTensor` s in ``func``'s flattened output. 2025-07-17T09:05:52.7046163Z If the flattened ``output`` is a single value, the ``out_placements`` should be 2025-07-17T09:05:52.7046322Z of type `PlacementType`. Otherwise if the flattened ``output`` has multiple 2025-07-17T09:05:52.7046487Z values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1 2025-07-17T09:05:52.7046710Z mapping to the flattened ``output``. 2025-07-17T09:05:52.7046843Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2025-07-17T09:05:52.7047019Z placements (a `Tuple[Placement]` value). For non-Tensor output, the `PlacementType` 2025-07-17T09:05:52.7047092Z should be `None`. 2025-07-17T09:05:52.7047246Z Note that the only exception is when no :class:`DTensor` argument is passed 2025-07-17T09:05:52.7047383Z in. In this case, even if `out_placements` is not `None`, the result function 2025-07-17T09:05:52.7047553Z should ignore the desired placements because the function is not running with 2025-07-17T09:05:52.7047629Z :class:`DTensor` s. 2025-07-17T09:05:52.7047750Z in_placements (Tuple[`PlacementType`, ...], optional): 2025-07-17T09:05:52.7047918Z the required placements of the :class:`DTensor` s in the flattened inputs of ``func``. 2025-07-17T09:05:52.7048078Z If ``in_placements`` is specified, :meth:`local_map` would examine whether the 2025-07-17T09:05:52.7048219Z placements of each :class:`DTensor` argument is the same as the required 2025-07-17T09:05:52.7048347Z placements or not. If the placements are not the same and 2025-07-17T09:05:52.7048496Z ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if 2025-07-17T09:05:52.7048654Z ``redistribute_inputs`` is ``True``, the argument will be first redistributed to 2025-07-17T09:05:52.7048804Z the required sharding placements before passing its local tensor to ``func``. 2025-07-17T09:05:52.7048952Z The only exception is when required placements are not ``None`` and the 2025-07-17T09:05:52.7049093Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2025-07-17T09:05:52.7049236Z will be skipped and the argument will be directly passed to ``func``. 2025-07-17T09:05:52.7049371Z If ``in_placements`` is ``None``, no placements examination will be performed. 2025-07-17T09:05:52.7049447Z Default: None 2025-07-17T09:05:52.7049564Z in_grad_placements (Tuple[`PlacementType`, ...], optional): 2025-07-17T09:05:52.7049706Z the placements hint of the :class:`DTensor` s gradient corresponds 2025-07-17T09:05:52.7049832Z to the flattened input DTensor. This argument is the hint that user 2025-07-17T09:05:52.7049958Z can give to :meth:`to_local` in case the gradient layout of the 2025-07-17T09:05:52.7050090Z local tensor input does not match its :class:`DTensor` input layout. 2025-07-17T09:05:52.7050220Z If not specified, we will assume the gradient layout of the local 2025-07-17T09:05:52.7050351Z tensor input remains the same as the original :class:`DTensor` input 2025-07-17T09:05:52.7050464Z and use that for gradient computation. Default: None. 2025-07-17T09:05:52.7050556Z device_mesh (:class:`DeviceMesh`, optional): 2025-07-17T09:05:52.7050704Z the device mesh that the output :class:`DTensor` s are placed on. If not 2025-07-17T09:05:52.7050853Z specified, this will be inferred from the first input :class:`DTensor`'s device 2025-07-17T09:05:52.7050935Z mesh. Default: None. 2025-07-17T09:05:52.7050940Z 2025-07-17T09:05:52.7051007Z Keyword Args: 2025-07-17T09:05:52.7051100Z redistribute_inputs (bool, optional): 2025-07-17T09:05:52.7051248Z the bool value indicating whether to reshard the input :class:`DTensor` s when 2025-07-17T09:05:52.7051395Z their placements are different from the required input placements. If this 2025-07-17T09:05:52.7051603Z value is ``False`` and some :class:`DTensor` input has a different placement, 2025-07-17T09:05:52.7051750Z an exception will be raised. Default: False. 2025-07-17T09:05:52.7051787Z 2025-07-17T09:05:52.7051848Z Returns: 2025-07-17T09:05:52.7052000Z A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor` 2025-07-17T09:05:52.7052259Z and returns a :class:`DTensor` constructed from the return value of ``func``. 2025-07-17T09:05:52.7052262Z 2025-07-17T09:05:52.7052320Z Raises: 2025-07-17T09:05:52.7052476Z AssertionError: For any non-DTensor output, we require its corresponding 2025-07-17T09:05:52.7052627Z output placement in ``out_placements`` be None. An AssertionError will be raised 2025-07-17T09:05:52.7052712Z if this is not the case. 2025-07-17T09:05:52.7052716Z 2025-07-17T09:05:52.7052866Z ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs 2025-07-17T09:05:52.7052979Z a redistribution according to ``in_placements``. 2025-07-17T09:05:52.7052984Z 2025-07-17T09:05:52.7053041Z Example: 2025-07-17T09:05:52.7053128Z >>> # xdoctest: +SKIP("distributed") 2025-07-17T09:05:52.7053216Z >>> def mm_allreduce_forward(device_mesh, W, X): 2025-07-17T09:05:52.7053311Z >>> partial_sum_tensor = torch.mm(W, X) 2025-07-17T09:05:52.7053458Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2025-07-17T09:05:52.7053542Z >>> return reduced_tensor 2025-07-17T09:05:52.7053602Z >>> 2025-07-17T09:05:52.7053700Z >>> W = torch.randn(12, 8, requires_grad=False) 2025-07-17T09:05:52.7053779Z >>> X = torch.randn(8, 16, requires_grad=False) 2025-07-17T09:05:52.7053855Z >>> Y = torch.mm(W, X) 2025-07-17T09:05:52.7053972Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2025-07-17T09:05:52.7054087Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2025-07-17T09:05:52.7054146Z >>> 2025-07-17T09:05:52.7054321Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor convertion 2025-07-17T09:05:52.7054405Z >>> local_mm_allreduce_forward = local_map( 2025-07-17T09:05:52.7054487Z >>> mm_allreduce_forward, 2025-07-17T09:05:52.7054569Z >>> out_placements=[Replicate()], 2025-07-17T09:05:52.7054649Z >>> in_placements=[col_wise, row_wise], 2025-07-17T09:05:52.7054735Z >>> device_mesh=device_mesh, 2025-07-17T09:05:52.7054796Z >>> ) 2025-07-17T09:05:52.7054861Z >>> 2025-07-17T09:05:52.7054926Z >>> W_dt = distribute_tensor( 2025-07-17T09:05:52.7055011Z ... W, device_mesh, (col_wise) 2025-07-17T09:05:52.7055087Z ... ) # col-wisely sharded W tensor 2025-07-17T09:05:52.7055164Z >>> X_dt = distribute_tensor( 2025-07-17T09:05:52.7055233Z ... X, device_mesh, (row_wise) 2025-07-17T09:05:52.7055313Z ... ) # row-wisely sharded X tensor 2025-07-17T09:05:52.7055392Z >>> Y_dt = local_mm_allreduce_forward( 2025-07-17T09:05:52.7055474Z ... device_mesh, W_dt, X_dt 2025-07-17T09:05:52.7055573Z ... ) # apply local_mm_allreduce_forward to DTensors 2025-07-17T09:05:52.7055577Z 2025-07-17T09:05:52.7055716Z .. note:: This API is currently experimental and subject to change 2025-07-17T09:05:52.7055720Z 2025-07-17T09:05:52.7055873Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.7055876Z 2025-07-17T09:05:52.7154802Z msg = Cannot scrape callname=PrepareModuleInput in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py line=428. 2025-07-17T09:05:52.7155066Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.7155072Z 2025-07-17T09:05:52.7155431Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2025-07-17T09:05:52.7155702Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2025-07-17T09:05:52.7156092Z 2025-07-17T09:05:52.7156179Z Keyword Args: 2025-07-17T09:05:52.7156346Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-07-17T09:05:52.7156622Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2025-07-17T09:05:52.7157044Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2025-07-17T09:05:52.7157165Z as a placeholder. default: None. 2025-07-17T09:05:52.7157341Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-07-17T09:05:52.7157641Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-07-17T09:05:52.7157944Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2025-07-17T09:05:52.7158080Z input_kwarg_layouts (Dict[str, Placement]): 2025-07-17T09:05:52.7158381Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2025-07-17T09:05:52.7158482Z default: None 2025-07-17T09:05:52.7158615Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2025-07-17T09:05:52.7158926Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-07-17T09:05:52.7159045Z have the desired DTensor layouts. default: None. 2025-07-17T09:05:52.7159155Z use_local_output (bool, optional): 2025-07-17T09:05:52.7159431Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2025-07-17T09:05:52.7159528Z Returns: 2025-07-17T09:05:52.7159800Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2025-07-17T09:05:52.7159805Z 2025-07-17T09:05:52.7159908Z Example:: 2025-07-17T09:05:52.7160005Z >>> # xdoctest: +SKIP(failing) 2025-07-17T09:05:52.7160279Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2025-07-17T09:05:52.7160441Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-07-17T09:05:52.7160527Z >>> ... 2025-07-17T09:05:52.7160773Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-07-17T09:05:52.7160902Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-07-17T09:05:52.7160997Z >>> 2025-07-17T09:05:52.7161280Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2025-07-17T09:05:52.7161397Z >>> # and then redistributed to Replicated DTensor. 2025-07-17T09:05:52.7161497Z >>> parallelize_module( 2025-07-17T09:05:52.7161611Z >>> block, # this can be a submodule or module 2025-07-17T09:05:52.7161703Z >>> tp_mesh, 2025-07-17T09:05:52.7161795Z >>> parallelize_plan={ 2025-07-17T09:05:52.7161919Z >>> "attn": PrepareModuleInput( 2025-07-17T09:05:52.7162055Z >>> input_layouts=(Shard(0), None, None, ...), 2025-07-17T09:05:52.7162213Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2025-07-17T09:05:52.7162296Z >>> ), 2025-07-17T09:05:52.7162365Z >>> } 2025-07-17T09:05:52.7162449Z >>> ) 2025-07-17T09:05:52.7162453Z 2025-07-17T09:05:52.7162657Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.7162662Z 2025-07-17T09:05:52.7163188Z msg = Cannot scrape callname=PrepareModuleOutput in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py line=597. 2025-07-17T09:05:52.7163398Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.7163402Z 2025-07-17T09:05:52.7163696Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2025-07-17T09:05:52.7164042Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2025-07-17T09:05:52.7164107Z 2025-07-17T09:05:52.7164184Z Keyword Args: 2025-07-17T09:05:52.7164326Z output_layouts (Union[Placement, Tuple[Placement]]): 2025-07-17T09:05:52.7164582Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2025-07-17T09:05:52.7165022Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2025-07-17T09:05:52.7165193Z ``None`` need to be specified as a placeholder. 2025-07-17T09:05:52.7165373Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2025-07-17T09:05:52.7165729Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2025-07-17T09:05:52.7165850Z have the desired DTensor layouts. 2025-07-17T09:05:52.7165957Z use_local_output (bool, optional): 2025-07-17T09:05:52.7166237Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2025-07-17T09:05:52.7166328Z Returns: 2025-07-17T09:05:52.7166549Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2025-07-17T09:05:52.7166564Z 2025-07-17T09:05:52.7166648Z Example:: 2025-07-17T09:05:52.7166761Z >>> # xdoctest: +SKIP(failing) 2025-07-17T09:05:52.7167012Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2025-07-17T09:05:52.7167168Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-07-17T09:05:52.7167261Z >>> ... 2025-07-17T09:05:52.7167522Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-07-17T09:05:52.7167641Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-07-17T09:05:52.7167719Z >>> 2025-07-17T09:05:52.7168036Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2025-07-17T09:05:52.7168148Z >>> # and then redistributed to Sharded DTensor. 2025-07-17T09:05:52.7168244Z >>> parallelize_module( 2025-07-17T09:05:52.7168353Z >>> block, # this can be a submodule or module 2025-07-17T09:05:52.7168438Z >>> tp_mesh, 2025-07-17T09:05:52.7168564Z >>> parallelize_plan = PrepareModuleOutput( 2025-07-17T09:05:52.7168685Z >>> output_layouts=Replicate(), 2025-07-17T09:05:52.7168789Z >>> desired_output_layouts=Shard(0) 2025-07-17T09:05:52.7168873Z >>> ) 2025-07-17T09:05:52.7168973Z >>> ) 2025-07-17T09:05:52.7168977Z 2025-07-17T09:05:52.7169205Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.7169209Z 2025-07-17T09:05:52.7169754Z msg = Cannot scrape callname=PrepareModuleInputOutput in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py line=705. 2025-07-17T09:05:52.7169963Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.7169966Z 2025-07-17T09:05:52.7170285Z Configure the nn.Module's inputs (and outputs) to convert the input tensors (and output tensors, respectively) of the nn.Module 2025-07-17T09:05:52.7170659Z to DTensors at runtime according to ``input_layouts`` (and output_layouts, respectively), and perform layout redistribution 2025-07-17T09:05:52.7170971Z according to the ``desired_input_layouts`` (and ``desired_output_layouts``, respectively). This is a combination of 2025-07-17T09:05:52.7171139Z :class:`PrepareModuleInput` and :class:`PrepareModuleOutput`. 2025-07-17T09:05:52.7171143Z 2025-07-17T09:05:52.7171216Z Keyword Args: 2025-07-17T09:05:52.7171379Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-07-17T09:05:52.7171634Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2025-07-17T09:05:52.7172012Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2025-07-17T09:05:52.7172173Z as a placeholder. default: None. 2025-07-17T09:05:52.7172389Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-07-17T09:05:52.7172794Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-07-17T09:05:52.7173138Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2025-07-17T09:05:52.7173243Z input_kwarg_layouts (Dict[str, Placement]): 2025-07-17T09:05:52.7173584Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2025-07-17T09:05:52.7173666Z default: None 2025-07-17T09:05:52.7173814Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2025-07-17T09:05:52.7174137Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-07-17T09:05:52.7174295Z have the desired DTensor layouts. default: None. 2025-07-17T09:05:52.7174393Z use_local_input (bool, optional): 2025-07-17T09:05:52.7174664Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2025-07-17T09:05:52.7174791Z output_layouts (Union[Placement, Tuple[Placement]]): 2025-07-17T09:05:52.7175048Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2025-07-17T09:05:52.7175358Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2025-07-17T09:05:52.7175486Z ``None`` need to be specified as a placeholder. 2025-07-17T09:05:52.7175659Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2025-07-17T09:05:52.7175984Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2025-07-17T09:05:52.7176076Z have the desired DTensor layouts. 2025-07-17T09:05:52.7176181Z use_local_output (bool, optional): 2025-07-17T09:05:52.7176459Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2025-07-17T09:05:52.7176545Z Returns: 2025-07-17T09:05:52.7176817Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs and outputs. 2025-07-17T09:05:52.7176821Z 2025-07-17T09:05:52.7176906Z Example:: 2025-07-17T09:05:52.7176991Z >>> # xdoctest: +SKIP(failing) 2025-07-17T09:05:52.7177283Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInputOutput 2025-07-17T09:05:52.7177477Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-07-17T09:05:52.7177561Z >>> ... 2025-07-17T09:05:52.7177791Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-07-17T09:05:52.7177903Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-07-17T09:05:52.7177979Z >>> 2025-07-17T09:05:52.7178234Z >>> # According to the style specified below, the first input of attn will be annotated as Sharded DTensor 2025-07-17T09:05:52.7178577Z >>> # and then redistributed to Replicated DTensor, and the output of the TransformerBlock will be annotated 2025-07-17T09:05:52.7178760Z >>> # as Replicated DTensor and then redistributed to Sharded DTensor. 2025-07-17T09:05:52.7178869Z >>> parallelize_module( 2025-07-17T09:05:52.7178989Z >>> block, # this can be a submodule or module 2025-07-17T09:05:52.7179088Z >>> tp_mesh, 2025-07-17T09:05:52.7179187Z >>> parallelize_plan={ 2025-07-17T09:05:52.7179305Z >>> "attn": PrepareModuleInputOutput( 2025-07-17T09:05:52.7179423Z >>> input_layouts=(Shard(0), None, None, ...), 2025-07-17T09:05:52.7179647Z >>> desired_input_layouts=(Replicate(), None, None, ...), 2025-07-17T09:05:52.7179797Z >>> output_layouts=Replicate(), 2025-07-17T09:05:52.7179921Z >>> desired_output_layouts=Shard(0), 2025-07-17T09:05:52.7180006Z >>> ), 2025-07-17T09:05:52.7180107Z >>> } 2025-07-17T09:05:52.7180182Z >>> ) 2025-07-17T09:05:52.7180185Z 2025-07-17T09:05:52.7180512Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.7180517Z 2025-07-17T09:05:52.7752844Z msg = Cannot scrape callname=_CustomReducer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/pipelining/microbatch.py line=29. 2025-07-17T09:05:52.7753068Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.7753102Z 2025-07-17T09:05:52.7753258Z Custom reducer class that can be used to specify a custom operation that 2025-07-17T09:05:52.7753421Z reduces losses of multiple microbatches into one value. 2025-07-17T09:05:52.7753434Z 2025-07-17T09:05:52.7753504Z Example: 2025-07-17T09:05:52.7753589Z >>> # xdoctest: +SKIP 2025-07-17T09:05:52.7753672Z >>> sum_reducer = _CustomReducer( 2025-07-17T09:05:52.7753752Z >>> torch.tensor(0.0), 2025-07-17T09:05:52.7753821Z >>> lambda a, b: a + b 2025-07-17T09:05:52.7753891Z >>> ) 2025-07-17T09:05:52.7753895Z 2025-07-17T09:05:52.7754060Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.7754064Z 2025-07-17T09:05:52.7973907Z msg = Cannot scrape callname=load_sharded_optimizer_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/optimizer.py line=221. 2025-07-17T09:05:52.7974135Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.7974140Z 2025-07-17T09:05:52.7974294Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2025-07-17T09:05:52.7974318Z 2025-07-17T09:05:52.7974435Z This is the current recommended way to checkpoint FSDP. 2025-07-17T09:05:52.7974523Z >>> # xdoctest: +SKIP 2025-07-17T09:05:52.7974623Z >>> import torch.distributed.checkpoint as dist_cp 2025-07-17T09:05:52.7974703Z >>> # Save 2025-07-17T09:05:52.7974780Z >>> model: torch.nn.Model 2025-07-17T09:05:52.7974872Z >>> optim_params = model.parameters() 2025-07-17T09:05:52.7974969Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2025-07-17T09:05:52.7975045Z >>> # Save 2025-07-17T09:05:52.7975182Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2025-07-17T09:05:52.7975257Z >>> state_dict = { 2025-07-17T09:05:52.7975366Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2025-07-17T09:05:52.7975450Z >>> "model": model.state_dict() 2025-07-17T09:05:52.7975509Z >>> } 2025-07-17T09:05:52.7975595Z >>> dist_cp.save_state_dict( 2025-07-17T09:05:52.7975672Z >>> state_dict=optim_state, 2025-07-17T09:05:52.7975806Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2025-07-17T09:05:52.7975905Z >>> planner=dist_cp.DefaultSavePlanner(), 2025-07-17T09:05:52.7975967Z >>> ) 2025-07-17T09:05:52.7976036Z >>> 2025-07-17T09:05:52.7976100Z >>> # Load 2025-07-17T09:05:52.7976251Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2025-07-17T09:05:52.7976339Z >>> model_state_dict = model_tp.state_dict() 2025-07-17T09:05:52.7976420Z >>> checkpoint = { 2025-07-17T09:05:52.7976507Z >>> "model": model_state_dict 2025-07-17T09:05:52.7976580Z >>> } 2025-07-17T09:05:52.7976654Z >>> dist_cp.load_state_dict( 2025-07-17T09:05:52.7976735Z >>> state_dict=checkpoint, 2025-07-17T09:05:52.7976856Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2025-07-17T09:05:52.7976949Z >>> planner=dist_cp.DefaultLoadPlanner(), 2025-07-17T09:05:52.7977009Z >>> ) 2025-07-17T09:05:52.7977125Z >>> model.load_state_dict(checkpoint["model_state"]) 2025-07-17T09:05:52.7977746Z >>> 2025-07-17T09:05:52.7977860Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2025-07-17T09:05:52.7978041Z >>> model_state_dict, 2025-07-17T09:05:52.7978120Z >>> optimizer_key="optimizer", 2025-07-17T09:05:52.7978244Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2025-07-17T09:05:52.7978304Z >>> ) 2025-07-17T09:05:52.7978371Z >>> 2025-07-17T09:05:52.7993657Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2025-07-17T09:05:52.7993800Z >>> model, optim, optim_state["optimizer"] 2025-07-17T09:05:52.7993855Z >>> ) 2025-07-17T09:05:52.7993924Z >>> 2025-07-17T09:05:52.7994007Z >>> optim.load_state_dict(flattened_osd) 2025-07-17T09:05:52.7994014Z 2025-07-17T09:05:52.7994194Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.7994197Z 2025-07-17T09:05:52.8229912Z msg = Cannot scrape callname=SavePlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/planner.py line=122. 2025-07-17T09:05:52.8230151Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.8230156Z 2025-07-17T09:05:52.8230333Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2025-07-17T09:05:52.8230337Z 2025-07-17T09:05:52.8230535Z SavePlanners are stateful objects that can be used to customize the whole save process. 2025-07-17T09:05:52.8230539Z 2025-07-17T09:05:52.8230716Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2025-07-17T09:05:52.8230810Z will be visible to the whole process. 2025-07-17T09:05:52.8230813Z 2025-07-17T09:05:52.8230978Z A planner subclass can expect the following sequence of calls during save_state_dict: 2025-07-17T09:05:52.8230981Z 2025-07-17T09:05:52.8231085Z 1) set_up_planner - called on all ranks. 2025-07-17T09:05:52.8231168Z Signals the start of a checkpoint save. 2025-07-17T09:05:52.8231206Z 2025-07-17T09:05:52.8231293Z 2) create_local_plan - called on all ranks. 2025-07-17T09:05:52.8231478Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2025-07-17T09:05:52.8231481Z 2025-07-17T09:05:52.8231596Z 3) create_global_plan - called on the coordinator rank only. 2025-07-17T09:05:52.8231733Z Takes the SavePlan from all ranks and make any global decision. 2025-07-17T09:05:52.8231736Z 2025-07-17T09:05:52.8231814Z 4) finish_plan - called on all ranks. 2025-07-17T09:05:52.8231960Z This gives each rank a chance to adjust to global planning decisions. 2025-07-17T09:05:52.8231963Z 2025-07-17T09:05:52.8232065Z 5) resolve_data - called multiple times on each rank 2025-07-17T09:05:52.8232202Z Lookups a value on the `state_dict` for the storage layer to write. 2025-07-17T09:05:52.8232206Z 2025-07-17T09:05:52.8232383Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2025-07-17T09:05:52.8232511Z most changes can be expressed by changes in a single method. 2025-07-17T09:05:52.8232515Z 2025-07-17T09:05:52.8232598Z There are 3 usual patterns of extension: 2025-07-17T09:05:52.8232602Z 2025-07-17T09:05:52.8232768Z Rewriting state_dict. This is the simplest way to extend the save process as it 2025-07-17T09:05:52.8232908Z doesn't requite understanding the intrincacies of how SavePlan works: 2025-07-17T09:05:52.8232911Z 2025-07-17T09:05:52.8232998Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T09:05:52.8233097Z >>> class RenamePlanner(DefaultSavePlanner): 2025-07-17T09:05:52.8233188Z >>> def set_up_planner( 2025-07-17T09:05:52.8233248Z >>> self, 2025-07-17T09:05:52.8233336Z >>> state_dict: STATE_DICT_TYPE, 2025-07-17T09:05:52.8233424Z >>> storage_meta: Optional[StorageMeta], 2025-07-17T09:05:52.8233504Z >>> is_coordinator: bool, 2025-07-17T09:05:52.8233604Z >>> ) -> None: 2025-07-17T09:05:52.8233700Z >>> # prefix all keys with `foo_`` 2025-07-17T09:05:52.8233885Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2025-07-17T09:05:52.8234268Z 2025-07-17T09:05:52.8234489Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2025-07-17T09:05:52.8234492Z 2025-07-17T09:05:52.8234576Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T09:05:52.8234673Z >>> class FP16Planner(DefaultSavePlanner): 2025-07-17T09:05:52.8234750Z >>> def create_local_plan(self): 2025-07-17T09:05:52.8234989Z >>> plan = super().create_local_plan() 2025-07-17T09:05:52.8235074Z >>> for p in plan: 2025-07-17T09:05:52.8235157Z >>> if p.tensor_data is not None: 2025-07-17T09:05:52.8235277Z >>> p.tensor_data.properties.dtype = torch.float16 2025-07-17T09:05:52.8235341Z >>> return plan 2025-07-17T09:05:52.8235410Z >>> 2025-07-17T09:05:52.8235488Z >>> def resolve_data(self, write_item): 2025-07-17T09:05:52.8235581Z >>> item = super().resolve_data(write_item) 2025-07-17T09:05:52.8235754Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2025-07-17T09:05:52.8235760Z 2025-07-17T09:05:52.8235972Z Using the global planning step to make central decisions that can't be made individually by each rank 2025-07-17T09:05:52.8235975Z 2025-07-17T09:05:52.8236051Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T09:05:52.8236138Z >>> from itertools import zip_longest 2025-07-17T09:05:52.8236218Z >>> from dataclasses import replace 2025-07-17T09:05:52.8236341Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2025-07-17T09:05:52.8236507Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2025-07-17T09:05:52.8236602Z >>> # This sample doesn't handle ShardedTensors 2025-07-17T09:05:52.8236687Z >>> def create_global_plan(self, all_plans): 2025-07-17T09:05:52.8236799Z >>> iters = [iter(all_plans[0].items)] * len(all_plans) 2025-07-17T09:05:52.8236868Z >>> items_per_rank = [ 2025-07-17T09:05:52.8236972Z >>> [item for item in items if item is not None] 2025-07-17T09:05:52.8237082Z >>> for items in zip(*zip_longest(*iters), strict=True) 2025-07-17T09:05:52.8237151Z >>> ] 2025-07-17T09:05:52.8237220Z >>> all_plans = [ 2025-07-17T09:05:52.8237297Z >>> replace(plan, items=items) 2025-07-17T09:05:52.8237427Z >>> for plan, items in zip(all_plans, items_per_rank, strict=True) 2025-07-17T09:05:52.8237488Z >>> ] 2025-07-17T09:05:52.8237596Z >>> return super().create_global_plan(all_plans) 2025-07-17T09:05:52.8237600Z 2025-07-17T09:05:52.8237757Z Finally, some planners need to save additional metadata in the checkpoint, this is 2025-07-17T09:05:52.8237927Z accomplished by having each rank contribute their data items in the local plan and 2025-07-17T09:05:52.8238007Z the global planner aggregate them: 2025-07-17T09:05:52.8238010Z 2025-07-17T09:05:52.8238094Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T09:05:52.8238192Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2025-07-17T09:05:52.8238292Z >>> def create_local_plan(self) -> SavePlan: 2025-07-17T09:05:52.8238376Z >>> plan = super().create_local_plan() 2025-07-17T09:05:52.8238494Z >>> return replace(plan, planner_data="per-rank-data") 2025-07-17T09:05:52.8238552Z >>> 2025-07-17T09:05:52.8238740Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2025-07-17T09:05:52.8238867Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2025-07-17T09:05:52.8238979Z >>> merged_data = [p.planner_data for p in global_plan] 2025-07-17T09:05:52.8239092Z >>> metadata = replace(metadata, planner_data=merged_data) 2025-07-17T09:05:52.8239181Z >>> return global_plan, metadata 2025-07-17T09:05:52.8239184Z 2025-07-17T09:05:52.8239339Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.8239342Z 2025-07-17T09:05:52.8239784Z msg = Cannot scrape callname=LoadPlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/planner.py line=305. 2025-07-17T09:05:52.8240005Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.8240008Z 2025-07-17T09:05:52.8240183Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2025-07-17T09:05:52.8240187Z 2025-07-17T09:05:52.8240469Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2025-07-17T09:05:52.8240473Z 2025-07-17T09:05:52.8240637Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2025-07-17T09:05:52.8240732Z will be visible to the whole process. 2025-07-17T09:05:52.8240735Z 2025-07-17T09:05:52.8240895Z A planner subclass can expect the following sequence of calls during load_state_dict: 2025-07-17T09:05:52.8240898Z 2025-07-17T09:05:52.8240989Z 1) set_up_planner - called on all ranks. 2025-07-17T09:05:52.8241081Z Signals the start of loading a checkpoint. 2025-07-17T09:05:52.8241085Z 2025-07-17T09:05:52.8241179Z 2) create_local_plan - called on all ranks. 2025-07-17T09:05:52.8241351Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2025-07-17T09:05:52.8241354Z 2025-07-17T09:05:52.8241480Z 3) create_global_plan - called on the coordinator rank only. 2025-07-17T09:05:52.8241610Z Takes the LoadPlan from all ranks and make any global decision. 2025-07-17T09:05:52.8241613Z 2025-07-17T09:05:52.8241719Z 4) load_bytes - called multiple times on each rank 2025-07-17T09:05:52.8241829Z This is called once per non-tensor value in state_dict. 2025-07-17T09:05:52.8241832Z 2025-07-17T09:05:52.8241979Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2025-07-17T09:05:52.8242094Z They are called in pair for each Tensor value in state_dict. 2025-07-17T09:05:52.8242097Z 2025-07-17T09:05:52.8242282Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2025-07-17T09:05:52.8242401Z most changes can be expressed by changes in a single method. 2025-07-17T09:05:52.8242404Z 2025-07-17T09:05:52.8242498Z There are two usual patterns of extension: 2025-07-17T09:05:52.8242501Z 2025-07-17T09:05:52.8242652Z Rewriting state_dict. This is the simplest way to extend the load process as it 2025-07-17T09:05:52.8242819Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2025-07-17T09:05:52.8242956Z to keep a reference to the original state_dict as load happens in place so 2025-07-17T09:05:52.8243072Z we need to be able to perform it in place 2025-07-17T09:05:52.8243075Z 2025-07-17T09:05:52.8243151Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T09:05:52.8243241Z >>> class RenamePlanner(DefaultLoadPlanner): 2025-07-17T09:05:52.8243309Z >>> def set_up_planner( 2025-07-17T09:05:52.8243377Z >>> self, 2025-07-17T09:05:52.8243453Z >>> state_dict: STATE_DICT_TYPE, 2025-07-17T09:05:52.8243530Z >>> metadata: Metadata, 2025-07-17T09:05:52.8243603Z >>> is_coordinator: bool, 2025-07-17T09:05:52.8243664Z >>> ) -> None: 2025-07-17T09:05:52.8243756Z >>> self.original_state_dict = state_dict 2025-07-17T09:05:52.8243869Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2025-07-17T09:05:52.8243934Z >>> 2025-07-17T09:05:52.8244007Z >>> if self.flatten_sharded_tensors: 2025-07-17T09:05:52.8244121Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2025-07-17T09:05:52.8244179Z >>> 2025-07-17T09:05:52.8244259Z >>> if self.flatten_state_dict: 2025-07-17T09:05:52.8244373Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2025-07-17T09:05:52.8244434Z >>> 2025-07-17T09:05:52.8244504Z >>> self.state_dict = state_dict 2025-07-17T09:05:52.8244584Z >>> self.metadata = metadata 2025-07-17T09:05:52.8244665Z >>> self.is_coordinator = is_coordinator 2025-07-17T09:05:52.8244815Z >>> 2025-07-17T09:05:52.8244895Z >>> def load_bytes(self, read_item, value): 2025-07-17T09:05:52.8245030Z >>> # Remove the "foo_" prefix 2025-07-17T09:05:52.8245221Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False) 2025-07-17T09:05:52.8245224Z 2025-07-17T09:05:52.8245227Z 2025-07-17T09:05:52.8245391Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2025-07-17T09:05:52.8245530Z 2025-07-17T09:05:52.8245607Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T09:05:52.8245859Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2025-07-17T09:05:52.8245974Z >>> def resolve_tensor(self, read_item): 2025-07-17T09:05:52.8246136Z >>> tensor = super().resolve_tensor(read_item) 2025-07-17T09:05:52.8246266Z >>> return torch.empty_like(tensor, device="cpu") 2025-07-17T09:05:52.8246360Z >>> 2025-07-17T09:05:52.8246531Z >>> def commit_tensor(self, read_item, tensor): 2025-07-17T09:05:52.8258976Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2025-07-17T09:05:52.8258993Z 2025-07-17T09:05:52.8259196Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.8259199Z 2025-07-17T09:05:52.8306495Z msg = Cannot scrape callname=get_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict.py line=1118. 2025-07-17T09:05:52.8306742Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.8306748Z 2025-07-17T09:05:52.8306872Z Return the model state_dict and optimizers state_dict. 2025-07-17T09:05:52.8306876Z 2025-07-17T09:05:52.8307040Z ``get_state_dict`` can process any module that is parallelized by PyTorch 2025-07-17T09:05:52.8307201Z FSDP/fully_shard, DDP/replicate, tensor_parallel/parallelize_module, and any 2025-07-17T09:05:52.8307372Z combination of these parallelisms. The main functions of ``get_state_dict`` 2025-07-17T09:05:52.8307516Z are: 1.) returning a model and optimizer state_dict that can be resharded 2025-07-17T09:05:52.8307673Z with a different number of trainers and/or different parallelisms. 2025-07-17T09:05:52.8307830Z 2.) hiding the parallelism-specific state_dict APIs. Users don't have to call 2025-07-17T09:05:52.8307910Z these APIs. 2025-07-17T09:05:52.8307999Z 3.) sanity checking the result state_dict. 2025-07-17T09:05:52.8308003Z 2025-07-17T09:05:52.8308161Z The keys of the result state dictionary are the canonical FQNs (Fully 2025-07-17T09:05:52.8308305Z Qualified Names). A canonical FQN refers to the FQN based on a parameter's 2025-07-17T09:05:52.8308459Z position in an nn.Module hierarchy. More specifically, a canonical FQN to a 2025-07-17T09:05:52.8308589Z parameter is the FQN returned by ``module.named_parameters()`` or 2025-07-17T09:05:52.8308732Z ``module.named_buffers()`` when the module is not distributed by any 2025-07-17T09:05:52.8308895Z parallelisms. Since the optimizer internally uses parameter IDs to represent 2025-07-17T09:05:52.8309045Z a parameter, there will be a conversion from the parameter IDs to the 2025-07-17T09:05:52.8309132Z canonical FQNs when calling this API. 2025-07-17T09:05:52.8309135Z 2025-07-17T09:05:52.8309286Z ``get_state_dict`` can also process a module that is not parallelized. In 2025-07-17T09:05:52.8309432Z such a case, ``get_state_dict`` only performs one function -- converting the 2025-07-17T09:05:52.8309541Z optimizer parameter IDs to the canonical FQNs. 2025-07-17T09:05:52.8309549Z 2025-07-17T09:05:52.8309615Z Example: 2025-07-17T09:05:52.8309699Z >>> # xdoctest: +SKIP 2025-07-17T09:05:52.8309768Z >>> import torch 2025-07-17T09:05:52.8309917Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-07-17T09:05:52.8310058Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2025-07-17T09:05:52.8310195Z >>> from torch.distributed.checkpoint.state_dict import get_state_dict 2025-07-17T09:05:52.8310209Z 2025-07-17T09:05:52.8310299Z >>> fsdp_model = FSDP(copy.deepcopy(model)) 2025-07-17T09:05:52.8310643Z >>> fsdp_optim = torch.optim.Adam(model.parameters(), lr=1e-3) 2025-07-17T09:05:52.8310741Z >>> ddp_model = DDP(copy.deepcopy(model)) 2025-07-17T09:05:52.8310858Z >>> ddp_optim = torch.optim.Adam(model.parameters(), lr=1e-3) 2025-07-17T09:05:52.8310862Z 2025-07-17T09:05:52.8310877Z 2025-07-17T09:05:52.8311167Z >>> ddp_state_dict, ddp_optim_state_dict = get_state_dict(ddp_model, ddp_optim) 2025-07-17T09:05:52.8311284Z >>> fsdp_state_dict, fsdp_optim_state_dict = get_state_dict( 2025-07-17T09:05:52.8311370Z ... fsdp_model, fsdp_optim 2025-07-17T09:05:52.8311437Z ... ) 2025-07-17T09:05:52.8311440Z 2025-07-17T09:05:52.8311589Z >>> # if we simply call ddp_model.state_dict() and fsdp_model.state_dict(), 2025-07-17T09:05:52.8311665Z >>> # the asserts will fail. 2025-07-17T09:05:52.8311760Z >>> assert ddp_state_dict == fsdp_state_dict 2025-07-17T09:05:52.8311859Z >>> assert ddp_optim_state == fsdp_optim_state_dict 2025-07-17T09:05:52.8311866Z 2025-07-17T09:05:52.8311871Z 2025-07-17T09:05:52.8311943Z Args: 2025-07-17T09:05:52.8312037Z model (nn.Module): the nn.Module to the model. 2025-07-17T09:05:52.8312171Z optimizers (Union[None, Optimizer, Iterable[Optimizer]]): 2025-07-17T09:05:52.8312279Z The optimizers that are used to optimize ``model``. 2025-07-17T09:05:52.8312468Z submodules (deprecated): Optional[set[nn.Module]]: only return the model parameters 2025-07-17T09:05:52.8312550Z that belong to the submodules. 2025-07-17T09:05:52.8312680Z options (StateDictOptions): the options to control how 2025-07-17T09:05:52.8312810Z model state_dict and optimizer state_dict should be returned. See 2025-07-17T09:05:52.8312907Z `StateDictOptions` for the details. 2025-07-17T09:05:52.8312910Z 2025-07-17T09:05:52.8312973Z Returns: 2025-07-17T09:05:52.8313107Z ``Tuple`` that contain model state_dict and optimizer state_dict. 2025-07-17T09:05:52.8313110Z 2025-07-17T09:05:52.8313252Z :rtype: typing.Tuple[typing.Dict[str, ValueType], OptimizerStateType] 2025-07-17T09:05:52.8313258Z 2025-07-17T09:05:52.8313420Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.8313423Z 2025-07-17T09:05:52.8335481Z msg = Cannot scrape callname=save in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=96. 2025-07-17T09:05:52.8335739Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.8335744Z 2025-07-17T09:05:52.8335851Z Save a distributed model in SPMD style. 2025-07-17T09:05:52.8335855Z 2025-07-17T09:05:52.8335990Z This function is different from ``torch.save()`` as it handles 2025-07-17T09:05:52.8336163Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2025-07-17T09:05:52.8336166Z 2025-07-17T09:05:52.8336333Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2025-07-17T09:05:52.8336450Z save will call ``state_dict`` before serialization. 2025-07-17T09:05:52.8336457Z 2025-07-17T09:05:52.8336545Z .. warning:: 2025-07-17T09:05:52.8336713Z There is no guarantees of Backwards Compatibility across PyTorch versions 2025-07-17T09:05:52.8336789Z for saved state_dicts. 2025-07-17T09:05:52.8336792Z 2025-07-17T09:05:52.8336867Z .. warning:: 2025-07-17T09:05:52.8337002Z If using the `process_group` argument, make sure that only its ranks 2025-07-17T09:05:52.8337136Z call `save_state_dict` and that all data in state_dict belong to it. 2025-07-17T09:05:52.8337144Z 2025-07-17T09:05:52.8337205Z .. note:: 2025-07-17T09:05:52.8337369Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2025-07-17T09:05:52.8337545Z the shard_group should be calling `save_state_dict` and the corresponding process 2025-07-17T09:05:52.8337626Z group needs to be passed in. 2025-07-17T09:05:52.8337638Z 2025-07-17T09:05:52.8337841Z .. note:: 2025-07-17T09:05:52.8338003Z If no process group is available, this function assumes the intention is to save the 2025-07-17T09:05:52.8338161Z state_dict in the local process. 2025-07-17T09:05:52.8338164Z 2025-07-17T09:05:52.8338226Z .. note: 2025-07-17T09:05:52.8338332Z Rank 0 is assumed to be the coordinator rank. 2025-07-17T09:05:52.8338335Z 2025-07-17T09:05:52.8338338Z 2025-07-17T09:05:52.8338399Z Args: 2025-07-17T09:05:52.8338625Z state_dict (Dict[str, Any]): The state_dict to save. 2025-07-17T09:05:52.8338718Z checkpoint_id (Union[str, os.PathLike, None]): 2025-07-17T09:05:52.8338863Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-07-17T09:05:52.8338989Z depends on the storage. It can be a path to a folder or to a file. 2025-07-17T09:05:52.8339114Z It can also be a key if the storage is a key-value store. 2025-07-17T09:05:52.8339183Z (Default: ``None``) 2025-07-17T09:05:52.8339288Z storage_writer (Optional[StorageWriter]): 2025-07-17T09:05:52.8339425Z Instance of StorageWriter used to perform writes. If this is not 2025-07-17T09:05:52.8339563Z specified, DCP will automatically infer the writer based on the 2025-07-17T09:05:52.8339686Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-07-17T09:05:52.8339773Z be raised. (Default: ``None``) 2025-07-17T09:05:52.8339855Z planner (Optional[SavePlanner]): 2025-07-17T09:05:52.8340000Z Instance of SavePlanner. If this is not specified, the default 2025-07-17T09:05:52.8340083Z planner will be used. (Default: ``None``) 2025-07-17T09:05:52.8340182Z process_group (Optional[ProcessGroup]): 2025-07-17T09:05:52.8340302Z ProcessGroup to be used for cross-rank synchronization. 2025-07-17T09:05:52.8340402Z (Default: ``None``) 2025-07-17T09:05:52.8340472Z no_dist (bool): 2025-07-17T09:05:52.8340585Z If ``True``, this function will assume the intent is to load 2025-07-17T09:05:52.8340710Z a checkpoint without using cross-rank synchronization. 2025-07-17T09:05:52.8340779Z (Default: ``False``) 2025-07-17T09:05:52.8340792Z 2025-07-17T09:05:52.8340857Z Returns: 2025-07-17T09:05:52.8340963Z Metadata: Metadata object for the saved checkpoint. 2025-07-17T09:05:52.8340966Z 2025-07-17T09:05:52.8341038Z Example: 2025-07-17T09:05:52.8341109Z >>> # xdoctest: +SKIP 2025-07-17T09:05:52.8341214Z >>> my_model = MyModule() 2025-07-17T09:05:52.8341222Z 2025-07-17T09:05:52.8341308Z >>> state_dict = {"model": my_model} 2025-07-17T09:05:52.8341311Z 2025-07-17T09:05:52.8341459Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter( 2025-07-17T09:05:52.8341537Z ... "/checkpoint/1" 2025-07-17T09:05:52.8341598Z ... ) 2025-07-17T09:05:52.8341695Z >>> torch.distributed.checkpoint.save( 2025-07-17T09:05:52.8341770Z >>> state_dict=state_dict, 2025-07-17T09:05:52.8341861Z >>> storage_writer=fs_storage_writer, 2025-07-17T09:05:52.8341923Z >>> ) 2025-07-17T09:05:52.8341925Z 2025-07-17T09:05:52.8341995Z .. note:: 2025-07-17T09:05:52.8342144Z save_state_dict uses collectives to coordinate writes across ranks. 2025-07-17T09:05:52.8342279Z For NCCL-based process groups, internal tensor representations of 2025-07-17T09:05:52.8342433Z objects must be moved to the GPU device before communication takes place. 2025-07-17T09:05:52.8342570Z In this case, the device used is given by ``torch.cuda.current_device()`` 2025-07-17T09:05:52.8342710Z and it is the user's responsibility to ensure that this is set so that 2025-07-17T09:05:52.8342832Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2025-07-17T09:05:52.8342835Z 2025-07-17T09:05:52.8342999Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.8343002Z 2025-07-17T09:05:52.8343360Z msg = Cannot scrape callname=async_save in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=221. 2025-07-17T09:05:52.8343602Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.8343818Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2025-07-17T09:05:52.8344005Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2025-07-17T09:05:52.8344008Z 2025-07-17T09:05:52.8344073Z .. warning:: 2025-07-17T09:05:52.8344300Z This feature is experimental and subject to change. 2025-07-17T09:05:52.8344399Z MUST CALL CLOSE AFTER LAST CHECKPOINT IS SAVED 2025-07-17T09:05:52.8344416Z 2025-07-17T09:05:52.8344478Z Args: 2025-07-17T09:05:52.8344584Z state_dict (Dict[str, Any]): The state_dict to save. 2025-07-17T09:05:52.8344689Z checkpoint_id (Union[str, os.PathLike, None]): 2025-07-17T09:05:52.8344823Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-07-17T09:05:52.8344960Z depends on the storage. It can be a path to a folder or to a file. 2025-07-17T09:05:52.8345071Z It can also be a key if the storage is a key-value store. 2025-07-17T09:05:52.8345155Z (Default: ``None``) 2025-07-17T09:05:52.8345243Z storage_writer (Optional[StorageWriter]): 2025-07-17T09:05:52.8345473Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2025-07-17T09:05:52.8345626Z this is not specified, DCP will automatically infer the writer based on the 2025-07-17T09:05:52.8345764Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-07-17T09:05:52.8345844Z be raised. (Default: ``None``) 2025-07-17T09:05:52.8345937Z planner (Optional[SavePlanner]): 2025-07-17T09:05:52.8346061Z Instance of SavePlanner. If this is not specified, the default 2025-07-17T09:05:52.8346157Z planner will be used. (Default: ``None``) 2025-07-17T09:05:52.8346249Z process_group (Optional[ProcessGroup]): 2025-07-17T09:05:52.8346380Z ProcessGroup to be used for cross-rank synchronization. 2025-07-17T09:05:52.8346450Z (Default: ``None``) 2025-07-17T09:05:52.8346570Z async_checkpointer_type (AsyncCheckpointerType): 2025-07-17T09:05:52.8346682Z whether to do checkpoint in separate thread or process 2025-07-17T09:05:52.8346795Z (Default: ``AsyncCheckpointerType.THREAD``) 2025-07-17T09:05:52.8346877Z async_stager (AsyncStager): 2025-07-17T09:05:52.8347050Z provides staging implementation. If storage_writer implements AsyncStager 2025-07-17T09:05:52.8347185Z and async_stager is provided, async_stager will be used for staging 2025-07-17T09:05:52.8347188Z 2025-07-17T09:05:52.8347260Z Returns: 2025-07-17T09:05:52.8347393Z Future: A future holding the resultant Metadata object from `save`. 2025-07-17T09:05:52.8347396Z 2025-07-17T09:05:52.8347467Z Example: 2025-07-17T09:05:52.8347539Z >>> # xdoctest: +SKIP 2025-07-17T09:05:52.8347611Z >>> my_model = MyModule() 2025-07-17T09:05:52.8347617Z 2025-07-17T09:05:52.8347711Z >>> state_dict = {"model": my_model} 2025-07-17T09:05:52.8347714Z 2025-07-17T09:05:52.8347860Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter( 2025-07-17T09:05:52.8347945Z ... "/checkpoint/1" 2025-07-17T09:05:52.8348009Z ... ) 2025-07-17T09:05:52.8348159Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2025-07-17T09:05:52.8348237Z >>> state_dict=state_dict, 2025-07-17T09:05:52.8348333Z >>> storage_writer=fs_storage_writer, 2025-07-17T09:05:52.8348394Z >>> ) 2025-07-17T09:05:52.8348465Z >>> 2025-07-17T09:05:52.8348537Z >>> # ... do some work ... 2025-07-17T09:05:52.8348608Z >>> 2025-07-17T09:05:52.8348684Z >>> checkpoint_future.result() 2025-07-17T09:05:52.8348687Z 2025-07-17T09:05:52.8348756Z 2025-07-17T09:05:52.8348997Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.8349060Z 2025-07-17T09:05:52.8369671Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_loader.py line=62. 2025-07-17T09:05:52.8369914Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.8369920Z 2025-07-17T09:05:52.8370368Z Load a checkpoint into a distributed state dict in SPMD style. 2025-07-17T09:05:52.8370373Z 2025-07-17T09:05:52.8370524Z Each rank must have the same keys in their ``state_dict`` provided to this 2025-07-17T09:05:52.8370667Z API. Mismatched keys may result in hangs or errors. If unsure, you can use 2025-07-17T09:05:52.8370815Z the ``utils._assert_same_keys`` API to check (but may incur communication 2025-07-17T09:05:52.8370886Z costs). 2025-07-17T09:05:52.8370891Z 2025-07-17T09:05:52.8371026Z Each rank will try to read the least amount of data necessary 2025-07-17T09:05:52.8371174Z to fulfill the requested `state_dict`. When loading :class:`ShardedTensor` 2025-07-17T09:05:52.8371330Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2025-07-17T09:05:52.8371342Z 2025-07-17T09:05:52.8371502Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2025-07-17T09:05:52.8371669Z load will first call ``state_dict`` before attempting deserialization, followed by 2025-07-17T09:05:52.8371787Z ``load_state_dict`` once the deserialization is complete. 2025-07-17T09:05:52.8371943Z For each non-``Stateful`` object, load will deserialize the object, and then replace 2025-07-17T09:05:52.8372054Z it in the ``state_dict`` with the deserialized object. 2025-07-17T09:05:52.8372057Z 2025-07-17T09:05:52.8372131Z .. warning:: 2025-07-17T09:05:52.8372250Z All tensors in ``state_dict`` must be allocated on their 2025-07-17T09:05:52.8372359Z destination device *prior to* calling this function. 2025-07-17T09:05:52.8372366Z 2025-07-17T09:05:52.8372517Z All non-tensor data is loaded using `torch.load()` and modified in place 2025-07-17T09:05:52.8372586Z on state_dict. 2025-07-17T09:05:52.8372590Z 2025-07-17T09:05:52.8372662Z .. warning:: 2025-07-17T09:05:52.8372789Z Users must call `load_state_dict` on the root module to ensure load 2025-07-17T09:05:52.8372918Z pos-processing and non-tensor data properly propagates. 2025-07-17T09:05:52.8372921Z 2025-07-17T09:05:52.8372987Z .. note: 2025-07-17T09:05:52.8373136Z If no process group is initialized, this function will assume the intent 2025-07-17T09:05:52.8373272Z is to load a checkpoint into the local process. This can be useful in the 2025-07-17T09:05:52.8373429Z case of local inference, and when using regular Tensors (as opposed to DTensor 2025-07-17T09:05:52.8373493Z or ShardedTensor) 2025-07-17T09:05:52.8373496Z 2025-07-17T09:05:52.8373569Z .. note: 2025-07-17T09:05:52.8373655Z Rank 0 is assumed to be the coordinator rank. 2025-07-17T09:05:52.8373660Z 2025-07-17T09:05:52.8373729Z Args: 2025-07-17T09:05:52.8373866Z state_dict (Dict[str, Any]): The state_dict to load the checkpoint into. 2025-07-17T09:05:52.8373973Z checkpoint_id (Union[str, os.PathLike, None]): 2025-07-17T09:05:52.8374104Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-07-17T09:05:52.8374244Z depends on the storage. It can be a path to a folder or to a file. 2025-07-17T09:05:52.8374358Z It can also be a key if the storage is a key-value store. 2025-07-17T09:05:52.8374446Z (Default: ``None``) 2025-07-17T09:05:52.8374540Z storage_reader (Optional[StorageReader]): 2025-07-17T09:05:52.8374681Z Instance of StorageWriter used to perform reads. If this is not 2025-07-17T09:05:52.8374803Z specified, DCP will automatically infer the reader based on the 2025-07-17T09:05:52.8374934Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-07-17T09:05:52.8375012Z be raised. (Default: ``None``) 2025-07-17T09:05:52.8375166Z planner (Optional[LoadPlanner]): 2025-07-17T09:05:52.8375347Z Instance of LoadPlanner. If this is not specified, the default 2025-07-17T09:05:52.8375451Z planner will be used. (Default: ``None``) 2025-07-17T09:05:52.8375536Z process_group (Optional[ProcessGroup]): 2025-07-17T09:05:52.8375655Z ProcessGroup to be used for cross-rank synchronization. 2025-07-17T09:05:52.8375842Z (Default: ``None``) 2025-07-17T09:05:52.8375980Z no_dist (bool): If ``True``, this function will assume the intent is to load 2025-07-17T09:05:52.8376147Z a checkpoint without using cross-rank synchronization. (Default: ``False``) 2025-07-17T09:05:52.8376211Z Returns: 2025-07-17T09:05:52.8376283Z None. 2025-07-17T09:05:52.8376287Z 2025-07-17T09:05:52.8376353Z Examples 2025-07-17T09:05:52.8376439Z >>> # xdoctest: +SKIP 2025-07-17T09:05:52.8376513Z >>> my_model = MyModule() 2025-07-17T09:05:52.8376631Z >>> optimizer = Adagrad(my_model.parameters()) 2025-07-17T09:05:52.8376718Z >>> model_state_dict = my_model.state_dict() 2025-07-17T09:05:52.8376881Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader( 2025-07-17T09:05:52.8376950Z ... "/checkpoint/1" 2025-07-17T09:05:52.8377019Z ... ) 2025-07-17T09:05:52.8377022Z 2025-07-17T09:05:52.8377126Z >>> torch.distributed.checkpoint.load_state_dict( 2025-07-17T09:05:52.8377216Z >>> state_dict=model_state_dict, 2025-07-17T09:05:52.8377297Z >>> storage_reader=fs_storage_reader, 2025-07-17T09:05:52.8377364Z >>> ) 2025-07-17T09:05:52.8377367Z 2025-07-17T09:05:52.8377491Z >>> # module.load_state_dict() function might have customized steps 2025-07-17T09:05:52.8377583Z >>> # to flush the state_dict, must call it to 2025-07-17T09:05:52.8377659Z >>> # ensure correct behavior. 2025-07-17T09:05:52.8377753Z >>> my_model.load_state_dict(model_state_dict) 2025-07-17T09:05:52.8377756Z 2025-07-17T09:05:52.8377819Z .. note:: 2025-07-17T09:05:52.8377983Z load_state_dict uses collectives to coordinate reads across ranks. 2025-07-17T09:05:52.8378122Z For NCCL-based process groups, internal tensor representations of 2025-07-17T09:05:52.8378273Z objects must be moved to the GPU device before communication takes place. 2025-07-17T09:05:52.8378408Z In this case, the device used is given by ``torch.cuda.current_device()`` 2025-07-17T09:05:52.8378554Z and it is the user's responsibility to ensure that this is set so that each 2025-07-17T09:05:52.8378683Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2025-07-17T09:05:52.8378686Z 2025-07-17T09:05:52.8378841Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.8378853Z 2025-07-17T09:05:52.8395991Z msg = Cannot scrape callname=BroadcastingTorchSaveReader in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/format_utils.py line=40. 2025-07-17T09:05:52.8396220Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.8396228Z 2025-07-17T09:05:52.8396415Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2025-07-17T09:05:52.8396579Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2025-07-17T09:05:52.8396583Z 2025-07-17T09:05:52.8396696Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2025-07-17T09:05:52.8396700Z 2025-07-17T09:05:52.8396787Z .. warning:: 2025-07-17T09:05:52.8396908Z Current implementation only supports loading Tensors. 2025-07-17T09:05:52.8396912Z 2025-07-17T09:05:52.8397000Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T09:05:52.8397073Z >>> sd = {"mode": model} 2025-07-17T09:05:52.8397149Z >>> dcp.load( 2025-07-17T09:05:52.8397208Z >>> sd, 2025-07-17T09:05:52.8397317Z >>> storage_reader=BroadcastingTorchSaveReader(), 2025-07-17T09:05:52.8397396Z >>> planner=DynamicMetaLoadPlanner(), 2025-07-17T09:05:52.8397632Z >>> checkpoint_id="path_to_model.pt" 2025-07-17T09:05:52.8397760Z >>> ) 2025-07-17T09:05:52.8397764Z 2025-07-17T09:05:52.8397936Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.8397939Z 2025-07-17T09:05:52.8398544Z msg = Cannot scrape callname=DynamicMetaLoadPlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/format_utils.py line=151. 2025-07-17T09:05:52.8398862Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.8398879Z 2025-07-17T09:05:52.8399106Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2025-07-17T09:05:52.8399308Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2025-07-17T09:05:52.8399390Z metadata file, like Torch Save files. 2025-07-17T09:05:52.8399393Z 2025-07-17T09:05:52.8399521Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2025-07-17T09:05:52.8399528Z 2025-07-17T09:05:52.8399595Z .. warning:: 2025-07-17T09:05:52.8399718Z Current implementation only supports loading Tensors. 2025-07-17T09:05:52.8399721Z 2025-07-17T09:05:52.8399798Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T09:05:52.8399876Z >>> sd = {"mode": model} 2025-07-17T09:05:52.8399940Z >>> dcp.load( 2025-07-17T09:05:52.8400008Z >>> sd, 2025-07-17T09:05:52.8400116Z >>> storage_reader=BroadcastingTorchSaveReader(), 2025-07-17T09:05:52.8400205Z >>> planner=DynamicMetaLoadPlanner(), 2025-07-17T09:05:52.8400281Z >>> checkpoint_id="path_to_model.pt" 2025-07-17T09:05:52.8400348Z >>> ) 2025-07-17T09:05:52.8400352Z 2025-07-17T09:05:52.8400508Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.8400512Z 2025-07-17T09:05:52.8629606Z msg = Cannot scrape callname=init_from_local_shards in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py line=361. 2025-07-17T09:05:52.8629853Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.8629867Z 2025-07-17T09:05:52.8630037Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2025-07-17T09:05:52.8630138Z Needs to be called on all ranks in an SPMD fashion. 2025-07-17T09:05:52.8630142Z 2025-07-17T09:05:52.8630213Z Args: 2025-07-17T09:05:52.8630396Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2025-07-17T09:05:52.8630522Z of shards that represent the local shards on this rank. 2025-07-17T09:05:52.8630666Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2025-07-17T09:05:52.8630759Z shape of the overall sharded tensor. 2025-07-17T09:05:52.8630763Z 2025-07-17T09:05:52.8630828Z Keyword args: 2025-07-17T09:05:52.8630998Z process_group (ProcessGroup, optional): The process group to work on. If None, 2025-07-17T09:05:52.8631088Z the default process group will be used. 2025-07-17T09:05:52.8631212Z init_rrefs (bool, optional): Whether or not to initialize 2025-07-17T09:05:52.8631349Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2025-07-17T09:05:52.8631482Z Need to initialize the RPC Framework if specified as ``True``. 2025-07-17T09:05:52.8631553Z Default: ``False``. 2025-07-17T09:05:52.8631557Z 2025-07-17T09:05:52.8631627Z Returns: 2025-07-17T09:05:52.8631733Z A :class:`ShardedTensor` object handle on this rank 2025-07-17T09:05:52.8631737Z 2025-07-17T09:05:52.8631739Z 2025-07-17T09:05:52.8631808Z Examples: 2025-07-17T09:05:52.8631996Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2025-07-17T09:05:52.8632114Z each shard have a (5, 5) local tensor, we can do it like below: 2025-07-17T09:05:52.8632117Z 2025-07-17T09:05:52.8632184Z on rank 0: 2025-07-17T09:05:52.8632267Z >>> # xdoctest: +SKIP("not distributed") 2025-07-17T09:05:52.8632352Z >>> local_shard_metadata = ShardMetadata( 2025-07-17T09:05:52.8632843Z >>> shard_offsets=[0, 0], 2025-07-17T09:05:52.8632905Z >>> shard_lengths=[5, 5], 2025-07-17T09:05:52.8632991Z >>> placement="rank:0/cuda:0" 2025-07-17T09:05:52.8633050Z >>> ) 2025-07-17T09:05:52.8633188Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2025-07-17T09:05:52.8633315Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2025-07-17T09:05:52.8633487Z 2025-07-17T09:05:52.8633561Z on rank 1: 2025-07-17T09:05:52.8633643Z >>> # xdoctest: +SKIP("not distributed") 2025-07-17T09:05:52.8633736Z >>> local_shard_metadata = ShardMetadata( 2025-07-17T09:05:52.8633806Z >>> shard_offsets=[5, 0], 2025-07-17T09:05:52.8633880Z >>> shard_lengths=[5, 5], 2025-07-17T09:05:52.8633953Z >>> placement="rank:1/cuda:1" 2025-07-17T09:05:52.8634019Z >>> ) 2025-07-17T09:05:52.8634135Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2025-07-17T09:05:52.8634264Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2025-07-17T09:05:52.8634271Z 2025-07-17T09:05:52.8634427Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.8634430Z 2025-07-17T09:05:52.8653227Z msg = Cannot scrape callname=ShardingPlan in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharding_plan/api.py line=12. 2025-07-17T09:05:52.8653446Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.8653452Z 2025-07-17T09:05:52.8653619Z Representation of a sharding plan, describes how to shard a module 2025-07-17T09:05:52.8653794Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2025-07-17T09:05:52.8653985Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2025-07-17T09:05:52.8654145Z layout of a module with a spec, and when to convert back to data parallel fashion. 2025-07-17T09:05:52.8654152Z 2025-07-17T09:05:52.8654223Z Args: 2025-07-17T09:05:52.8654393Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2025-07-17T09:05:52.8654515Z :class:`torch.distributed._shard.sharder.Sharder`]): 2025-07-17T09:05:52.8654679Z a dict describes how to shard a module, there're currently two ways to shard a module: 2025-07-17T09:05:52.8654850Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2025-07-17T09:05:52.8654942Z a parameter to a `ShardingSpec`. 2025-07-17T09:05:52.8655119Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2025-07-17T09:05:52.8655196Z to a `Sharder` object. 2025-07-17T09:05:52.8655409Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2025-07-17T09:05:52.8655569Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2025-07-17T09:05:52.8655724Z keyed by the name of module to ShardingSpec("" in key means the root module). 2025-07-17T09:05:52.8655794Z Default: `None` 2025-07-17T09:05:52.8655956Z return_local_tensor (List[str], optional): a list of string, each element enables 2025-07-17T09:05:52.8656101Z a module's sharded output to be returned as a Tensor from its local shards to 2025-07-17T09:05:52.8656268Z ensure further processing in a data parallel fashion. ("" in list means the 2025-07-17T09:05:52.8656335Z root module). 2025-07-17T09:05:52.8656407Z Default: None 2025-07-17T09:05:52.8656471Z Example: 2025-07-17T09:05:52.8656658Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2025-07-17T09:05:52.8656825Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2025-07-17T09:05:52.8656829Z 2025-07-17T09:05:52.8656949Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2025-07-17T09:05:52.8657154Z >>> class MyModule(nn.Module): 2025-07-17T09:05:52.8657346Z >>> def __init__(self) -> None: 2025-07-17T09:05:52.8657420Z >>> super().__init__() 2025-07-17T09:05:52.8657497Z >>> self.fc1 = nn.Linear() 2025-07-17T09:05:52.8657578Z >>> self.gelu = nn.GELU() 2025-07-17T09:05:52.8657643Z >>> self.fc2 = nn.Linear() 2025-07-17T09:05:52.8657833Z >>> self.relu = nn.Linear() 2025-07-17T09:05:52.8657894Z >>> 2025-07-17T09:05:52.8657979Z >>> def forward(self, input): 2025-07-17T09:05:52.8658099Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2025-07-17T09:05:52.8658103Z 2025-07-17T09:05:52.8658106Z 2025-07-17T09:05:52.8658209Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2025-07-17T09:05:52.8658284Z >>> sharding_plan = ShardingPlan( 2025-07-17T09:05:52.8658357Z >>> plan={ 2025-07-17T09:05:52.8658427Z >>> "fc1.weight": spec1, 2025-07-17T09:05:52.8658512Z >>> "fc2.weight": spec2 2025-07-17T09:05:52.8658574Z >>> }, 2025-07-17T09:05:52.8658652Z >>> output_plan={ 2025-07-17T09:05:52.8658722Z >>> "fc2": output_spec 2025-07-17T09:05:52.8658789Z >>> }, 2025-07-17T09:05:52.8658863Z >>> return_local_tensor=["fc2"] 2025-07-17T09:05:52.8658933Z >>> ) 2025-07-17T09:05:52.8658936Z 2025-07-17T09:05:52.8659095Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.8659099Z 2025-07-17T09:05:52.8716036Z msg = Cannot scrape callname=ShardedTensor._init_from_local_tensor in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=835. 2025-07-17T09:05:52.8716246Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.8716251Z 2025-07-17T09:05:52.8716435Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2025-07-17T09:05:52.8716520Z size and sharding spec on each rank. 2025-07-17T09:05:52.8716532Z 2025-07-17T09:05:52.8716604Z Args: 2025-07-17T09:05:52.8716749Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2025-07-17T09:05:52.8716920Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2025-07-17T09:05:52.8717033Z The specification describing how to shard the Tensor. 2025-07-17T09:05:52.8717157Z global_size (Sequence[int]): Size of the sharded tensor. 2025-07-17T09:05:52.8717312Z process_group (ProcessGroup, optional): The process group to aggregate on. 2025-07-17T09:05:52.8717387Z Default: None 2025-07-17T09:05:52.8717497Z init_rrefs (bool, optional): Whether or not to initialize 2025-07-17T09:05:52.8717639Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2025-07-17T09:05:52.8717765Z Need to initialize the RPC Framework if specified as ``True``. 2025-07-17T09:05:52.8717840Z Default: ``False``. 2025-07-17T09:05:52.8717846Z 2025-07-17T09:05:52.8717905Z Returns: 2025-07-17T09:05:52.8718068Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2025-07-17T09:05:52.8718148Z tensor stored in the current rank. 2025-07-17T09:05:52.8718151Z 2025-07-17T09:05:52.8718226Z Examples: 2025-07-17T09:05:52.8718300Z >>> # xdoctest: +SKIP 2025-07-17T09:05:52.8718407Z >>> # All tensors below are of torch.int64 type. 2025-07-17T09:05:52.8718493Z >>> # We have 2 process groups, 2 ranks. 2025-07-17T09:05:52.8718608Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2025-07-17T09:05:52.8718752Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2025-07-17T09:05:52.8718820Z >>> local_tensor 2025-07-17T09:05:52.8718902Z tensor([[1, 2, 3, 4]]) # Rank 0 2025-07-17T09:05:52.8718967Z tensor([[3, 4, 5, 6]]) # Rank 1 2025-07-17T09:05:52.8719045Z >>> sharding_dim = 0 2025-07-17T09:05:52.8719131Z >>> sharding_spec = ChunkShardingSpec( 2025-07-17T09:05:52.8719417Z dim=sharding_dim, 2025-07-17T09:05:52.8719562Z placements=[ 2025-07-17T09:05:52.8719642Z "rank:0/cuda:0", 2025-07-17T09:05:52.8719708Z "rank:1/cuda:1", 2025-07-17T09:05:52.8719776Z ], 2025-07-17T09:05:52.8719833Z ) 2025-07-17T09:05:52.8719934Z >>> st = ShardedTensor._init_from_local_tensor( 2025-07-17T09:05:52.8720018Z ... local_tensor, sharding_spec, [2, 4] 2025-07-17T09:05:52.8720208Z ... ) 2025-07-17T09:05:52.8720281Z >>> st 2025-07-17T09:05:52.8720348Z ShardedTensor( 2025-07-17T09:05:52.8720434Z ShardedTensorMetadata( 2025-07-17T09:05:52.8720503Z shards_metadata=[ 2025-07-17T09:05:52.8720683Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2025-07-17T09:05:52.8720832Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2025-07-17T09:05:52.8720901Z ], 2025-07-17T09:05:52.8720974Z size=torch.Size([2, 4]) 2025-07-17T09:05:52.8721051Z ) 2025-07-17T09:05:52.8721118Z >>> st.local_tensor() 2025-07-17T09:05:52.8721198Z tensor([1, 2, 3, 4]) # Rank 0 2025-07-17T09:05:52.8721265Z tensor([3, 4, 5, 6]) # Rank 1 2025-07-17T09:05:52.8721270Z 2025-07-17T09:05:52.8721445Z Warning: This API is experimental and subject to change. It lacks of a fully across 2025-07-17T09:05:52.8721599Z rank validations, and we only validate the local shard on the current rank. 2025-07-17T09:05:52.8721798Z We fully rely on the user to ensure local tensor is sharded based on the 2025-07-17T09:05:52.8721864Z sharding spec. 2025-07-17T09:05:52.8721867Z 2025-07-17T09:05:52.8722030Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.8722033Z 2025-07-17T09:05:52.8722452Z msg = Cannot scrape callname=ShardedTensor.reshard in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=1076. 2025-07-17T09:05:52.8722638Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.8722644Z 2025-07-17T09:05:52.8722802Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2025-07-17T09:05:52.8722879Z single local shard. 2025-07-17T09:05:52.8722882Z 2025-07-17T09:05:52.8723023Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2025-07-17T09:05:52.8723182Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2025-07-17T09:05:52.8723259Z we swap local shards directly. 2025-07-17T09:05:52.8723426Z For more generic cases, we merge different shards across different ranks and split 2025-07-17T09:05:52.8723572Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2025-07-17T09:05:52.8723575Z 2025-07-17T09:05:52.8723645Z Args: 2025-07-17T09:05:52.8723820Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2025-07-17T09:05:52.8723943Z specification describing how the tensor is sharded. 2025-07-17T09:05:52.8723947Z 2025-07-17T09:05:52.8724005Z Returns: 2025-07-17T09:05:52.8724146Z A :class:`ShardedTensor` object whose local shards are resharded. 2025-07-17T09:05:52.8724150Z 2025-07-17T09:05:52.8724211Z Examples: 2025-07-17T09:05:52.8724291Z >>> # xdoctest: +SKIP 2025-07-17T09:05:52.8724375Z >>> # We have 2 process groups, 2 ranks. 2025-07-17T09:05:52.8724502Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2025-07-17T09:05:52.8724583Z >>> tensor = torch.stack([tensor, tensor]) 2025-07-17T09:05:52.8724658Z >>> tensor 2025-07-17T09:05:52.8724740Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2025-07-17T09:05:52.8724821Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2025-07-17T09:05:52.8724893Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2025-07-17T09:05:52.8724983Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2025-07-17T09:05:52.8725123Z >>> sharding_dim = 0 2025-07-17T09:05:52.8725209Z >>> spec = ChunkShardingSpec( 2025-07-17T09:05:52.8725332Z dim=sharding_dim, 2025-07-17T09:05:52.8725406Z placements=[ 2025-07-17T09:05:52.8725478Z "rank:0/cuda:0", 2025-07-17T09:05:52.8725540Z "rank:1/cuda:1", 2025-07-17T09:05:52.8725615Z "rank:2/cuda:2", 2025-07-17T09:05:52.8725674Z "rank:3/cuda:3", 2025-07-17T09:05:52.8725851Z ], 2025-07-17T09:05:52.8725916Z ) 2025-07-17T09:05:52.8725994Z >>> current_offsets = [0] * 2 2025-07-17T09:05:52.8726069Z >>> current_offsets[0] = rank * 2 2025-07-17T09:05:52.8726161Z >>> shard_metadata = ShardMetadata( 2025-07-17T09:05:52.8726266Z shard_offsets=copy.deepcopy(current_offsets), 2025-07-17T09:05:52.8726350Z shard_sizes=tensor.size(), 2025-07-17T09:05:52.8726436Z placement=spec.placements[rank], 2025-07-17T09:05:52.8726502Z ) 2025-07-17T09:05:52.8726569Z >>> local_shards = [ 2025-07-17T09:05:52.8726638Z Shard( 2025-07-17T09:05:52.8726712Z tensor=tensor, 2025-07-17T09:05:52.8726792Z metadata=shard_metadata, 2025-07-17T09:05:52.8726861Z ) 2025-07-17T09:05:52.8726914Z ] 2025-07-17T09:05:52.8727069Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2025-07-17T09:05:52.8727134Z >>> sharding_dim = 1 2025-07-17T09:05:52.8727233Z >>> resharding_spec = ChunkShardingSpec( 2025-07-17T09:05:52.8727303Z dim=sharding_dim, 2025-07-17T09:05:52.8727380Z placements=[ 2025-07-17T09:05:52.8727447Z "rank:0/cuda:0", 2025-07-17T09:05:52.8727518Z "rank:1/cuda:1", 2025-07-17T09:05:52.8727581Z "rank:2/cuda:2", 2025-07-17T09:05:52.8727653Z "rank:3/cuda:3", 2025-07-17T09:05:52.8727713Z ], 2025-07-17T09:05:52.8727772Z ) 2025-07-17T09:05:52.8727861Z >>> st.reshard(resharding_spec) 2025-07-17T09:05:52.8727942Z >>> tensor = st.local_shards()[0].tensor 2025-07-17T09:05:52.8728012Z >>> tensor 2025-07-17T09:05:52.8728106Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2025-07-17T09:05:52.8728196Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2025-07-17T09:05:52.8728282Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2025-07-17T09:05:52.8728386Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2025-07-17T09:05:52.8728389Z 2025-07-17T09:05:52.8728544Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.8728548Z 2025-07-17T09:05:52.8968889Z msg = Cannot scrape callname=HierarchicalModelAverager in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/model_averaging/hierarchical_model_averager.py line=19. 2025-07-17T09:05:52.8969120Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.8969142Z 2025-07-17T09:05:52.8969376Z Runs hierarchical model averaging (`hierarchical SGD `_). 2025-07-17T09:05:52.8969382Z 2025-07-17T09:05:52.8969581Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2025-07-17T09:05:52.8969716Z by using different periods concurrently after the warm-up stage. 2025-07-17T09:05:52.8969982Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2025-07-17T09:05:52.8970185Z that supports `post-local SGD `_, which essentially only supports 2025-07-17T09:05:52.8970382Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2025-07-17T09:05:52.8970597Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2025-07-17T09:05:52.8970785Z Similarly, the process groups within this class do not have such an intra-machine process 2025-07-17T09:05:52.8971241Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2025-07-17T09:05:52.8971341Z 2025-07-17T09:05:52.8971413Z Args: 2025-07-17T09:05:52.8971580Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2025-07-17T09:05:52.8971726Z process group size, used for initializing process groups of 2025-07-17T09:05:52.8972031Z different sizes in a hierarchy to average parameters concurrently. 2025-07-17T09:05:52.8972177Z Particularly, at each iteration, there will be at most a single 2025-07-17T09:05:52.8972322Z process group that runs averaging -- the period of such group should 2025-07-17T09:05:52.8972465Z have the largest period which the current step can be divided by. 2025-07-17T09:05:52.8972581Z For example, if the dict has three keys: 2, 4, and 8, 2025-07-17T09:05:52.8972724Z then this means totally three process groups will be created to 2025-07-17T09:05:52.8972857Z average parameters every 2, 4, and 8 iterations, respectively. 2025-07-17T09:05:52.8972987Z At the 4th iteration, only the second process group will run 2025-07-17T09:05:52.8973099Z averaging, because the first process group should be a 2025-07-17T09:05:52.8973251Z subset of the second process group, and no need to execute the first 2025-07-17T09:05:52.8973340Z process group redundantly. 2025-07-17T09:05:52.8973472Z On the other hand, the third process group can only be triggered 2025-07-17T09:05:52.8973609Z every 8 iterations, so it will not be triggered at the 4th iteration. 2025-07-17T09:05:52.8973802Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2025-07-17T09:05:52.8974066Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2025-07-17T09:05:52.8974188Z If ``None``, the default process group, which is created 2025-07-17T09:05:52.8974321Z by :func:`torch.distributed.init_process_group`, will be used. 2025-07-17T09:05:52.8974423Z (default: ``None``) 2025-07-17T09:05:52.8974428Z 2025-07-17T09:05:52.8974505Z Example:: 2025-07-17T09:05:52.8974600Z >>> # xdoctest: +SKIP('undefined rank') 2025-07-17T09:05:52.8974679Z >>> from collections import OrderedDict 2025-07-17T09:05:52.8974752Z >>> import torch 2025-07-17T09:05:52.8974833Z >>> import torch.distributed as dist 2025-07-17T09:05:52.8975009Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2025-07-17T09:05:52.8975085Z >>> PostLocalSGDState, 2025-07-17T09:05:52.8975170Z >>> post_localSGD_hook, 2025-07-17T09:05:52.8975237Z >>> ) 2025-07-17T09:05:52.8975455Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2025-07-17T09:05:52.8975541Z >>> import torch.nn as nn 2025-07-17T09:05:52.8975602Z >>> 2025-07-17T09:05:52.8975729Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2025-07-17T09:05:52.8975810Z >>> torch.cuda.set_device(rank) 2025-07-17T09:05:52.8975914Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2025-07-17T09:05:52.8976016Z >>> model = nn.parallel.DistributedDataParallel( 2025-07-17T09:05:52.8976122Z >>> module, device_ids=[rank], output_device=rank 2025-07-17T09:05:52.8976182Z >>> ) 2025-07-17T09:05:52.8976289Z >>> # Register a post-localSGD communication hook. 2025-07-17T09:05:52.8976453Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2025-07-17T09:05:52.8976692Z >>> subgroup, _ = dist.new_subgroups() 2025-07-17T09:05:52.8976938Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2025-07-17T09:05:52.8977052Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-07-17T09:05:52.8977111Z >>> 2025-07-17T09:05:52.8977289Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2025-07-17T09:05:52.8977478Z >>> # the 16 processes every 16 iterations. 2025-07-17T09:05:52.8977608Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2025-07-17T09:05:52.8977755Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2025-07-17T09:05:52.8977947Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-07-17T09:05:52.8978102Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2025-07-17T09:05:52.8978208Z >>> # After 100 steps, run model averaging at two levels. 2025-07-17T09:05:52.8978288Z >>> for step in range(0, 200): 2025-07-17T09:05:52.8978362Z >>> optimizer.zero_grad() 2025-07-17T09:05:52.8978448Z >>> loss = loss_fn(output, labels) 2025-07-17T09:05:52.8978517Z >>> loss.backward() 2025-07-17T09:05:52.8978602Z >>> optimizer.step() 2025-07-17T09:05:52.8978703Z >>> # Average parameters after ``optimizer.step()``. 2025-07-17T09:05:52.8978887Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2025-07-17T09:05:52.8978999Z >>> averager.average_parameters(model.parameters()) 2025-07-17T09:05:52.8979003Z 2025-07-17T09:05:52.8979081Z .. warning :: 2025-07-17T09:05:52.8979237Z The last group size in the dict must be the size of the provided ``process_group``, 2025-07-17T09:05:52.8979398Z which indicates model averaging at the highest level of the hierarchy. 2025-07-17T09:05:52.8979578Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2025-07-17T09:05:52.8979585Z 2025-07-17T09:05:52.8979657Z .. warning :: 2025-07-17T09:05:52.8979802Z `HierarchicalModelAverager` is experimental and subject to change. 2025-07-17T09:05:52.8979805Z 2025-07-17T09:05:52.8979971Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.8979974Z 2025-07-17T09:05:52.8980463Z msg = Cannot scrape callname=PeriodicModelAverager in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/model_averaging/averagers.py line=38. 2025-07-17T09:05:52.8980648Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.8980664Z 2025-07-17T09:05:52.8980794Z Averages parameters periodically after the warm-up stage. 2025-07-17T09:05:52.8980797Z 2025-07-17T09:05:52.8980973Z This can be used for running `post-local SGD `_, 2025-07-17T09:05:52.8981110Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2025-07-17T09:05:52.8981264Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2025-07-17T09:05:52.8981267Z 2025-07-17T09:05:52.8981340Z Args: 2025-07-17T09:05:52.8981465Z period (int): The number of steps per model averaging. 2025-07-17T09:05:52.8981641Z Usually the period should be greater than ``1`` to reduce the communication cost. 2025-07-17T09:05:52.8981733Z Otherwise, only DDP needs to be used. 2025-07-17T09:05:52.8981874Z warmup_steps (int): The number of warm-up steps. During this stage, 2025-07-17T09:05:52.8981959Z model averaging is skipped. 2025-07-17T09:05:52.8982089Z process_group: The process group to be used for all-reduce. 2025-07-17T09:05:52.8982186Z If ``None``, the default process group, which 2025-07-17T09:05:52.8982310Z is created by :func:`torch.distributed.init_process_group`, 2025-07-17T09:05:52.8982460Z will be used. (default: ``None``) 2025-07-17T09:05:52.8982514Z 2025-07-17T09:05:52.8982589Z Example:: 2025-07-17T09:05:52.8982592Z 2025-07-17T09:05:52.8982677Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T09:05:52.8982754Z >>> import torch 2025-07-17T09:05:52.8982836Z >>> import torch.distributed as dist 2025-07-17T09:05:52.8983026Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2025-07-17T09:05:52.8983301Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2025-07-17T09:05:52.8983376Z >>> import torch.nn as nn 2025-07-17T09:05:52.8983449Z >>> 2025-07-17T09:05:52.8983563Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2025-07-17T09:05:52.8983650Z >>> torch.cuda.set_device(rank) 2025-07-17T09:05:52.8983738Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2025-07-17T09:05:52.8983850Z >>> model = nn.parallel.DistributedDataParallel( 2025-07-17T09:05:52.8983950Z >>> module, device_ids=[rank], output_device=rank 2025-07-17T09:05:52.8984018Z >>> ) 2025-07-17T09:05:52.8984118Z >>> # Register a post-localSGD communication hook. 2025-07-17T09:05:52.8984305Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2025-07-17T09:05:52.8984410Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-07-17T09:05:52.8984476Z >>> 2025-07-17T09:05:52.8984636Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2025-07-17T09:05:52.8984747Z >>> # After 100 steps, run model averaging every 4 steps. 2025-07-17T09:05:52.8984934Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-07-17T09:05:52.8985091Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2025-07-17T09:05:52.8985164Z >>> for step in range(0, 200): 2025-07-17T09:05:52.8985250Z >>> optimizer.zero_grad() 2025-07-17T09:05:52.8985431Z >>> loss = loss_fn(output, labels) 2025-07-17T09:05:52.8985512Z >>> loss.backward() 2025-07-17T09:05:52.8985584Z >>> optimizer.step() 2025-07-17T09:05:52.8985703Z >>> # Will average model parameters globally every 4 steps. Thus, 2025-07-17T09:05:52.8985840Z >>> # inter-node communication only occurs every 4 iterations after 2025-07-17T09:05:52.8985925Z >>> # the initial ``warmup_steps`` period. 2025-07-17T09:05:52.8986039Z >>> averager.average_parameters(model.parameters()) 2025-07-17T09:05:52.8986043Z 2025-07-17T09:05:52.8986192Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.8986195Z 2025-07-17T09:05:52.9058233Z msg = Cannot scrape callname=powerSGD_hook in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py line=342. 2025-07-17T09:05:52.9058458Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.9058483Z 2025-07-17T09:05:52.9058575Z Implement PowerSGD algorithm. 2025-07-17T09:05:52.9058579Z 2025-07-17T09:05:52.9058738Z This DDP communication hook implements PowerSGD gradient compression 2025-07-17T09:05:52.9058894Z algorithm described in the `paper `_. 2025-07-17T09:05:52.9059054Z Once gradient tensors are aggregated across all workers, this hook applies 2025-07-17T09:05:52.9059134Z compression as follows: 2025-07-17T09:05:52.9059139Z 2025-07-17T09:05:52.9059412Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2025-07-17T09:05:52.9059415Z 2025-07-17T09:05:52.9059669Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2025-07-17T09:05:52.9059672Z 2025-07-17T09:05:52.9059920Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2025-07-17T09:05:52.9060108Z 2025-07-17T09:05:52.9060262Z 2. Handles uncompressed tensors: 2025-07-17T09:05:52.9060264Z 2025-07-17T09:05:52.9060560Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2025-07-17T09:05:52.9060563Z 2025-07-17T09:05:52.9060900Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2025-07-17T09:05:52.9060916Z 2025-07-17T09:05:52.9061064Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2025-07-17T09:05:52.9061067Z 2025-07-17T09:05:52.9061223Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2025-07-17T09:05:52.9061427Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2025-07-17T09:05:52.9061431Z 2025-07-17T09:05:52.9061538Z 3.2. Computes each P in Ps, which is equal to MQ; 2025-07-17T09:05:52.9061546Z 2025-07-17T09:05:52.9061620Z 3.3. Allreduces Ps as a batch; 2025-07-17T09:05:52.9061626Z 2025-07-17T09:05:52.9061736Z 3.4. Orthogonalizes each P in Ps; 2025-07-17T09:05:52.9061739Z 2025-07-17T09:05:52.9061864Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2025-07-17T09:05:52.9061867Z 2025-07-17T09:05:52.9061945Z 3.6. Allreduces Qs as a batch; 2025-07-17T09:05:52.9061948Z 2025-07-17T09:05:52.9062131Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2025-07-17T09:05:52.9062135Z 2025-07-17T09:05:52.9062386Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2025-07-17T09:05:52.9062556Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2025-07-17T09:05:52.9062812Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2025-07-17T09:05:52.9062817Z 2025-07-17T09:05:52.9062879Z Args: 2025-07-17T09:05:52.9063144Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2025-07-17T09:05:52.9063353Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2025-07-17T09:05:52.9063443Z and ``min_compression_rate``. 2025-07-17T09:05:52.9063688Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2025-07-17T09:05:52.9063847Z Note that since DDP comm hook only supports single process single device mode, 2025-07-17T09:05:52.9063947Z only exactly one tensor is stored in this bucket. 2025-07-17T09:05:52.9063950Z 2025-07-17T09:05:52.9064021Z Returns: 2025-07-17T09:05:52.9064168Z Future handler of the communication, which updates the gradients in place. 2025-07-17T09:05:52.9064171Z 2025-07-17T09:05:52.9064250Z Example:: 2025-07-17T09:05:52.9064323Z >>> # xdoctest: +SKIP 2025-07-17T09:05:52.9064501Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2025-07-17T09:05:52.9064605Z start_powerSGD_iter=10, min_compression_rate=0.5) 2025-07-17T09:05:52.9064719Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2025-07-17T09:05:52.9064721Z 2025-07-17T09:05:52.9064882Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.9064885Z 2025-07-17T09:05:52.9068539Z msg = Cannot scrape callname=post_localSGD_hook in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/post_localSGD_hook.py line=72. 2025-07-17T09:05:52.9068737Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.9068740Z 2025-07-17T09:05:52.9068830Z Run post-localSGD algorithm. 2025-07-17T09:05:52.9068833Z 2025-07-17T09:05:52.9068979Z This DDP communication hook is used for running post-localSGD algorithm, 2025-07-17T09:05:52.9069241Z by combining with a model averaging component (e.g., 2025-07-17T09:05:52.9069449Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2025-07-17T09:05:52.9069539Z that runs after the optimizer step. 2025-07-17T09:05:52.9069542Z 2025-07-17T09:05:52.9069603Z Args: 2025-07-17T09:05:52.9069761Z state (PostLocalSGDState): State information to run post-localSGD. 2025-07-17T09:05:52.9070091Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2025-07-17T09:05:52.9070348Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2025-07-17T09:05:52.9070500Z Note that since DDP comm hook only supports single process single device mode, 2025-07-17T09:05:52.9070612Z only exactly one tensor is stored in this bucket. 2025-07-17T09:05:52.9070616Z 2025-07-17T09:05:52.9070678Z Returns: 2025-07-17T09:05:52.9070841Z Future handler of the communication, which updates the gradients in place. 2025-07-17T09:05:52.9070846Z 2025-07-17T09:05:52.9070911Z Example:: 2025-07-17T09:05:52.9070994Z >>> # xdoctest: +SKIP 2025-07-17T09:05:52.9071151Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2025-07-17T09:05:52.9071248Z start_localSGD_iter=10) 2025-07-17T09:05:52.9071365Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2025-07-17T09:05:52.9071578Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2025-07-17T09:05:52.9071793Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2025-07-17T09:05:52.9071796Z 2025-07-17T09:05:52.9071960Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.9071963Z 2025-07-17T09:05:52.9711693Z msg = Cannot scrape callname=FullyShardedDataParallel.set_state_dict_type in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=637. 2025-07-17T09:05:52.9712035Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.9712208Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2025-07-17T09:05:52.9712213Z 2025-07-17T09:05:52.9712396Z Also takes (optional) configuration for the model's and optimizer's state dict. 2025-07-17T09:05:52.9712539Z The target module does not have to be a FSDP module. If the target 2025-07-17T09:05:52.9712671Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2025-07-17T09:05:52.9712674Z 2025-07-17T09:05:52.9712833Z .. note:: This API should be called for only the top-level (root) 2025-07-17T09:05:52.9712895Z module. 2025-07-17T09:05:52.9712899Z 2025-07-17T09:05:52.9713035Z .. note:: This API enables users to transparently use the conventional 2025-07-17T09:05:52.9713158Z ``state_dict`` API to take model checkpoints in cases where the 2025-07-17T09:05:52.9713292Z root FSDP module is wrapped by another ``nn.Module``. For example, 2025-07-17T09:05:52.9713419Z the following will ensure ``state_dict`` is called on all non-FSDP 2025-07-17T09:05:52.9713567Z instances, while dispatching into `sharded_state_dict` implementation 2025-07-17T09:05:52.9713640Z for FSDP: 2025-07-17T09:05:52.9713644Z 2025-07-17T09:05:52.9713725Z Example:: 2025-07-17T09:05:52.9713728Z 2025-07-17T09:05:52.9713820Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T09:05:52.9713906Z >>> model = DDP(FSDP(...)) 2025-07-17T09:05:52.9713986Z >>> FSDP.set_state_dict_type( 2025-07-17T09:05:52.9714062Z >>> model, 2025-07-17T09:05:52.9714153Z >>> StateDictType.SHARDED_STATE_DICT, 2025-07-17T09:05:52.9714735Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2025-07-17T09:05:52.9714973Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2025-07-17T09:05:52.9715047Z >>> ) 2025-07-17T09:05:52.9715135Z >>> param_state_dict = model.state_dict() 2025-07-17T09:05:52.9715247Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2025-07-17T09:05:52.9715259Z 2025-07-17T09:05:52.9715498Z Args: 2025-07-17T09:05:52.9715587Z module (torch.nn.Module): Root module. 2025-07-17T09:05:52.9715740Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2025-07-17T09:05:52.9715878Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2025-07-17T09:05:52.9715969Z target ``state_dict_type``. 2025-07-17T09:05:52.9716119Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2025-07-17T09:05:52.9716212Z for the optimizer state dict. 2025-07-17T09:05:52.9716219Z 2025-07-17T09:05:52.9716278Z Returns: 2025-07-17T09:05:52.9716425Z A StateDictSettings that include the previous state_dict type and 2025-07-17T09:05:52.9716502Z configuration for the module. 2025-07-17T09:05:52.9716570Z 2025-07-17T09:05:52.9716715Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.9716722Z 2025-07-17T09:05:52.9717192Z msg = Cannot scrape callname=FullyShardedDataParallel.state_dict_type in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=795. 2025-07-17T09:05:52.9717347Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.9717502Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2025-07-17T09:05:52.9717505Z 2025-07-17T09:05:52.9717696Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2025-07-17T09:05:52.9717798Z :meth:`set_state_dict_type` for the detail. 2025-07-17T09:05:52.9717801Z 2025-07-17T09:05:52.9717873Z Example:: 2025-07-17T09:05:52.9717877Z 2025-07-17T09:05:52.9717980Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T09:05:52.9718054Z >>> model = DDP(FSDP(...)) 2025-07-17T09:05:52.9718147Z >>> with FSDP.state_dict_type( 2025-07-17T09:05:52.9718215Z >>> model, 2025-07-17T09:05:52.9718310Z >>> StateDictType.SHARDED_STATE_DICT, 2025-07-17T09:05:52.9718368Z >>> ): 2025-07-17T09:05:52.9718460Z >>> checkpoint = model.state_dict() 2025-07-17T09:05:52.9718464Z 2025-07-17T09:05:52.9718524Z Args: 2025-07-17T09:05:52.9718614Z module (torch.nn.Module): Root module. 2025-07-17T09:05:52.9718753Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2025-07-17T09:05:52.9718906Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2025-07-17T09:05:52.9719012Z configuration for the target ``state_dict_type``. 2025-07-17T09:05:52.9719161Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2025-07-17T09:05:52.9719284Z ``state_dict`` configuration for the target ``state_dict_type``. 2025-07-17T09:05:52.9719354Z 2025-07-17T09:05:52.9719507Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.9719510Z 2025-07-17T09:05:52.9751088Z msg = Cannot scrape callname=FullyShardedDataParallel.optim_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1808. 2025-07-17T09:05:52.9751301Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.9751317Z 2025-07-17T09:05:52.9751479Z Transform the state-dict of an optimizer corresponding to a sharded model. 2025-07-17T09:05:52.9751727Z 2025-07-17T09:05:52.9751883Z The given state-dict can be transformed to one of three types: 2025-07-17T09:05:52.9752074Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2025-07-17T09:05:52.9752078Z 2025-07-17T09:05:52.9752239Z For full optimizer state_dict, all states are unflattened and not sharded. 2025-07-17T09:05:52.9752512Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2025-07-17T09:05:52.9752594Z avoid OOM. 2025-07-17T09:05:52.9752598Z 2025-07-17T09:05:52.9752741Z For sharded optimizer state_dict, all states are unflattened but sharded. 2025-07-17T09:05:52.9752880Z CPU only can be specified via :meth:`state_dict_type` to further save 2025-07-17T09:05:52.9752944Z memory. 2025-07-17T09:05:52.9752948Z 2025-07-17T09:05:52.9753089Z For local state_dict, no transformation will be performed. But a state 2025-07-17T09:05:52.9753234Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2025-07-17T09:05:52.9753320Z nature (this is not supported yet). 2025-07-17T09:05:52.9753331Z 2025-07-17T09:05:52.9753400Z Example:: 2025-07-17T09:05:52.9753403Z 2025-07-17T09:05:52.9753493Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T09:05:52.9753652Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-07-17T09:05:52.9753762Z >>> from torch.distributed.fsdp import StateDictType 2025-07-17T09:05:52.9753887Z >>> from torch.distributed.fsdp import FullStateDictConfig 2025-07-17T09:05:52.9754014Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2025-07-17T09:05:52.9754092Z >>> # Save a checkpoint 2025-07-17T09:05:52.9754171Z >>> model, optim = ... 2025-07-17T09:05:52.9754264Z >>> FSDP.set_state_dict_type( 2025-07-17T09:05:52.9754326Z >>> model, 2025-07-17T09:05:52.9754419Z >>> StateDictType.FULL_STATE_DICT, 2025-07-17T09:05:52.9754508Z >>> FullStateDictConfig(rank0_only=False), 2025-07-17T09:05:52.9754611Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-07-17T09:05:52.9754673Z >>> ) 2025-07-17T09:05:52.9754766Z >>> state_dict = model.state_dict() 2025-07-17T09:05:52.9754879Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2025-07-17T09:05:52.9754983Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2025-07-17T09:05:52.9755054Z >>> # Load a checkpoint 2025-07-17T09:05:52.9755138Z >>> model, optim = ... 2025-07-17T09:05:52.9755243Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2025-07-17T09:05:52.9755319Z >>> FSDP.set_state_dict_type( 2025-07-17T09:05:52.9755391Z >>> model, 2025-07-17T09:05:52.9755471Z >>> StateDictType.FULL_STATE_DICT, 2025-07-17T09:05:52.9755568Z >>> FullStateDictConfig(rank0_only=False), 2025-07-17T09:05:52.9755659Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-07-17T09:05:52.9755735Z >>> ) 2025-07-17T09:05:52.9755816Z >>> model.load_state_dict(state_dict) 2025-07-17T09:05:52.9755928Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2025-07-17T09:05:52.9756009Z >>> model, optim, optim_state_dict 2025-07-17T09:05:52.9756076Z >>> ) 2025-07-17T09:05:52.9756162Z >>> optim.load_state_dict(optim_state_dict) 2025-07-17T09:05:52.9756165Z 2025-07-17T09:05:52.9756235Z Args: 2025-07-17T09:05:52.9756361Z model (torch.nn.Module): Root module (which may or may not be a 2025-07-17T09:05:52.9756501Z :class:`FullyShardedDataParallel` instance) whose parameters 2025-07-17T09:05:52.9756587Z were passed into the optimizer ``optim``. 2025-07-17T09:05:52.9756710Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2025-07-17T09:05:52.9756777Z parameters. 2025-07-17T09:05:52.9756921Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2025-07-17T09:05:52.9757052Z transform. If the value is None, optim.state_dict() will be used. ( 2025-07-17T09:05:52.9757120Z Default: ``None``) 2025-07-17T09:05:52.9757343Z group (dist.ProcessGroup): Model's process group across which parameters 2025-07-17T09:05:52.9757516Z are sharded or ``None`` if using the default process group. ( 2025-07-17T09:05:52.9757595Z Default: ``None``) 2025-07-17T09:05:52.9757598Z 2025-07-17T09:05:52.9757660Z Returns: 2025-07-17T09:05:52.9757795Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2025-07-17T09:05:52.9758005Z ``model``. The sharding of the optimizer state is based on 2025-07-17T09:05:52.9758087Z ``state_dict_type``. 2025-07-17T09:05:52.9758090Z 2025-07-17T09:05:52.9758240Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.9758243Z 2025-07-17T09:05:52.9758755Z msg = Cannot scrape callname=FullyShardedDataParallel.optim_state_dict_to_load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1906. 2025-07-17T09:05:52.9758929Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.9758935Z 2025-07-17T09:05:52.9759143Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2025-07-17T09:05:52.9759147Z 2025-07-17T09:05:52.9759261Z Given a ``optim_state_dict`` that is transformed through 2025-07-17T09:05:52.9759390Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2025-07-17T09:05:52.9759528Z state_dict that can be loaded to ``optim`` which is the optimizer for 2025-07-17T09:05:52.9759650Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2025-07-17T09:05:52.9759653Z 2025-07-17T09:05:52.9759750Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T09:05:52.9759894Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-07-17T09:05:52.9760003Z >>> from torch.distributed.fsdp import StateDictType 2025-07-17T09:05:52.9760113Z >>> from torch.distributed.fsdp import FullStateDictConfig 2025-07-17T09:05:52.9760249Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2025-07-17T09:05:52.9760322Z >>> # Save a checkpoint 2025-07-17T09:05:52.9760401Z >>> model, optim = ... 2025-07-17T09:05:52.9760475Z >>> FSDP.set_state_dict_type( 2025-07-17T09:05:52.9760545Z >>> model, 2025-07-17T09:05:52.9760625Z >>> StateDictType.FULL_STATE_DICT, 2025-07-17T09:05:52.9760730Z >>> FullStateDictConfig(rank0_only=False), 2025-07-17T09:05:52.9760824Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-07-17T09:05:52.9760885Z >>> ) 2025-07-17T09:05:52.9760974Z >>> state_dict = model.state_dict() 2025-07-17T09:05:52.9761048Z >>> original_osd = optim.state_dict() 2025-07-17T09:05:52.9761146Z >>> optim_state_dict = FSDP.optim_state_dict( 2025-07-17T09:05:52.9761211Z >>> model, 2025-07-17T09:05:52.9761286Z >>> optim, 2025-07-17T09:05:52.9761361Z >>> optim_state_dict=original_osd 2025-07-17T09:05:52.9761431Z >>> ) 2025-07-17T09:05:52.9761523Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2025-07-17T09:05:52.9761603Z >>> # Load a checkpoint 2025-07-17T09:05:52.9761667Z >>> model, optim = ... 2025-07-17T09:05:52.9761773Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2025-07-17T09:05:52.9761842Z >>> FSDP.set_state_dict_type( 2025-07-17T09:05:52.9761909Z >>> model, 2025-07-17T09:05:52.9761991Z >>> StateDictType.FULL_STATE_DICT, 2025-07-17T09:05:52.9762072Z >>> FullStateDictConfig(rank0_only=False), 2025-07-17T09:05:52.9762166Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-07-17T09:05:52.9762222Z >>> ) 2025-07-17T09:05:52.9762310Z >>> model.load_state_dict(state_dict) 2025-07-17T09:05:52.9762409Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2025-07-17T09:05:52.9762490Z >>> model, optim, optim_state_dict 2025-07-17T09:05:52.9762547Z >>> ) 2025-07-17T09:05:52.9762640Z >>> optim.load_state_dict(optim_state_dict) 2025-07-17T09:05:52.9762712Z 2025-07-17T09:05:52.9762826Z Args: 2025-07-17T09:05:52.9762954Z model (torch.nn.Module): Root module (which may or may not be a 2025-07-17T09:05:52.9763075Z :class:`FullyShardedDataParallel` instance) whose parameters 2025-07-17T09:05:52.9763171Z were passed into the optimizer ``optim``. 2025-07-17T09:05:52.9763284Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2025-07-17T09:05:52.9763487Z parameters. 2025-07-17T09:05:52.9763619Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2025-07-17T09:05:52.9763758Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2025-07-17T09:05:52.9763873Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2025-07-17T09:05:52.9763988Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2025-07-17T09:05:52.9764107Z load_directly (bool): If this is set to True, this API will also 2025-07-17T09:05:52.9764242Z call optim.load_state_dict(result) before returning the result. 2025-07-17T09:05:52.9764374Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2025-07-17T09:05:52.9764452Z (Default: ``False``) 2025-07-17T09:05:52.9764593Z group (dist.ProcessGroup): Model's process group across which parameters 2025-07-17T09:05:52.9764715Z are sharded or ``None`` if using the default process group. ( 2025-07-17T09:05:52.9764785Z Default: ``None``) 2025-07-17T09:05:52.9764788Z 2025-07-17T09:05:52.9764933Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.9764946Z 2025-07-17T09:05:52.9895717Z msg = Cannot scrape callname=MixedPrecision in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/api.py line=114. 2025-07-17T09:05:52.9895982Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.9895987Z 2025-07-17T09:05:52.9896106Z This configures FSDP-native mixed precision training. 2025-07-17T09:05:52.9896126Z 2025-07-17T09:05:52.9896211Z Attributes: 2025-07-17T09:05:52.9896369Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2025-07-17T09:05:52.9896506Z parameters during forward and backward and thus the dtype for 2025-07-17T09:05:52.9896634Z forward and backward computation. Outside forward and backward, the 2025-07-17T09:05:52.9896771Z *sharded* parameters are kept in full precision (e.g. for the 2025-07-17T09:05:52.9896898Z optimizer step), and for model checkpointing, the parameters are 2025-07-17T09:05:52.9897020Z always saved in full precision. (Default: ``None``) 2025-07-17T09:05:52.9897156Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2025-07-17T09:05:52.9897297Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2025-07-17T09:05:52.9897408Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2025-07-17T09:05:52.9897548Z the ``param_dtype`` value, still running gradient reduction in low 2025-07-17T09:05:52.9897686Z precision. This is permitted to differ from ``param_dtype``, e.g. 2025-07-17T09:05:52.9897821Z to force gradient reduction to run in full precision. (Default: 2025-07-17T09:05:52.9897882Z ``None``) 2025-07-17T09:05:52.9898026Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2025-07-17T09:05:52.9898153Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2025-07-17T09:05:52.9898287Z ``buffer_dtype`` in the first forward pass and keeps them in that 2025-07-17T09:05:52.9898419Z dtype thereafter. For model checkpointing, the buffers are saved 2025-07-17T09:05:52.9898544Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2025-07-17T09:05:52.9898605Z ``None``) 2025-07-17T09:05:52.9898733Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2025-07-17T09:05:52.9898868Z gradients to full precision after the backward pass in preparation 2025-07-17T09:05:52.9899320Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2025-07-17T09:05:52.9899437Z in the dtype used for gradient reduction, which can save memory if 2025-07-17T09:05:52.9899576Z using a custom optimizer that supports running in low precision. 2025-07-17T09:05:52.9899647Z (Default: ``False``) 2025-07-17T09:05:52.9899918Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2025-07-17T09:05:52.9900061Z its forward args and kwargs to ``param_dtype``. This is to ensure 2025-07-17T09:05:52.9900188Z that parameter and input dtypes match for forward computation, as 2025-07-17T09:05:52.9900329Z required by many ops. This may need to be set to ``True`` when only 2025-07-17T09:05:52.9900458Z applying mixed precision to some but not all FSDP modules, in which 2025-07-17T09:05:52.9900596Z case a mixed-precision FSDP submodule needs to recast its inputs. 2025-07-17T09:05:52.9900670Z (Default: ``False``) 2025-07-17T09:05:52.9900815Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2025-07-17T09:05:52.9900935Z casts its forward args and kwargs to ``param_dtype``, overriding 2025-07-17T09:05:52.9901062Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2025-07-17T09:05:52.9901159Z this does not do anything. (Default: ``True``) 2025-07-17T09:05:52.9901304Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2025-07-17T09:05:52.9901422Z module classes to ignore for mixed precision when using an 2025-07-17T09:05:52.9901547Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2025-07-17T09:05:52.9901670Z applied to them separately with mixed precision disabled (meaning 2025-07-17T09:05:52.9901805Z that the final FSDP construction would deviate from the specified 2025-07-17T09:05:52.9901921Z policy). If ``auto_wrap_policy`` is not specified, then this does 2025-07-17T09:05:52.9902053Z not do anything. This API is experimental and subject to change. 2025-07-17T09:05:52.9902128Z (Default: ``(_BatchNorm,)``) 2025-07-17T09:05:52.9902133Z 2025-07-17T09:05:52.9902258Z .. note:: This API is experimental and subject to change. 2025-07-17T09:05:52.9902261Z 2025-07-17T09:05:52.9902392Z .. note:: Only floating point tensors are cast to their specified dtypes. 2025-07-17T09:05:52.9902396Z 2025-07-17T09:05:52.9902519Z .. note:: In ``summon_full_params``, parameters are forced to full 2025-07-17T09:05:52.9902595Z precision, but buffers are not. 2025-07-17T09:05:52.9902599Z 2025-07-17T09:05:52.9902745Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2025-07-17T09:05:52.9902879Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2025-07-17T09:05:52.9903030Z Disabling FSDP's mixed precision for those norm modules only means that 2025-07-17T09:05:52.9903163Z the affine parameters are kept in ``float32``. However, this incurs 2025-07-17T09:05:52.9903322Z separate all-gathers and reduce-scatters for those norm modules, which 2025-07-17T09:05:52.9903453Z may be inefficient, so if the workload permits, the user should prefer 2025-07-17T09:05:52.9903558Z to still apply mixed precision to those modules. 2025-07-17T09:05:52.9903562Z 2025-07-17T09:05:52.9903692Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2025-07-17T09:05:52.9903830Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2025-07-17T09:05:52.9903966Z modules will have FSDP applied to them separately with mixed precision 2025-07-17T09:05:52.9904089Z disabled. See the ``_module_classes_to_ignore`` argument. 2025-07-17T09:05:52.9904092Z 2025-07-17T09:05:52.9904215Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2025-07-17T09:05:52.9904354Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2025-07-17T09:05:52.9904528Z its ``cast_root_forward_inputs`` takes precedence over its 2025-07-17T09:05:52.9904717Z ``cast_forward_inputs``. For non-root FSDP instances, their 2025-07-17T09:05:52.9904849Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2025-07-17T09:05:52.9904994Z sufficient for the typical case where each FSDP instance has the same 2025-07-17T09:05:52.9905239Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2025-07-17T09:05:52.9905474Z ``param_dtype`` at the beginning of the model's forward pass. 2025-07-17T09:05:52.9905477Z 2025-07-17T09:05:52.9905606Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2025-07-17T09:05:52.9905757Z configurations, we recommend setting individual ``cast_forward_inputs`` 2025-07-17T09:05:52.9905881Z values to configure casting inputs or not before each instance's 2025-07-17T09:05:52.9906006Z forward. In such a case, since the casts happen before each FSDP 2025-07-17T09:05:52.9906144Z instance's forward, a parent FSDP instance should have its non-FSDP 2025-07-17T09:05:52.9906292Z submodules run before its FSDP submodules to avoid the activation dtype 2025-07-17T09:05:52.9906420Z being changed due to a different ``MixedPrecision`` configuration. 2025-07-17T09:05:52.9906423Z 2025-07-17T09:05:52.9906500Z Example:: 2025-07-17T09:05:52.9906503Z 2025-07-17T09:05:52.9906597Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T09:05:52.9906719Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2025-07-17T09:05:52.9906794Z >>> model[1] = FSDP( 2025-07-17T09:05:52.9906860Z >>> model[1], 2025-07-17T09:05:52.9907057Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2025-07-17T09:05:52.9907117Z >>> ) 2025-07-17T09:05:52.9907196Z >>> model = FSDP( 2025-07-17T09:05:52.9907257Z >>> model, 2025-07-17T09:05:52.9907472Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2025-07-17T09:05:52.9907534Z >>> ) 2025-07-17T09:05:52.9907547Z 2025-07-17T09:05:52.9907680Z The above shows a working example. On the other hand, if ``model[1]`` 2025-07-17T09:05:52.9907808Z were replaced with ``model[0]``, meaning that the submodule using 2025-07-17T09:05:52.9907948Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2025-07-17T09:05:52.9908081Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2025-07-17T09:05:52.9908152Z ones. 2025-07-17T09:05:52.9908155Z 2025-07-17T09:05:52.9908158Z 2025-07-17T09:05:52.9908310Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.9908322Z 2025-07-17T09:05:52.9908660Z msg = Cannot scrape callname=FullStateDictConfig in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/api.py line=295. 2025-07-17T09:05:52.9908827Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:52.9908831Z 2025-07-17T09:05:52.9908963Z ``FullStateDictConfig`` is a config class meant to be used with 2025-07-17T09:05:52.9909098Z ``StateDictType.FULL_STATE_DICT``. We recommend enabling both 2025-07-17T09:05:52.9909224Z ``offload_to_cpu=True`` and ``rank0_only=True`` when saving full state 2025-07-17T09:05:52.9909371Z dicts to save GPU memory and CPU memory, respectively. This config class 2025-07-17T09:05:52.9909496Z is meant to be used via the :func:`state_dict_type` context manager as 2025-07-17T09:05:52.9909563Z follows: 2025-07-17T09:05:52.9909579Z 2025-07-17T09:05:52.9909666Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T09:05:52.9909810Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-07-17T09:05:52.9909902Z >>> fsdp = FSDP(model, auto_wrap_policy=...) 2025-07-17T09:05:52.9910029Z >>> cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True) 2025-07-17T09:05:52.9910253Z >>> with FSDP.state_dict_type(fsdp, StateDictType.FULL_STATE_DICT, cfg): 2025-07-17T09:05:52.9910400Z >>> state = fsdp.state_dict() 2025-07-17T09:05:52.9910540Z >>> # `state` will be empty on non rank 0 and contain CPU tensors on rank 0. 2025-07-17T09:05:52.9910710Z >>> # To reload checkpoint for inference, finetuning, transfer learning, etc: 2025-07-17T09:05:52.9910985Z >>> model = model_fn() # Initialize model in preparation for wrapping with FSDP 2025-07-17T09:05:52.9911060Z >>> if dist.get_rank() == 0: 2025-07-17T09:05:52.9911186Z >>> # Load checkpoint only on rank 0 to avoid memory redundancy 2025-07-17T09:05:52.9911283Z >>> state_dict = torch.load("my_checkpoint.pt") 2025-07-17T09:05:52.9911375Z >>> model.load_state_dict(state_dict) 2025-07-17T09:05:52.9911514Z >>> # All ranks initialize FSDP module as usual. `sync_module_states` argument 2025-07-17T09:05:52.9911671Z >>> # communicates loaded checkpoint states from rank 0 to rest of the world. 2025-07-17T09:05:52.9911743Z >>> fsdp = FSDP( 2025-07-17T09:05:52.9911818Z ... model, 2025-07-17T09:05:52.9911907Z ... device_id=torch.cuda.current_device(), 2025-07-17T09:05:52.9911989Z ... auto_wrap_policy=..., 2025-07-17T09:05:52.9912063Z ... sync_module_states=True, 2025-07-17T09:05:52.9912133Z ... ) 2025-07-17T09:05:52.9912270Z >>> # After this point, all ranks have FSDP model with loaded checkpoint. 2025-07-17T09:05:52.9912274Z 2025-07-17T09:05:52.9912346Z Attributes: 2025-07-17T09:05:52.9912474Z rank0_only (bool): If ``True``, then only rank 0 saves the full state 2025-07-17T09:05:52.9912610Z dict, and nonzero ranks save an empty dict. If ``False``, then all 2025-07-17T09:05:52.9912713Z ranks save the full state dict. (Default: ``False``) 2025-07-17T09:05:52.9912716Z 2025-07-17T09:05:52.9912876Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:52.9912878Z 2025-07-17T09:05:53.0134553Z msg = Cannot scrape callname=_server_process_global_profile in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/server_process_global_profiler.py line=19. 2025-07-17T09:05:53.0135246Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:53.0135478Z 2025-07-17T09:05:53.0135635Z It has the same API as ``torch.autograd.profiler.profile`` class, 2025-07-17T09:05:53.0136017Z except that it enables profiling on all threads running RPC server request callbacks. 2025-07-17T09:05:53.0136252Z 2025-07-17T09:05:53.0136434Z Context manager that manages autograd profiler state and holds a summary of results. 2025-07-17T09:05:53.0136817Z Under the hood it just records events of functions being executed in C++ and 2025-07-17T09:05:53.0137167Z exposes those events to Python. You can wrap any code into it and it will 2025-07-17T09:05:53.0137462Z only report runtime of PyTorch functions. 2025-07-17T09:05:53.0137762Z Note: profiler is thread local and is automatically propagated into the async tasks 2025-07-17T09:05:53.0138007Z 2025-07-17T09:05:53.0138066Z Args: 2025-07-17T09:05:53.0138320Z enabled (bool, optional): Setting this to False makes this context manager a no-op. 2025-07-17T09:05:53.0138616Z Default: ``True``. 2025-07-17T09:05:53.0138738Z 2025-07-17T09:05:53.0138912Z use_cuda (bool, optional): Enables timing of CUDA events as well using the cudaEvent API. 2025-07-17T09:05:53.0139283Z Adds approximately 4us of overhead to each tensor operation. 2025-07-17T09:05:53.0139536Z Default: ``False`` 2025-07-17T09:05:53.0139647Z 2025-07-17T09:05:53.0139784Z record_shapes (bool, optional): If shapes recording is set, information 2025-07-17T09:05:53.0140144Z about input dimensions will be collected. This allows one to see which 2025-07-17T09:05:53.0140494Z dimensions have been used under the hood and further group by them 2025-07-17T09:05:53.0140828Z using prof.key_averages(group_by_input_shape=True). Please note that 2025-07-17T09:05:53.0141451Z shape recording might skew your profiling data. It is recommended to 2025-07-17T09:05:53.0141828Z use separate runs with and without shape recording to validate the timing. 2025-07-17T09:05:53.0142201Z Most likely the skew will be negligible for bottom most events (in a case 2025-07-17T09:05:53.0142687Z of nested function calls). But for higher level functions the total 2025-07-17T09:05:53.0143022Z self cpu time might be artificially increased because of the shape 2025-07-17T09:05:53.0143277Z collection. 2025-07-17T09:05:53.0143376Z 2025-07-17T09:05:53.0143559Z profile_memory (bool, optional): Whether to report memory usage, default: ``False`` 2025-07-17T09:05:53.0143779Z 2025-07-17T09:05:53.0143868Z .. warning:: 2025-07-17T09:05:53.0144082Z Enabling memory profiling incurs additional profiler overhead 2025-07-17T09:05:53.0144276Z 2025-07-17T09:05:53.0144339Z .. warning:: 2025-07-17T09:05:53.0144617Z Due to some CUDA multiprocessing limitations (see :ref:`multiprocessing-cuda-note`), 2025-07-17T09:05:53.0144984Z one cannot use the profiler with ``use_cuda = True`` to benchmark 2025-07-17T09:05:53.0145420Z DataLoaders with ``num_workers > 0``. If you wish to benchmark data loading, 2025-07-17T09:05:53.0145739Z please use ``use_cuda = False`` or ``num_workers = 0``. 2025-07-17T09:05:53.0145916Z 2025-07-17T09:05:53.0145990Z Example: 2025-07-17T09:05:53.0146152Z >>> # xdoctest: +SKIP 2025-07-17T09:05:53.0146337Z >>> # On worker 0: 2025-07-17T09:05:53.0146511Z >>> import torch 2025-07-17T09:05:53.0146705Z >>> import torch.distributed.rpc as rpc 2025-07-17T09:05:53.0146942Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-07-17T09:05:53.0147190Z >>> x, y = torch.tensor(1), torch.tensor(2) 2025-07-17T09:05:53.0147405Z >>> outer_profile_rref = rpc.remote( 2025-07-17T09:05:53.0147653Z ... dst_worker_name, rpc._server_process_global_profile 2025-07-17T09:05:53.0147888Z ... ) 2025-07-17T09:05:53.0148064Z >>> outer_profile_rref.rpc_sync().__enter__() 2025-07-17T09:05:53.0148313Z >>> rpc.rpc_sync(dst_worker_name, torch.add, (x, y)) 2025-07-17T09:05:53.0148544Z >>> inner_profile_rref = rpc.remote( 2025-07-17T09:05:53.0148784Z ... dst_worker_name, rpc._server_process_global_profile 2025-07-17T09:05:53.0149008Z ... ) 2025-07-17T09:05:53.0149183Z >>> inner_profile_rref.rpc_sync().__enter__() 2025-07-17T09:05:53.0149423Z >>> rpc.rpc_sync(dst_worker_name, torch.sub, (x, y)) 2025-07-17T09:05:53.0149689Z >>> inner_profile_rref.rpc_sync().__exit__(None, None, None) 2025-07-17T09:05:53.0149970Z >>> outer_profile_rref.rpc_sync().__exit__(None, None, None) 2025-07-17T09:05:53.0150238Z >>> print(inner_profile_rref.rpc_sync().key_averages()) 2025-07-17T09:05:53.0150557Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-07-17T09:05:53.0150967Z Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls 2025-07-17T09:05:53.0151357Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-07-17T09:05:53.0151686Z sub 85.06% 76.275us 100.00% 89.667us 89.667us 1 2025-07-17T09:05:53.0151990Z empty 14.94% 13.392us 14.94% 13.392us 13.392us 1 2025-07-17T09:05:53.0152310Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-07-17T09:05:53.0152588Z Self CPU time total: 89.667us 2025-07-17T09:05:53.0152822Z >>> print(outer_profile_rref.rpc_sync().key_averages()) 2025-07-17T09:05:53.0153122Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-07-17T09:05:53.0153509Z Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls 2025-07-17T09:05:53.0154052Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-07-17T09:05:53.0154368Z sub 35.65% 76.275us 41.91% 89.667us 89.667us 1 2025-07-17T09:05:53.0154784Z empty 12.67% 27.101us 12.67% 27.101us 13.551us 2 2025-07-17T09:05:53.0155070Z add 51.68% 110.550us 58.09% 124.259us 124.259us 1 2025-07-17T09:05:53.0155389Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-07-17T09:05:53.0155679Z Self CPU time total: 213.926us 2025-07-17T09:05:53.0155877Z >>> rpc.shutdown() 2025-07-17T09:05:53.0155983Z 2025-07-17T09:05:53.0156055Z >>> # On worker 1: 2025-07-17T09:05:53.0156233Z >>> import torch.distributed.rpc as rpc 2025-07-17T09:05:53.0156474Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-07-17T09:05:53.0156736Z >>> # wait for worker 0 to finish work, and then shutdown. 2025-07-17T09:05:53.0156972Z >>> rpc.shutdown() 2025-07-17T09:05:53.0157074Z 2025-07-17T09:05:53.0157226Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:53.0157447Z 2025-07-17T09:05:53.0245956Z msg = Cannot scrape callname=TensorPipeRpcBackendOptions.set_device_map in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/options.py line=113. 2025-07-17T09:05:53.0246660Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:53.0246901Z 2025-07-17T09:05:53.0247038Z Set device mapping between each RPC caller and callee pair. This 2025-07-17T09:05:53.0247364Z function can be called multiple times to incrementally add 2025-07-17T09:05:53.0247627Z device placement configurations. 2025-07-17T09:05:53.0247770Z 2025-07-17T09:05:53.0247828Z Args: 2025-07-17T09:05:53.0247991Z to (str): Callee name. 2025-07-17T09:05:53.0248236Z device_map (Dict of int, str, or torch.device): Device placement 2025-07-17T09:05:53.0248544Z mappings from this worker to the callee. This map must be 2025-07-17T09:05:53.0248790Z invertible. 2025-07-17T09:05:53.0248882Z 2025-07-17T09:05:53.0248957Z Example: 2025-07-17T09:05:53.0249127Z >>> # xdoctest: +SKIP("distributed") 2025-07-17T09:05:53.0249337Z >>> # both workers 2025-07-17T09:05:53.0249500Z >>> def add(x, y): 2025-07-17T09:05:53.0249694Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2025-07-17T09:05:53.0249926Z >>> return x + y, (x + y).to(2) 2025-07-17T09:05:53.0250122Z >>> 2025-07-17T09:05:53.0250261Z >>> # on worker 0 2025-07-17T09:05:53.0250465Z >>> options = TensorPipeRpcBackendOptions( 2025-07-17T09:05:53.0250689Z >>> num_worker_threads=8, 2025-07-17T09:05:53.0250896Z >>> device_maps={"worker1": {0: 1}} 2025-07-17T09:05:53.0251123Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2025-07-17T09:05:53.0251350Z >>> ) 2025-07-17T09:05:53.0251513Z >>> options.set_device_map("worker1", {1: 2}) 2025-07-17T09:05:53.0251744Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2025-07-17T09:05:53.0251945Z >>> 2025-07-17T09:05:53.0252096Z >>> rpc.init_rpc( 2025-07-17T09:05:53.0252260Z >>> "worker0", 2025-07-17T09:05:53.0252426Z >>> rank=0, 2025-07-17T09:05:53.0252593Z >>> world_size=2, 2025-07-17T09:05:53.0252793Z >>> backend=rpc.BackendType.TENSORPIPE, 2025-07-17T09:05:53.0253016Z >>> rpc_backend_options=options 2025-07-17T09:05:53.0253211Z >>> ) 2025-07-17T09:05:53.0253354Z >>> 2025-07-17T09:05:53.0253490Z >>> x = torch.ones(2) 2025-07-17T09:05:53.0253708Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2025-07-17T09:05:53.0253993Z >>> # The first argument will be moved to cuda:1 on worker1. When 2025-07-17T09:05:53.0254474Z >>> # sending the return value back, it will follow the invert of 2025-07-17T09:05:53.0254850Z >>> # the device map, and hence will be moved back to cuda:0 and 2025-07-17T09:05:53.0255091Z >>> # cuda:1 on worker0 2025-07-17T09:05:53.0255300Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2025-07-17T09:05:53.0255569Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2025-07-17T09:05:53.0255720Z 2025-07-17T09:05:53.0256047Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:53.0256266Z 2025-07-17T09:05:53.0257517Z msg = Cannot scrape callname=async_execution in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/functions.py line=6. 2025-07-17T09:05:53.0258128Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:53.0258366Z 2025-07-17T09:05:53.0258525Z A decorator for a function indicating that the return value of the function 2025-07-17T09:05:53.0258883Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2025-07-17T09:05:53.0259245Z function can run asynchronously on the RPC callee. More specifically, the 2025-07-17T09:05:53.0259597Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2025-07-17T09:05:53.0259950Z function and installs subsequent processing steps as a callback to that 2025-07-17T09:05:53.0260314Z :class:`~torch.futures.Future`. The installed callback will read the value 2025-07-17T09:05:53.0260642Z from the :class:`~torch.futures.Future` when completed and send the 2025-07-17T09:05:53.0260948Z value back as the RPC response. That also means the returned 2025-07-17T09:05:53.0261253Z :class:`~torch.futures.Future` only exists on the callee side and is never 2025-07-17T09:05:53.0261599Z sent through RPC. This decorator is useful when the wrapped function's 2025-07-17T09:05:53.0261927Z (``fn``) execution needs to pause and resume due to, e.g., containing 2025-07-17T09:05:53.0262247Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2025-07-17T09:05:53.0262456Z 2025-07-17T09:05:53.0262596Z .. note:: To enable asynchronous execution, applications must pass the 2025-07-17T09:05:53.0262924Z function object returned by this decorator to RPC APIs. If RPC detected 2025-07-17T09:05:53.0263262Z attributes installed by this decorator, it knows that this function 2025-07-17T09:05:53.0263580Z returns a ``Future`` object and will handle that accordingly. 2025-07-17T09:05:53.0263887Z However, this does not mean this decorator has to be outmost one when 2025-07-17T09:05:53.0264218Z defining a function. For example, when combined with ``@staticmethod`` 2025-07-17T09:05:53.0264539Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2025-07-17T09:05:53.0264858Z inner decorator to allow the target function be recognized as a static 2025-07-17T09:05:53.0265195Z or class function. This target function can still execute asynchronously 2025-07-17T09:05:53.0265637Z because, when accessed, the static or class method preserves attributes 2025-07-17T09:05:53.0265940Z installed by ``@rpc.functions.async_execution``. 2025-07-17T09:05:53.0266097Z 2025-07-17T09:05:53.0266100Z 2025-07-17T09:05:53.0266174Z Example:: 2025-07-17T09:05:53.0266398Z The returned :class:`~torch.futures.Future` object can come from 2025-07-17T09:05:53.0266669Z :meth:`~torch.distributed.rpc.rpc_async`, 2025-07-17T09:05:53.0266965Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2025-07-17T09:05:53.0267283Z constructor. The example below shows directly using the 2025-07-17T09:05:53.0267547Z :class:`~torch.futures.Future` returned by 2025-07-17T09:05:53.0267790Z :meth:`~torch.futures.Future.then`. 2025-07-17T09:05:53.0267929Z 2025-07-17T09:05:53.0268013Z >>> from torch.distributed import rpc 2025-07-17T09:05:53.0268203Z >>> 2025-07-17T09:05:53.0268357Z >>> # omitting setup and shutdown RPC 2025-07-17T09:05:53.0268734Z >>> 2025-07-17T09:05:53.0268876Z >>> # On all workers 2025-07-17T09:05:53.0269127Z >>> @rpc.functions.async_execution 2025-07-17T09:05:53.0269353Z >>> def async_add_chained(to, x, y, z): 2025-07-17T09:05:53.0269610Z >>> # This function runs on "worker1" and returns immediately when 2025-07-17T09:05:53.0269897Z >>> # the callback is installed through the `then(cb)` API. In the 2025-07-17T09:05:53.0270310Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2025-07-17T09:05:53.0270596Z >>> # When the return value of that `rpc_async` arrives at 2025-07-17T09:05:53.0270875Z >>> # "worker1", "worker1" will run the lambda function accordingly 2025-07-17T09:05:53.0271156Z >>> # and set the value for the previously returned `Future`, which 2025-07-17T09:05:53.0271436Z >>> # will then trigger RPC to send the result back to "worker0". 2025-07-17T09:05:53.0271710Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-07-17T09:05:53.0271951Z >>> lambda fut: fut.wait() + z 2025-07-17T09:05:53.0272390Z >>> ) 2025-07-17T09:05:53.0272637Z >>> 2025-07-17T09:05:53.0272842Z >>> # On worker0 2025-07-17T09:05:53.0284207Z >>> # xdoctest: +SKIP 2025-07-17T09:05:53.0284404Z >>> ret = rpc.rpc_sync( 2025-07-17T09:05:53.0284604Z >>> "worker1", 2025-07-17T09:05:53.0284785Z >>> async_add_chained, 2025-07-17T09:05:53.0285009Z >>> args=("worker2", torch.ones(2), 1, 1) 2025-07-17T09:05:53.0285247Z >>> ) 2025-07-17T09:05:53.0285431Z >>> print(ret) # prints tensor([3., 3.]) 2025-07-17T09:05:53.0285573Z 2025-07-17T09:05:53.0285728Z When combined with TorchScript decorators, this decorator must be the 2025-07-17T09:05:53.0286001Z outmost one. 2025-07-17T09:05:53.0286099Z 2025-07-17T09:05:53.0286187Z >>> from torch import Tensor 2025-07-17T09:05:53.0286404Z >>> from torch.futures import Future 2025-07-17T09:05:53.0286631Z >>> from torch.distributed import rpc 2025-07-17T09:05:53.0286831Z >>> 2025-07-17T09:05:53.0286997Z >>> # omitting setup and shutdown RPC 2025-07-17T09:05:53.0287203Z >>> 2025-07-17T09:05:53.0287359Z >>> # On all workers 2025-07-17T09:05:53.0287549Z >>> @torch.jit.script 2025-07-17T09:05:53.0287766Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2025-07-17T09:05:53.0288001Z >>> return x + y 2025-07-17T09:05:53.0288170Z >>> 2025-07-17T09:05:53.0288340Z >>> @rpc.functions.async_execution 2025-07-17T09:05:53.0288554Z >>> @torch.jit.script 2025-07-17T09:05:53.0288787Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2025-07-17T09:05:53.0289067Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2025-07-17T09:05:53.0289287Z >>> 2025-07-17T09:05:53.0289440Z >>> # On worker0 2025-07-17T09:05:53.0289619Z >>> ret = rpc.rpc_sync( 2025-07-17T09:05:53.0289805Z >>> "worker1", 2025-07-17T09:05:53.0289974Z >>> async_add, 2025-07-17T09:05:53.0290157Z >>> args=("worker2", torch.ones(2), 1) 2025-07-17T09:05:53.0290366Z >>> ) 2025-07-17T09:05:53.0290537Z >>> print(ret) # prints tensor([2., 2.]) 2025-07-17T09:05:53.0290675Z 2025-07-17T09:05:53.0290827Z When combined with static or class method, this decorator must be the 2025-07-17T09:05:53.0291086Z inner one. 2025-07-17T09:05:53.0291192Z 2025-07-17T09:05:53.0291272Z >>> from torch.distributed import rpc 2025-07-17T09:05:53.0291480Z >>> 2025-07-17T09:05:53.0291653Z >>> # omitting setup and shutdown RPC 2025-07-17T09:05:53.0291851Z >>> 2025-07-17T09:05:53.0292005Z >>> # On all workers 2025-07-17T09:05:53.0292200Z >>> class AsyncExecutionClass: 2025-07-17T09:05:53.0292400Z >>> 2025-07-17T09:05:53.0292547Z >>> @staticmethod 2025-07-17T09:05:53.0292749Z >>> @rpc.functions.async_execution 2025-07-17T09:05:53.0292971Z >>> def static_async_add(to, x, y, z): 2025-07-17T09:05:53.0293217Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-07-17T09:05:53.0293618Z >>> lambda fut: fut.wait() + z 2025-07-17T09:05:53.0293883Z >>> ) 2025-07-17T09:05:53.0294042Z >>> 2025-07-17T09:05:53.0294196Z >>> @classmethod 2025-07-17T09:05:53.0294381Z >>> @rpc.functions.async_execution 2025-07-17T09:05:53.0294606Z >>> def class_async_add(cls, to, x, y, z): 2025-07-17T09:05:53.0294845Z >>> ret_fut = torch.futures.Future() 2025-07-17T09:05:53.0295201Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-07-17T09:05:53.0295474Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2025-07-17T09:05:53.0295707Z >>> ) 2025-07-17T09:05:53.0295868Z >>> return ret_fut 2025-07-17T09:05:53.0296049Z >>> 2025-07-17T09:05:53.0296218Z >>> @rpc.functions.async_execution 2025-07-17T09:05:53.0296440Z >>> def bound_async_add(self, to, x, y, z): 2025-07-17T09:05:53.0296707Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-07-17T09:05:53.0296958Z >>> lambda fut: fut.wait() + z 2025-07-17T09:05:53.0297175Z >>> ) 2025-07-17T09:05:53.0297329Z >>> 2025-07-17T09:05:53.0297477Z >>> # On worker0 2025-07-17T09:05:53.0297649Z >>> ret = rpc.rpc_sync( 2025-07-17T09:05:53.0297820Z >>> "worker1", 2025-07-17T09:05:53.0298012Z >>> AsyncExecutionClass.static_async_add, 2025-07-17T09:05:53.0298244Z >>> args=("worker2", torch.ones(2), 1, 2) 2025-07-17T09:05:53.0298445Z >>> ) 2025-07-17T09:05:53.0298615Z >>> print(ret) # prints tensor([4., 4.]) 2025-07-17T09:05:53.0298816Z >>> 2025-07-17T09:05:53.0298978Z >>> ret = rpc.rpc_sync( 2025-07-17T09:05:53.0299155Z >>> "worker1", 2025-07-17T09:05:53.0299339Z >>> AsyncExecutionClass.class_async_add, 2025-07-17T09:05:53.0299559Z >>> args=("worker2", torch.ones(2), 1, 2) 2025-07-17T09:05:53.0299754Z >>> ) 2025-07-17T09:05:53.0299915Z >>> print(ret) # prints tensor([4., 4.]) 2025-07-17T09:05:53.0300068Z 2025-07-17T09:05:53.0300178Z This decorator also works with RRef helpers, i.e., . 2025-07-17T09:05:53.0300448Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2025-07-17T09:05:53.0300705Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2025-07-17T09:05:53.0300964Z :meth:`torch.distributed.rpc.RRef.remote`. 2025-07-17T09:05:53.0301120Z 2025-07-17T09:05:53.0301209Z >>> from torch.distributed import rpc 2025-07-17T09:05:53.0301416Z >>> 2025-07-17T09:05:53.0301594Z >>> # reuse the AsyncExecutionClass class above 2025-07-17T09:05:53.0301855Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-07-17T09:05:53.0302144Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2025-07-17T09:05:53.0302424Z >>> print(ret) # prints tensor([4., 4.]) 2025-07-17T09:05:53.0302629Z >>> 2025-07-17T09:05:53.0302817Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-07-17T09:05:53.0303114Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2025-07-17T09:05:53.0303401Z >>> print(ret) # prints tensor([4., 4.]) 2025-07-17T09:05:53.0303603Z >>> 2025-07-17T09:05:53.0303776Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-07-17T09:05:53.0304068Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2025-07-17T09:05:53.0304347Z >>> print(ret) # prints tensor([4., 4.]) 2025-07-17T09:05:53.0304490Z 2025-07-17T09:05:53.0304644Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:53.0304867Z 2025-07-17T09:05:53.0328586Z msg = Cannot scrape callname=construct_and_record_rdzv_event in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/elastic/events/__init__.py line=94. 2025-07-17T09:05:53.0329268Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:53.0329511Z 2025-07-17T09:05:53.0329650Z Initialize rendezvous event object and record its operations. 2025-07-17T09:05:53.0330040Z 2025-07-17T09:05:53.0330181Z Args: 2025-07-17T09:05:53.0330372Z run_id (str): The run id of the rendezvous. 2025-07-17T09:05:53.0330635Z message (str): The message describing the event. 2025-07-17T09:05:53.0330957Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2025-07-17T09:05:53.0331327Z name (str): Event name. (E.g. Current action being performed). 2025-07-17T09:05:53.0331726Z hostname (str): Hostname of the node. 2025-07-17T09:05:53.0331970Z pid (Optional[int]): The process id of the node. 2025-07-17T09:05:53.0332283Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2025-07-17T09:05:53.0332674Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2025-07-17T09:05:53.0333000Z rank (Optional[int]): The rank of the node, if known. 2025-07-17T09:05:53.0333230Z Returns: 2025-07-17T09:05:53.0333383Z None 2025-07-17T09:05:53.0333536Z Example: 2025-07-17T09:05:53.0333713Z >>> # See DynamicRendezvousHandler class 2025-07-17T09:05:53.0333933Z >>> def _record( 2025-07-17T09:05:53.0334104Z ... self, 2025-07-17T09:05:53.0334278Z ... message: str, 2025-07-17T09:05:53.0334486Z ... node_state: NodeState = NodeState.RUNNING, 2025-07-17T09:05:53.0334722Z ... rank: Optional[int] = None, 2025-07-17T09:05:53.0334924Z ... ) -> None: 2025-07-17T09:05:53.0335125Z ... construct_and_record_rdzv_event( 2025-07-17T09:05:53.0335385Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2025-07-17T09:05:53.0335644Z ... run_id=self._settings.run_id, 2025-07-17T09:05:53.0335870Z ... message=message, 2025-07-17T09:05:53.0336070Z ... node_state=node_state, 2025-07-17T09:05:53.0336285Z ... hostname=self._this_node.addr, 2025-07-17T09:05:53.0336513Z ... pid=self._this_node.pid, 2025-07-17T09:05:53.0336740Z ... local_id=self._this_node.local_id, 2025-07-17T09:05:53.0336979Z ... rank=rank, 2025-07-17T09:05:53.0337156Z ... ) 2025-07-17T09:05:53.0337255Z 2025-07-17T09:05:53.0337414Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:53.0337638Z 2025-07-17T09:05:53.0935238Z msg = Cannot scrape callname=_RemoteModule.__init__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=129. 2025-07-17T09:05:53.0935946Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:53.0936191Z 2025-07-17T09:05:53.0936343Z RemoteModule instance can only be created after RPC initialization. 2025-07-17T09:05:53.0936562Z 2025-07-17T09:05:53.0936690Z It creates a user-specified module on a specified remote node. 2025-07-17T09:05:53.0937024Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2025-07-17T09:05:53.0937310Z executed on the remote node. 2025-07-17T09:05:53.0937579Z It takes care of autograd recording to ensure the backward pass propagates 2025-07-17T09:05:53.0937893Z gradients back to the corresponding remote module. 2025-07-17T09:05:53.0938286Z It can be shared across processors using `RPC framework `__, 2025-07-17T09:05:53.0938679Z without incurring any overheads of copying the actual module, 2025-07-17T09:05:53.0938990Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2025-07-17T09:05:53.0939261Z pointing to the remote module. 2025-07-17T09:05:53.0939379Z 2025-07-17T09:05:53.0939517Z The arguments of ``forward_async`` and ``forward`` are the same as 2025-07-17T09:05:53.0939867Z the ``forward`` method of the module returned by the ``module_cls``. 2025-07-17T09:05:53.0940046Z 2025-07-17T09:05:53.0940246Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2025-07-17T09:05:53.0940485Z 2025-07-17T09:05:53.0940642Z Particularly, to create a hybrid model, typically the local modules should be 2025-07-17T09:05:53.0941570Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2025-07-17T09:05:53.0941925Z Hybrid Example: 2025-07-17T09:05:53.0942103Z >>> class HybridModel(nn.Module): 2025-07-17T09:05:53.0942336Z >>> def __init__(self) -> None: 2025-07-17T09:05:53.0942559Z >>> nn.Module.__init__(self) 2025-07-17T09:05:53.0942968Z >>> self.remote_embedding = RemoteModule(...) 2025-07-17T09:05:53.0943219Z >>> self.local_linear = nn.Linear(...) 2025-07-17T09:05:53.0943377Z 2025-07-17T09:05:53.0943506Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2025-07-17T09:05:53.0943841Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2025-07-17T09:05:53.0944185Z the generated ``RemoteModule`` will have 2 methods in signature of 2025-07-17T09:05:53.0944491Z ``def forward(input: Tensor) -> Tensor:`` and 2025-07-17T09:05:53.0944764Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2025-07-17T09:05:53.0944939Z 2025-07-17T09:05:53.0945022Z .. note:: 2025-07-17T09:05:53.0945205Z If the remote module is placed on a cuda device, 2025-07-17T09:05:53.0945584Z any input CPU tensors will be automatically moved to the same cuda device, 2025-07-17T09:05:53.0946030Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2025-07-17T09:05:53.0946316Z 2025-07-17T09:05:53.0946387Z Args: 2025-07-17T09:05:53.0946653Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-07-17T09:05:53.0947075Z The device can be a local device or a remote device specified by one of the following remote 2025-07-17T09:05:53.0947372Z formats: 2025-07-17T09:05:53.0947464Z 2025-07-17T09:05:53.0947570Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2025-07-17T09:05:53.0947828Z 2. "/" (ex: "trainer0/cuda:0"). 2025-07-17T09:05:53.0947985Z 2025-07-17T09:05:53.0948143Z In addition, the device field can be optional and the default value is "cpu". 2025-07-17T09:05:53.0948433Z module_cls (nn.Module): For example, 2025-07-17T09:05:53.0948641Z >>> class MyModule(nn.Module): 2025-07-17T09:05:53.0948843Z >>> def forward(input): 2025-07-17T09:05:53.0949051Z >>> return input + 1 2025-07-17T09:05:53.0949237Z >>> 2025-07-17T09:05:53.0949400Z >>> module_cls = MyModule 2025-07-17T09:05:53.0949649Z args (Sequence, optional): args to be passed to ``module_cls``. 2025-07-17T09:05:53.0949947Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2025-07-17T09:05:53.0950293Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2025-07-17T09:05:53.0950668Z to be created. The type object should be decorated by @torch.jit.interface. 2025-07-17T09:05:53.0951013Z If not provided, the generated RemoteModule is not torchscript-able. 2025-07-17T09:05:53.0951355Z Warning, this is an experimental API and susceptible to frequent changes. 2025-07-17T09:05:53.0951556Z 2025-07-17T09:05:53.0951625Z Returns: 2025-07-17T09:05:53.0951857Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-07-17T09:05:53.0952198Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2025-07-17T09:05:53.0952565Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-07-17T09:05:53.0952895Z on the user-provided module on the remote side. 2025-07-17T09:05:53.0953054Z 2025-07-17T09:05:53.0953117Z Example:: 2025-07-17T09:05:53.0953305Z Run the following code in two different processes: 2025-07-17T09:05:53.0953467Z 2025-07-17T09:05:53.0953544Z >>> # xdoctest: +SKIP("distributed") 2025-07-17T09:05:53.0953749Z >>> # On worker 0: 2025-07-17T09:05:53.0954002Z >>> import torch 2025-07-17T09:05:53.0954193Z >>> import torch.distributed.rpc as rpc 2025-07-17T09:05:53.0954477Z >>> from torch import nn, Tensor 2025-07-17T09:05:53.0954745Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-07-17T09:05:53.0955005Z >>> 2025-07-17T09:05:53.0955171Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-07-17T09:05:53.0955405Z >>> remote_linear_module = RemoteModule( 2025-07-17T09:05:53.0955790Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-07-17T09:05:53.0955990Z >>> ) 2025-07-17T09:05:53.0956146Z >>> input = torch.randn(128, 20) 2025-07-17T09:05:53.0956370Z >>> ret_fut = remote_linear_module.forward_async(input) 2025-07-17T09:05:53.0956601Z >>> ret = ret_fut.wait() 2025-07-17T09:05:53.0956783Z >>> rpc.shutdown() 2025-07-17T09:05:53.0956875Z 2025-07-17T09:05:53.0956947Z >>> # On worker 1: 2025-07-17T09:05:53.0957129Z >>> import torch 2025-07-17T09:05:53.0957308Z >>> import torch.distributed.rpc as rpc 2025-07-17T09:05:53.0957510Z >>> 2025-07-17T09:05:53.0957669Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-07-17T09:05:53.0957881Z >>> rpc.shutdown() 2025-07-17T09:05:53.0957982Z 2025-07-17T09:05:53.0958133Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:53.0958348Z 2025-07-17T09:05:53.0958789Z msg = Cannot scrape callname=_RemoteModule.init_from_module_rref in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=506. 2025-07-17T09:05:53.0959386Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:53.0959600Z 2025-07-17T09:05:53.0959797Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2025-07-17T09:05:53.0960046Z 2025-07-17T09:05:53.0960243Z This alternate initialization method can be particularly useful if we want to create multiple 2025-07-17T09:05:53.0960677Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2025-07-17T09:05:53.0960924Z 2025-07-17T09:05:53.0961097Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2025-07-17T09:05:53.0961436Z which is not supported. The recommended way is as follows: 2025-07-17T09:05:53.0961607Z 2025-07-17T09:05:53.0961692Z 1. the sender creates a RemoteModule; 2025-07-17T09:05:53.0961927Z 2. the sender sends its ``module_rref`` over RPC; 2025-07-17T09:05:53.0962301Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2025-07-17T09:05:53.0962565Z 2025-07-17T09:05:53.0962628Z Example:: 2025-07-17T09:05:53.0962806Z Run the following code in two different processes: 2025-07-17T09:05:53.0962969Z 2025-07-17T09:05:53.0963046Z >>> # xdoctest: +SKIP("distributed") 2025-07-17T09:05:53.0963242Z >>> # On worker 0: 2025-07-17T09:05:53.0963405Z >>> import torch 2025-07-17T09:05:53.0963584Z >>> import torch.distributed.rpc as rpc 2025-07-17T09:05:53.0963805Z >>> from torch import nn, Tensor 2025-07-17T09:05:53.0964078Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-07-17T09:05:53.0964331Z >>> 2025-07-17T09:05:53.0964500Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-07-17T09:05:53.0964723Z >>> remote_module = RemoteModule( 2025-07-17T09:05:53.0964928Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-07-17T09:05:53.0965137Z >>> ) 2025-07-17T09:05:53.0965277Z >>> 2025-07-17T09:05:53.0965435Z >>> remote_module1 = rpc.rpc_sync( 2025-07-17T09:05:53.0965659Z >>> "worker1/cpu", 2025-07-17T09:05:53.0965864Z >>> RemoteModule.init_from_module_rref, 2025-07-17T09:05:53.0966117Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2025-07-17T09:05:53.0966349Z >>> ) 2025-07-17T09:05:53.0966503Z >>> rpc.shutdown() 2025-07-17T09:05:53.0966606Z 2025-07-17T09:05:53.0966677Z >>> # On worker 1: 2025-07-17T09:05:53.0966926Z >>> import torch 2025-07-17T09:05:53.0967170Z >>> import torch.distributed.rpc as rpc 2025-07-17T09:05:53.0967379Z >>> 2025-07-17T09:05:53.0967556Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-07-17T09:05:53.0967774Z >>> rpc.shutdown() 2025-07-17T09:05:53.0967879Z 2025-07-17T09:05:53.0967951Z Args: 2025-07-17T09:05:53.0968222Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-07-17T09:05:53.0968757Z The device can be a local device or a remote device specified by one of the following remote 2025-07-17T09:05:53.0969067Z formats: 2025-07-17T09:05:53.0969160Z 2025-07-17T09:05:53.0969271Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2025-07-17T09:05:53.0969529Z 2. "/" (ex: "trainer0/cuda:0"). 2025-07-17T09:05:53.0969686Z 2025-07-17T09:05:53.0969846Z In addition, the device field can be optional and the default value is "cpu". 2025-07-17T09:05:53.0970209Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2025-07-17T09:05:53.0970496Z the created remote module. 2025-07-17T09:05:53.0970788Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2025-07-17T09:05:53.0971155Z to be created. The type object should be decorated by @torch.jit.interface. 2025-07-17T09:05:53.0971505Z If not provided, the generated RemoteModule is not torchscript-able. 2025-07-17T09:05:53.0971844Z Warning, this is an experimental API and susceptible to frequent changes. 2025-07-17T09:05:53.0972053Z 2025-07-17T09:05:53.0972115Z Returns: 2025-07-17T09:05:53.0972353Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-07-17T09:05:53.0972710Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2025-07-17T09:05:53.0973086Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-07-17T09:05:53.0973417Z on the user-provided module on the remote side. 2025-07-17T09:05:53.0973577Z 2025-07-17T09:05:53.0973725Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:53.0973940Z 2025-07-17T09:05:53.0974289Z msg = Cannot scrape callname=RemoteModule in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=598. 2025-07-17T09:05:53.0974850Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:53.0975070Z 2025-07-17T09:05:53.0975215Z A RemoteModule instance can only be created after RPC initialization. 2025-07-17T09:05:53.0975414Z 2025-07-17T09:05:53.0975544Z It creates a user-specified module on a specified remote node. 2025-07-17T09:05:53.0975873Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2025-07-17T09:05:53.0976163Z executed on the remote node. 2025-07-17T09:05:53.0976442Z It takes care of autograd recording to ensure the backward pass propagates 2025-07-17T09:05:53.0976764Z gradients back to the corresponding remote module. 2025-07-17T09:05:53.0976925Z 2025-07-17T09:05:53.0977070Z It generates two methods ``forward_async`` and ``forward`` based on the 2025-07-17T09:05:53.0977401Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2025-07-17T09:05:53.0977751Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2025-07-17T09:05:53.0978079Z and ``forward`` are the same as the ``forward`` method of the module 2025-07-17T09:05:53.0978348Z returned by the ``module_cls``. 2025-07-17T09:05:53.0978490Z 2025-07-17T09:05:53.0978613Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2025-07-17T09:05:53.0978945Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2025-07-17T09:05:53.0979300Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2025-07-17T09:05:53.0979576Z 2025-07-17T09:05:53.0979663Z | ``def forward(input: Tensor) -> Tensor:`` 2025-07-17T09:05:53.0979970Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2025-07-17T09:05:53.0980144Z 2025-07-17T09:05:53.0980204Z Args: 2025-07-17T09:05:53.0980464Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-07-17T09:05:53.0981005Z The format should be "/", where the device field can be parsed as torch.device type. 2025-07-17T09:05:53.0981369Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2025-07-17T09:05:53.0981678Z In addition, the device field can be optional and the default value is "cpu". 2025-07-17T09:05:53.0982036Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2025-07-17T09:05:53.0982237Z 2025-07-17T09:05:53.0982323Z >>> class MyModule(nn.Module): 2025-07-17T09:05:53.0982532Z >>> def forward(input): 2025-07-17T09:05:53.0982731Z >>> return input + 1 2025-07-17T09:05:53.0982918Z >>> 2025-07-17T09:05:53.0983079Z >>> module_cls = MyModule 2025-07-17T09:05:53.0983212Z 2025-07-17T09:05:53.0983337Z args (Sequence, optional): args to be passed to ``module_cls``. 2025-07-17T09:05:53.0983637Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2025-07-17T09:05:53.0983821Z 2025-07-17T09:05:53.0983884Z Returns: 2025-07-17T09:05:53.0984121Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-07-17T09:05:53.0984477Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2025-07-17T09:05:53.0984839Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-07-17T09:05:53.0985161Z on the user-provided module on the remote side. 2025-07-17T09:05:53.0985382Z 2025-07-17T09:05:53.0985458Z Example:: 2025-07-17T09:05:53.0985648Z Run the following code in two different processes: 2025-07-17T09:05:53.0985806Z 2025-07-17T09:05:53.0985891Z >>> # xdoctest: +SKIP("distributed") 2025-07-17T09:05:53.0986098Z >>> # On worker 0: 2025-07-17T09:05:53.0986263Z >>> import torch 2025-07-17T09:05:53.0986445Z >>> import torch.distributed.rpc as rpc 2025-07-17T09:05:53.0986664Z >>> from torch import nn, Tensor 2025-07-17T09:05:53.0986933Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-07-17T09:05:53.0987191Z >>> 2025-07-17T09:05:53.0987373Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-07-17T09:05:53.0987608Z >>> remote_linear_module = RemoteModule( 2025-07-17T09:05:53.0987837Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-07-17T09:05:53.0988039Z >>> ) 2025-07-17T09:05:53.0988197Z >>> input = torch.randn(128, 20) 2025-07-17T09:05:53.0988422Z >>> ret_fut = remote_linear_module.forward_async(input) 2025-07-17T09:05:53.0988664Z >>> ret = ret_fut.wait() 2025-07-17T09:05:53.0988840Z >>> rpc.shutdown() 2025-07-17T09:05:53.0988947Z 2025-07-17T09:05:53.0989008Z >>> # On worker 1: 2025-07-17T09:05:53.0989177Z >>> import torch 2025-07-17T09:05:53.0989365Z >>> import torch.distributed.rpc as rpc 2025-07-17T09:05:53.0989571Z >>> 2025-07-17T09:05:53.0989735Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-07-17T09:05:53.0989954Z >>> rpc.shutdown() 2025-07-17T09:05:53.0990044Z 2025-07-17T09:05:53.0990176Z Furthermore, a more practical example that is combined with 2025-07-17T09:05:53.0990639Z `DistributedDataParallel `__ (DDP) 2025-07-17T09:05:53.0991181Z can be found in this `tutorial `__. 2025-07-17T09:05:53.0991452Z 2025-07-17T09:05:53.0991606Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:53.0991814Z 2025-07-17T09:05:53.6903104Z msg = Cannot scrape callname=assoc_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=245. 2025-07-17T09:05:53.6904632Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:53.6905186Z Return a new dict with new, potentially nested, key value pair 2025-07-17T09:05:53.6905519Z 2025-07-17T09:05:53.6905589Z >>> purchase = { 2025-07-17T09:05:53.6905773Z ... "name": "Alice", 2025-07-17T09:05:53.6906229Z ... "order": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-07-17T09:05:53.6906505Z ... "credit card": "5555-1234-1234-1234", 2025-07-17T09:05:53.6906722Z ... } 2025-07-17T09:05:53.6906944Z >>> assoc_in(purchase, ["order", "costs"], [0.25, 1.00]) # doctest: +SKIP 2025-07-17T09:05:53.6907219Z {'credit card': '5555-1234-1234-1234', 2025-07-17T09:05:53.6907415Z 'name': 'Alice', 2025-07-17T09:05:53.6907636Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2025-07-17T09:05:53.6907864Z 2025-07-17T09:05:53.6908128Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:53.6908353Z 2025-07-17T09:05:53.6908742Z msg = Cannot scrape callname=update_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=261. 2025-07-17T09:05:53.6909323Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:53.6909650Z Update value in a (potentially) nested dictionary 2025-07-17T09:05:53.6909814Z 2025-07-17T09:05:53.6909882Z inputs: 2025-07-17T09:05:53.6910060Z d - dictionary on which to operate 2025-07-17T09:05:53.6910339Z keys - list or tuple giving the location of the value to be changed in d 2025-07-17T09:05:53.6910634Z func - function to operate on that value 2025-07-17T09:05:53.6910777Z 2025-07-17T09:05:53.6910911Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2025-07-17T09:05:53.6911241Z original dictionary with v replaced by func(v), but does not mutate the 2025-07-17T09:05:53.6911525Z original dictionary. 2025-07-17T09:05:53.6911637Z 2025-07-17T09:05:53.6911778Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2025-07-17T09:05:53.6912107Z specified by the keys, with the innermost value set to func(default). 2025-07-17T09:05:53.6912312Z 2025-07-17T09:05:53.6912390Z >>> inc = lambda x: x + 1 2025-07-17T09:05:53.6912600Z >>> update_in({"a": 0}, ["a"], inc) 2025-07-17T09:05:53.6912797Z {'a': 1} 2025-07-17T09:05:53.6912894Z 2025-07-17T09:05:53.6912961Z >>> transaction = { 2025-07-17T09:05:53.6913138Z ... "name": "Alice", 2025-07-17T09:05:53.6913379Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-07-17T09:05:53.6913649Z ... "credit card": "5555-1234-1234-1234", 2025-07-17T09:05:53.6913851Z ... } 2025-07-17T09:05:53.6914076Z >>> update_in(transaction, ["purchase", "costs"], sum) # doctest: +SKIP 2025-07-17T09:05:53.6914353Z {'credit card': '5555-1234-1234-1234', 2025-07-17T09:05:53.6914556Z 'name': 'Alice', 2025-07-17T09:05:53.6914760Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2025-07-17T09:05:53.6914941Z 2025-07-17T09:05:53.6915024Z >>> # updating a value when k0 is not in d 2025-07-17T09:05:53.6915261Z >>> update_in({}, [1, 2, 3], str, default="bar") 2025-07-17T09:05:53.6915474Z {1: {2: {3: 'bar'}}} 2025-07-17T09:05:53.6915665Z >>> update_in({1: "foo"}, [2, 3, 4], inc, 0) 2025-07-17T09:05:53.6915875Z {1: 'foo', 2: {3: {4: 1}}} 2025-07-17T09:05:53.6916054Z 2025-07-17T09:05:53.6916299Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:53.6916513Z 2025-07-17T09:05:53.6916937Z msg = Cannot scrape callname=get_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=320. 2025-07-17T09:05:53.6917514Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:53.6917942Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2025-07-17T09:05:53.6918181Z 2025-07-17T09:05:53.6918298Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2025-07-17T09:05:53.6918599Z ``no_default`` is specified, then it raises KeyError or IndexError. 2025-07-17T09:05:53.6918790Z 2025-07-17T09:05:53.6919023Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2025-07-17T09:05:53.6919313Z structures such as dictionaries and lists. 2025-07-17T09:05:53.6919456Z 2025-07-17T09:05:53.6919535Z >>> transaction = { 2025-07-17T09:05:53.6919715Z ... "name": "Alice", 2025-07-17T09:05:53.6919953Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-07-17T09:05:53.6920227Z ... "credit card": "5555-1234-1234-1234", 2025-07-17T09:05:53.6920431Z ... } 2025-07-17T09:05:53.6920606Z >>> get_in(["purchase", "items", 0], transaction) 2025-07-17T09:05:53.6920829Z 'Apple' 2025-07-17T09:05:53.6920992Z >>> get_in(["name"], transaction) 2025-07-17T09:05:53.6921194Z 'Alice' 2025-07-17T09:05:53.6921359Z >>> get_in(["purchase", "total"], transaction) 2025-07-17T09:05:53.6921606Z >>> get_in(["purchase", "items", "apple"], transaction) 2025-07-17T09:05:53.6921866Z >>> get_in(["purchase", "items", 10], transaction) 2025-07-17T09:05:53.6922110Z >>> get_in(["purchase", "total"], transaction, 0) 2025-07-17T09:05:53.6922323Z 0 2025-07-17T09:05:53.6922490Z >>> get_in(["y"], {}, no_default=True) 2025-07-17T09:05:53.6922707Z Traceback (most recent call last): 2025-07-17T09:05:53.6922897Z ... 2025-07-17T09:05:53.6923051Z KeyError: 'y' 2025-07-17T09:05:53.6923155Z 2025-07-17T09:05:53.6923217Z See Also: 2025-07-17T09:05:53.6923379Z itertoolz.get 2025-07-17T09:05:53.6923550Z operator.getitem 2025-07-17T09:05:53.6923731Z 2025-07-17T09:05:53.6923968Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:53.6924185Z 2025-07-17T09:05:53.6924566Z msg = Cannot scrape callname=groupby in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=373. 2025-07-17T09:05:53.6925150Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:53.6925451Z Group a collection by a key function 2025-07-17T09:05:53.6925579Z 2025-07-17T09:05:53.6925697Z >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"] 2025-07-17T09:05:53.6925953Z >>> groupby(len, names) # doctest: +SKIP 2025-07-17T09:05:53.6926203Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2025-07-17T09:05:53.6926364Z 2025-07-17T09:05:53.6926447Z >>> iseven = lambda x: x % 2 == 0 2025-07-17T09:05:53.6926677Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2025-07-17T09:05:53.6926925Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2025-07-17T09:05:53.6927063Z 2025-07-17T09:05:53.6927154Z Non-callable keys imply grouping on a member. 2025-07-17T09:05:53.6927317Z 2025-07-17T09:05:53.6927383Z >>> groupby( 2025-07-17T09:05:53.6927554Z ... "gender", 2025-07-17T09:05:53.6927721Z ... [ 2025-07-17T09:05:53.6927896Z ... {"name": "Alice", "gender": "F"}, 2025-07-17T09:05:53.6928119Z ... {"name": "Bob", "gender": "M"}, 2025-07-17T09:05:53.6928346Z ... {"name": "Charlie", "gender": "M"}, 2025-07-17T09:05:53.6928562Z ... ], 2025-07-17T09:05:53.6928721Z ... ) # doctest:+SKIP 2025-07-17T09:05:53.6928896Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2025-07-17T09:05:53.6929108Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2025-07-17T09:05:53.6929315Z {'gender': 'M', 'name': 'Charlie'}]} 2025-07-17T09:05:53.6929458Z 2025-07-17T09:05:53.6929545Z Not to be confused with ``itertools.groupby`` 2025-07-17T09:05:53.6929695Z 2025-07-17T09:05:53.6929756Z See Also: 2025-07-17T09:05:53.6929987Z countby 2025-07-17T09:05:53.6930141Z 2025-07-17T09:05:53.6930432Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:53.6930642Z 2025-07-17T09:05:53.8313572Z msg = Cannot scrape callname=Conv1d in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py line=354. 2025-07-17T09:05:53.8314855Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:53.8315248Z Applies a 1D convolution over a quantized input signal composed of 2025-07-17T09:05:53.8315525Z several quantized input planes. 2025-07-17T09:05:53.8315664Z 2025-07-17T09:05:53.8315838Z For details on input arguments, parameters, and implementation see 2025-07-17T09:05:53.8316116Z :class:`~torch.nn.Conv1d`. 2025-07-17T09:05:53.8316238Z 2025-07-17T09:05:53.8316332Z .. note:: 2025-07-17T09:05:53.8316555Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2025-07-17T09:05:53.8316747Z 2025-07-17T09:05:53.8316818Z .. note:: 2025-07-17T09:05:53.8317026Z Only `torch.quint8` is supported for the input data type. 2025-07-17T09:05:53.8317212Z 2025-07-17T09:05:53.8317215Z 2025-07-17T09:05:53.8317281Z Attributes: 2025-07-17T09:05:53.8317513Z weight (Tensor): packed tensor derived from the learnable weight 2025-07-17T09:05:53.8317784Z parameter. 2025-07-17T09:05:53.8318017Z scale (Tensor): scalar for the output scale 2025-07-17T09:05:53.8318290Z zero_point (Tensor): scalar for the output zero point 2025-07-17T09:05:53.8318466Z 2025-07-17T09:05:53.8318573Z See :class:`~torch.nn.Conv1d` for other attributes. 2025-07-17T09:05:53.8318735Z 2025-07-17T09:05:53.8318802Z Examples:: 2025-07-17T09:05:53.8318901Z 2025-07-17T09:05:53.8319001Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2025-07-17T09:05:53.8319257Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2025-07-17T09:05:53.8319487Z >>> input = torch.randn(20, 16, 100) 2025-07-17T09:05:53.8319708Z >>> # quantize input to quint8 2025-07-17T09:05:53.8319919Z >>> # xdoctest: +SKIP 2025-07-17T09:05:53.8320174Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2025-07-17T09:05:53.8320469Z ... dtype=torch.quint8) 2025-07-17T09:05:53.8320687Z >>> output = m(q_input) 2025-07-17T09:05:53.8320816Z 2025-07-17T09:05:53.8320881Z 2025-07-17T09:05:53.8321122Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:53.8321345Z 2025-07-17T09:05:53.8365944Z msg = Cannot scrape callname=LSTM in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/rnn.py line=12. 2025-07-17T09:05:53.8366700Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:53.8367025Z A quantized long short-term memory (LSTM). 2025-07-17T09:05:53.8367199Z 2025-07-17T09:05:53.8367373Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2025-07-17T09:05:53.8367617Z 2025-07-17T09:05:53.8367687Z Attributes: 2025-07-17T09:05:53.8367881Z layers : instances of the `_LSTMLayer` 2025-07-17T09:05:53.8368031Z 2025-07-17T09:05:53.8368103Z .. note:: 2025-07-17T09:05:53.8368333Z To access the weights and biases, you need to access them per layer. 2025-07-17T09:05:53.8368670Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2025-07-17T09:05:53.8368840Z 2025-07-17T09:05:53.8368912Z Examples:: 2025-07-17T09:05:53.8369080Z >>> # xdoctest: +SKIP 2025-07-17T09:05:53.8369283Z >>> custom_module_config = { 2025-07-17T09:05:53.8369519Z ... 'float_to_observed_custom_module_class': { 2025-07-17T09:05:53.8369760Z ... nn.LSTM: nn.quantizable.LSTM, 2025-07-17T09:05:53.8369970Z ... }, 2025-07-17T09:05:53.8370160Z ... 'observed_to_quantized_custom_module_class': { 2025-07-17T09:05:53.8370626Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2025-07-17T09:05:53.8371000Z ... } 2025-07-17T09:05:53.8371158Z ... } 2025-07-17T09:05:53.8371387Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2025-07-17T09:05:53.8371711Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2025-07-17T09:05:53.8371958Z 2025-07-17T09:05:53.8372336Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:53.8372555Z 2025-07-17T09:05:54.0145742Z msg = Cannot scrape callname=ModelReport in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report.py line=24. 2025-07-17T09:05:54.0146589Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:54.0146817Z 2025-07-17T09:05:54.0147015Z The ModelReport class aims to provide users an easy way to diagnose issues that they run into 2025-07-17T09:05:54.0147475Z with their models. The class works with all traceable GraphModules to help diagnose issues, 2025-07-17T09:05:54.0147921Z though the requirements on the type of model more-so depends on the specific report the user 2025-07-17T09:05:54.0148346Z is trying to generate. With respect to the reports, the ModelReport class is initialized with 2025-07-17T09:05:54.0148781Z a set of Detector classes, each of which generate reports on quantization configuration 2025-07-17T09:05:54.0149096Z issues a use might have. 2025-07-17T09:05:54.0149215Z 2025-07-17T09:05:54.0149315Z Currently supports generating reports on: 2025-07-17T09:05:54.0149609Z - Suggestions for per-channel vs. per-tensor quantization (nn.Module) 2025-07-17T09:05:54.0149986Z - Suggestions for dynamic vs static quantization for linear layers (Graph Modules) 2025-07-17T09:05:54.0150391Z - Suggestions for input-weight equalization for linear and conv layers (Graph Modules) 2025-07-17T09:05:54.0150767Z - Suggestions for outlier detection for all layers (Graph Modules) 2025-07-17T09:05:54.0150967Z 2025-07-17T09:05:54.0151200Z The ModelReport class has the primary functionality of inserting observers (primarily the ModelReportObserver) 2025-07-17T09:05:54.0151699Z where needed for each detector to gather the information it needs, and then after calibration, the ModelReport 2025-07-17T09:05:54.0152193Z class compiles the report generated by each Detector class into a single report to return to the user. It also 2025-07-17T09:05:54.0152607Z has the capability to remove all the observers it inserted as well. 2025-07-17T09:05:54.0152805Z 2025-07-17T09:05:54.0152979Z * :attr:`_model` The model we wish to generate the report for. Must be a traceable GraphModule 2025-07-17T09:05:54.0153214Z 2025-07-17T09:05:54.0153430Z * :attr:`_desired_report_detectors` The set of Detectors representing desired reports from the ModelReport class 2025-07-17T09:05:54.0153897Z Make sure that these are all unique types of detectors [do not have more than 1 of the same class] 2025-07-17T09:05:54.0154159Z 2025-07-17T09:05:54.0154356Z * :attr:`_desired_detector_names` The set of detector names of the _desired_report_detectors. 2025-07-17T09:05:54.0154734Z This set is generated by calling the get_detector_name() of each detector 2025-07-17T09:05:54.0154937Z 2025-07-17T09:05:54.0155135Z * :attr:`_detector_name_to_observer_fqns` The mapping from each detector to fqns of observers of interest 2025-07-17T09:05:54.0155583Z The purpose of this is to keep track of what observers were inserted for each detector, so that they 2025-07-17T09:05:54.0155912Z can be removed at the end if desired 2025-07-17T09:05:54.0156061Z 2025-07-17T09:05:54.0156252Z * :attr:`_prepared_flag` A boolean flag that keeps track of whether we have prepared the model or not 2025-07-17T09:05:54.0156642Z This is to ensure we only insert observers once with the ModelReport instance 2025-07-17T09:05:54.0156857Z 2025-07-17T09:05:54.0157013Z * :attr:`_removed_observers` A boolean to track if we have removed observers already 2025-07-17T09:05:54.0157789Z The purpose is to ensure we don't attempt to remove observers twice with the same ModelReport 2025-07-17T09:05:54.0158217Z instance. This also allows the functionality where we can generate the report multiple times 2025-07-17T09:05:54.0158577Z as long as we haven't removed the observers yet. 2025-07-17T09:05:54.0158732Z 2025-07-17T09:05:54.0158806Z Note: 2025-07-17T09:05:54.0159285Z This class was initially designed to work with the Fx Graph Mode workflow in mind. However, 2025-07-17T09:05:54.0159726Z full functionality is available as long as there is a traceable GraphModule that is being used. 2025-07-17T09:05:54.0160171Z One method to get a traceable GraphModule without going through the Fx workflow is to use 2025-07-17T09:05:54.0160507Z the QuantizationTracer class. 2025-07-17T09:05:54.0160634Z 2025-07-17T09:05:54.0160722Z General Flow for Fx workflow: 2025-07-17T09:05:54.0161087Z 1.) Initialize ModelReport object with reports of interest by passing in initialized detector objects and model 2025-07-17T09:05:54.0161470Z 2.) Prepare your model with prepare_fx 2025-07-17T09:05:54.0161765Z 3.) Call model_report.prepare_detailed_calibration to add relevant observers 2025-07-17T09:05:54.0162061Z 4.) Calibrate your model with data 2025-07-17T09:05:54.0162399Z 5.) Call model_report.generate_report on your model to generate report and optionally remove added observers 2025-07-17T09:05:54.0162732Z Optional 2025-07-17T09:05:54.0162985Z 6.) Call model_report.generate_visualizer to get a ModelReportVisualizer instance 2025-07-17T09:05:54.0163367Z 7.) To help in parsing report information and debugging, view report info as a: 2025-07-17T09:05:54.0163644Z - Table 2025-07-17T09:05:54.0163817Z - Histogram 2025-07-17T09:05:54.0163981Z - Line plot 2025-07-17T09:05:54.0164263Z 8.) Call model_report.generate_qconfigs to generate the qconfigs based on the report suggestions 2025-07-17T09:05:54.0164514Z 2025-07-17T09:05:54.0164607Z Example (with QuantizationTracer): 2025-07-17T09:05:54.0164810Z >>> # xdoctest: +SKIP 2025-07-17T09:05:54.0164995Z >>> # get the necessary qconfig 2025-07-17T09:05:54.0165210Z >>> config = PrepareCustomConfig() 2025-07-17T09:05:54.0165445Z >>> skipped_module_names, skipped_module_classes = ( 2025-07-17T09:05:54.0165712Z ... get_skipped_module_name_and_classes(config, False) 2025-07-17T09:05:54.0165951Z ... ) 2025-07-17T09:05:54.0166041Z 2025-07-17T09:05:54.0166130Z >>> # initialize our model and get GraphModule 2025-07-17T09:05:54.0166352Z >>> model = SomeModel() 2025-07-17T09:05:54.0166622Z >>> tracer = QuantizationTracer(skipped_module_names, skipped_module_classes) 2025-07-17T09:05:54.0166951Z >>> graph_module = GraphModule(model, tracer.trace(model)) 2025-07-17T09:05:54.0167110Z 2025-07-17T09:05:54.0167215Z >>> # get our set of detectors and ModelReport instance 2025-07-17T09:05:54.0167448Z >>> detector_set = set( 2025-07-17T09:05:54.0167626Z ... [ 2025-07-17T09:05:54.0167807Z ... DynamicStaticDetector(tolerance=0.5), 2025-07-17T09:05:54.0168091Z ... InputWeightEqualizationDetector(ratio_threshold=0.7), 2025-07-17T09:05:54.0168340Z ... ] 2025-07-17T09:05:54.0168485Z ... ) 2025-07-17T09:05:54.0168704Z >>> tracer_reporter = ModelReport(graph_module, tracer_detector_set) 2025-07-17T09:05:54.0168900Z 2025-07-17T09:05:54.0169005Z >>> # now we insert the observers and calibrate the model 2025-07-17T09:05:54.0169322Z >>> tracer_model_with_observers = tracer_reporter.prepare_detailed_calibration() 2025-07-17T09:05:54.0169638Z >>> for i in range(num_callibration_batches): 2025-07-17T09:05:54.0169882Z >>> example_input = get_callibration_input() 2025-07-17T09:05:54.0170124Z >>> tracer_model_with_observers(example_input) 2025-07-17T09:05:54.0170274Z 2025-07-17T09:05:54.0170442Z >>> # finally we generate the reports and optionally remove the observers we inserted 2025-07-17T09:05:54.0170847Z >>> reports = tracer_reporter.generate_model_report( 2025-07-17T09:05:54.0171146Z ... remove_inserted_observers=True 2025-07-17T09:05:54.0171345Z ... ) 2025-07-17T09:05:54.0171424Z 2025-07-17T09:05:54.0171561Z >>> # Optional: we can generate the qconfig mapping based on the suggestions 2025-07-17T09:05:54.0171871Z >>> qconfigs = model_report.generate_qconfig_mapping() 2025-07-17T09:05:54.0172157Z 2025-07-17T09:05:54.0172303Z >>> # Optional: we can generate the equalization mapping based on the suggestions 2025-07-17T09:05:54.0172626Z >>> qconfigs = model_report.generate_equalization_mapping() 2025-07-17T09:05:54.0172804Z 2025-07-17T09:05:54.0172970Z >>> # Optional: we get a ModelReportVisualizer instance to do any visualizations desired 2025-07-17T09:05:54.0173336Z >>> model_report_visualizer = tracer_reporter.generate_visualizer() 2025-07-17T09:05:54.0173534Z 2025-07-17T09:05:54.0173537Z 2025-07-17T09:05:54.0173697Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:54.0173925Z 2025-07-17T09:05:54.0272237Z msg = Cannot scrape callname=ModelReportVisualizer.generate_filtered_tables in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=301. 2025-07-17T09:05:54.0273086Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:54.0273531Z 2025-07-17T09:05:54.0273807Z Takes in optional filter values and generates two tables with desired information. 2025-07-17T09:05:54.0274041Z 2025-07-17T09:05:54.0274187Z The generated tables are presented in both a list-of-lists format 2025-07-17T09:05:54.0274376Z 2025-07-17T09:05:54.0274523Z The reason for the two tables are that they handle different things: 2025-07-17T09:05:54.0274819Z 1.) the first table handles all tensor level information 2025-07-17T09:05:54.0275129Z 2.) the second table handles and displays all channel based information 2025-07-17T09:05:54.0275338Z 2025-07-17T09:05:54.0275535Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2025-07-17T09:05:54.0275982Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2025-07-17T09:05:54.0276452Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2025-07-17T09:05:54.0276724Z 2025-07-17T09:05:54.0276794Z Tensor table columns: 2025-07-17T09:05:54.0277056Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2025-07-17T09:05:54.0277489Z ---- --------- --------- --------- --------- --------- 2025-07-17T09:05:54.0277758Z 2025-07-17T09:05:54.0277866Z Per-Channel table columns: 2025-07-17T09:05:54.0278250Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2025-07-17T09:05:54.0278677Z ---- --------- ------- --------- --------- --------- --------- 2025-07-17T09:05:54.0278856Z 2025-07-17T09:05:54.0278932Z Args: 2025-07-17T09:05:54.0279195Z feature_filter (str, optional): Filters the features presented to only those that 2025-07-17T09:05:54.0279504Z contain this filter substring 2025-07-17T09:05:54.0279750Z Default = "", results in all the features being printed 2025-07-17T09:05:54.0280081Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-07-17T09:05:54.0280457Z Default = "", results in all the modules in the reports to be visible in the table 2025-07-17T09:05:54.0280668Z 2025-07-17T09:05:54.0280762Z Returns a dictionary with two keys: 2025-07-17T09:05:54.0281003Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2025-07-17T09:05:54.0281278Z "tensor_level_info", "channel_level_info" 2025-07-17T09:05:54.0281508Z Each key maps to a tuple with: 2025-07-17T09:05:54.0281731Z A list of the headers of each table 2025-07-17T09:05:54.0282171Z A list of lists containing the table information row by row 2025-07-17T09:05:54.0282542Z The 0th index row will contain the headers of the columns 2025-07-17T09:05:54.0282795Z The rest of the rows will contain data 2025-07-17T09:05:54.0282941Z 2025-07-17T09:05:54.0283020Z Example Use: 2025-07-17T09:05:54.0283210Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T09:05:54.0283601Z >>> mod_report_visualizer.generate_filtered_tables( 2025-07-17T09:05:54.0283888Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-07-17T09:05:54.0284243Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2025-07-17T09:05:54.0284474Z 2025-07-17T09:05:54.0284639Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:54.0284855Z 2025-07-17T09:05:54.0285381Z msg = Cannot scrape callname=ModelReportVisualizer.generate_table_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=399. 2025-07-17T09:05:54.0286105Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:54.0286324Z 2025-07-17T09:05:54.0286501Z Takes in optional filter values and prints out formatted tables of the information. 2025-07-17T09:05:54.0286727Z 2025-07-17T09:05:54.0286946Z The reason for the two tables printed out instead of one large one are that they handle different things: 2025-07-17T09:05:54.0287325Z 1.) the first table handles all tensor level information 2025-07-17T09:05:54.0287620Z 2.) the second table handles and displays all channel based information 2025-07-17T09:05:54.0287822Z 2025-07-17T09:05:54.0288047Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2025-07-17T09:05:54.0288496Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2025-07-17T09:05:54.0288959Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2025-07-17T09:05:54.0289235Z 2025-07-17T09:05:54.0289304Z Tensor table columns: 2025-07-17T09:05:54.0289533Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2025-07-17T09:05:54.0289816Z ---- --------- --------- --------- --------- --------- 2025-07-17T09:05:54.0289981Z 2025-07-17T09:05:54.0290063Z Per-Channel table columns: 2025-07-17T09:05:54.0290188Z 2025-07-17T09:05:54.0290322Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2025-07-17T09:05:54.0290628Z ---- --------- ------- --------- --------- --------- --------- 2025-07-17T09:05:54.0290799Z 2025-07-17T09:05:54.0290863Z Args: 2025-07-17T09:05:54.0291112Z feature_filter (str, optional): Filters the features presented to only those that 2025-07-17T09:05:54.0291412Z contain this filter substring 2025-07-17T09:05:54.0291664Z Default = "", results in all the features being printed 2025-07-17T09:05:54.0292001Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-07-17T09:05:54.0292370Z Default = "", results in all the modules in the reports to be visible in the table 2025-07-17T09:05:54.0292578Z 2025-07-17T09:05:54.0292652Z Example Use: 2025-07-17T09:05:54.0292825Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T09:05:54.0293082Z >>> mod_report_visualizer.generate_table_visualization( 2025-07-17T09:05:54.0293385Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-07-17T09:05:54.0293651Z ... ) 2025-07-17T09:05:54.0293859Z >>> # prints out neatly formatted table with per_channel_min info 2025-07-17T09:05:54.0294122Z >>> # for all modules in block 1 of the model 2025-07-17T09:05:54.0294275Z 2025-07-17T09:05:54.0294427Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:54.0294641Z 2025-07-17T09:05:54.0295265Z msg = Cannot scrape callname=ModelReportVisualizer.generate_plot_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=564. 2025-07-17T09:05:54.0296086Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:54.0296310Z 2025-07-17T09:05:54.0296453Z Takes in a feature and optional module_filter and plots of the desired data. 2025-07-17T09:05:54.0302360Z 2025-07-17T09:05:54.0302553Z For per channel features, it averages the value across the channels and plots a point 2025-07-17T09:05:54.0302950Z per module. The reason for this is that for models with hundreds of channels, it can 2025-07-17T09:05:54.0303341Z be hard to differentiate one channel line from another, and so the point of generating 2025-07-17T09:05:54.0303728Z a single average point per module is to give a sense of general trends that encourage 2025-07-17T09:05:54.0304015Z further deep dives. 2025-07-17T09:05:54.0304133Z 2025-07-17T09:05:54.0304195Z Note: 2025-07-17T09:05:54.0304444Z Only features in the report that have tensor value data are plottable by this class 2025-07-17T09:05:54.0304780Z When the tensor information is plotted, it will plot: 2025-07-17T09:05:54.0305038Z idx as the x val, feature value as the y_val 2025-07-17T09:05:54.0305411Z When the channel information is plotted, it will plot: 2025-07-17T09:05:54.0305747Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2025-07-17T09:05:54.0306112Z The reason for this is that we want to be able to compare values across the 2025-07-17T09:05:54.0306456Z channels for same layer, and it will be hard if values are staggered by idx 2025-07-17T09:05:54.0306766Z This means each module is represented by only 1 x value 2025-07-17T09:05:54.0306999Z Args: 2025-07-17T09:05:54.0307221Z feature_filter (str): Filters the features presented to only those that 2025-07-17T09:05:54.0307497Z contain this filter substring 2025-07-17T09:05:54.0307792Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-07-17T09:05:54.0308153Z Default = "", results in all the modules in the reports to be visible in the table 2025-07-17T09:05:54.0308368Z 2025-07-17T09:05:54.0308430Z Example Use: 2025-07-17T09:05:54.0308624Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T09:05:54.0308872Z >>> mod_report_visualizer.generate_plot_visualization( 2025-07-17T09:05:54.0309154Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-07-17T09:05:54.0309396Z ... ) 2025-07-17T09:05:54.0309599Z >>> # outputs line plot of per_channel_min information for all 2025-07-17T09:05:54.0309887Z >>> # modules in block1 of model each channel gets it's own line, 2025-07-17T09:05:54.0310161Z >>> # and it's plotted across the in-order modules on the x-axis 2025-07-17T09:05:54.0310335Z 2025-07-17T09:05:54.0310491Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:54.0310708Z 2025-07-17T09:05:54.0311226Z msg = Cannot scrape callname=ModelReportVisualizer.generate_histogram_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=643. 2025-07-17T09:05:54.0311951Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:54.0312176Z 2025-07-17T09:05:54.0312346Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2025-07-17T09:05:54.0312575Z 2025-07-17T09:05:54.0312635Z Note: 2025-07-17T09:05:54.0312875Z Only features in the report that have tensor value data can be viewed as a histogram 2025-07-17T09:05:54.0313256Z If you want to plot a histogram from all the channel values of a specific feature for 2025-07-17T09:05:54.0313624Z a specific model, make sure to specify both the model and the feature properly 2025-07-17T09:05:54.0314138Z in the filters and you should be able to see a distribution of the channel data 2025-07-17T09:05:54.0314416Z 2025-07-17T09:05:54.0326013Z Args: 2025-07-17T09:05:54.0326392Z feature_filter (str, optional): Filters the features presented to only those that 2025-07-17T09:05:54.0326834Z contain this filter substring 2025-07-17T09:05:54.0327200Z Default = "", results in all the features being printed 2025-07-17T09:05:54.0327778Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-07-17T09:05:54.0328156Z Default = "", results in all the modules in the reports to be visible in the table 2025-07-17T09:05:54.0328512Z num_bins (int, optional): The number of bins to create the histogram with 2025-07-17T09:05:54.0328848Z Default = 10, the values will be split into 10 equal sized bins 2025-07-17T09:05:54.0329028Z 2025-07-17T09:05:54.0329107Z Example Use: 2025-07-17T09:05:54.0329273Z >>> # xdoctest: +SKIP 2025-07-17T09:05:54.0329587Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2025-07-17T09:05:54.0329983Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-07-17T09:05:54.0330246Z ... ) 2025-07-17T09:05:54.0330509Z # outputs histogram of per_channel_min information for all modules in block1 of model 2025-07-17T09:05:54.0330912Z information is gathered across all channels for all modules in block 1 for the 2025-07-17T09:05:54.0331273Z per_channel_min and is displayed in a histogram of equally sized bins 2025-07-17T09:05:54.0331479Z 2025-07-17T09:05:54.0331633Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:54.0331855Z 2025-07-17T09:05:54.0388876Z msg = Cannot scrape callname=DTypeConfig in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/backend_config/backend_config.py line=181. 2025-07-17T09:05:54.0389683Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:54.0389948Z 2025-07-17T09:05:54.0390109Z Config object that specifies the supported data types passed as arguments to 2025-07-17T09:05:54.0390495Z quantize ops in the reference model spec, for input and output activations, 2025-07-17T09:05:54.0390790Z weights, and biases. 2025-07-17T09:05:54.0390896Z 2025-07-17T09:05:54.0391012Z For example, consider the following reference model: 2025-07-17T09:05:54.0391185Z 2025-07-17T09:05:54.0391312Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2025-07-17T09:05:54.0391479Z 2025-07-17T09:05:54.0391631Z The pattern in the square brackets refers to the reference pattern of 2025-07-17T09:05:54.0391983Z statically quantized linear. Setting the input dtype as `torch.quint8` 2025-07-17T09:05:54.0392361Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2025-07-17T09:05:54.0392704Z to the first quantize op (quant1). Similarly, setting the output dtype as 2025-07-17T09:05:54.0393043Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2025-07-17T09:05:54.0393324Z the second quantize op (quant2). 2025-07-17T09:05:54.0393451Z 2025-07-17T09:05:54.0393595Z Note that the dtype here does not refer to the interface dtypes of the 2025-07-17T09:05:54.0393908Z op. For example, the "input dtype" here is not the dtype of the input 2025-07-17T09:05:54.0394239Z tensor passed to the quantized linear op. Though it can still be the 2025-07-17T09:05:54.0394561Z same as the interface dtype, this is not always the case, e.g. the 2025-07-17T09:05:54.0394880Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2025-07-17T09:05:54.0395220Z specified in the DTypeConfig would still be quint8. The semantics of 2025-07-17T09:05:54.0395542Z dtypes here are the same as the semantics of the dtypes specified in 2025-07-17T09:05:54.0395795Z the observers. 2025-07-17T09:05:54.0395900Z 2025-07-17T09:05:54.0396033Z These dtypes are matched against the ones specified in the user's 2025-07-17T09:05:54.0396670Z QConfig. If there is a match, and the QConfig satisfies the constraints 2025-07-17T09:05:54.0397091Z specified in the DTypeConfig (if any), then we will quantize the given 2025-07-17T09:05:54.0397439Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2025-07-17T09:05:54.0397724Z the pattern will not be quantized. 2025-07-17T09:05:54.0397855Z 2025-07-17T09:05:54.0397952Z Example usage:: 2025-07-17T09:05:54.0398176Z 2025-07-17T09:05:54.0398269Z >>> # xdoctest: +SKIP(failing) 2025-07-17T09:05:54.0398477Z >>> dtype_config1 = DTypeConfig( 2025-07-17T09:05:54.0398681Z ... input_dtype=torch.quint8, 2025-07-17T09:05:54.0398888Z ... output_dtype=torch.quint8, 2025-07-17T09:05:54.0399101Z ... weight_dtype=torch.qint8, 2025-07-17T09:05:54.0399306Z ... bias_dtype=torch.float) 2025-07-17T09:05:54.0399446Z 2025-07-17T09:05:54.0399521Z >>> dtype_config2 = DTypeConfig( 2025-07-17T09:05:54.0399748Z ... input_dtype=DTypeWithConstraints( 2025-07-17T09:05:54.0399997Z ... dtype=torch.quint8, 2025-07-17T09:05:54.0400212Z ... quant_min_lower_bound=0, 2025-07-17T09:05:54.0400426Z ... quant_max_upper_bound=255, 2025-07-17T09:05:54.0400629Z ... ), 2025-07-17T09:05:54.0400816Z ... output_dtype=DTypeWithConstraints( 2025-07-17T09:05:54.0401029Z ... dtype=torch.quint8, 2025-07-17T09:05:54.0401243Z ... quant_min_lower_bound=0, 2025-07-17T09:05:54.0401456Z ... quant_max_upper_bound=255, 2025-07-17T09:05:54.0401654Z ... ), 2025-07-17T09:05:54.0401823Z ... weight_dtype=DTypeWithConstraints( 2025-07-17T09:05:54.0402038Z ... dtype=torch.qint8, 2025-07-17T09:05:54.0402249Z ... quant_min_lower_bound=-128, 2025-07-17T09:05:54.0402469Z ... quant_max_upper_bound=127, 2025-07-17T09:05:54.0402671Z ... ), 2025-07-17T09:05:54.0402835Z ... bias_dtype=torch.float) 2025-07-17T09:05:54.0402960Z 2025-07-17T09:05:54.0403048Z >>> dtype_config1.input_dtype 2025-07-17T09:05:54.0403243Z torch.quint8 2025-07-17T09:05:54.0403347Z 2025-07-17T09:05:54.0403420Z >>> dtype_config2.input_dtype 2025-07-17T09:05:54.0403611Z torch.quint8 2025-07-17T09:05:54.0403712Z 2025-07-17T09:05:54.0403806Z >>> dtype_config2.input_dtype_with_constraints 2025-07-17T09:05:54.0404291Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2025-07-17T09:05:54.0404665Z 2025-07-17T09:05:54.0404822Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:54.0405052Z 2025-07-17T09:05:54.0481609Z msg = Cannot scrape callname=ActivationSparsifier in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py line=16. 2025-07-17T09:05:54.0482525Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:54.0482781Z 2025-07-17T09:05:54.0482968Z The Activation sparsifier class aims to sparsify/prune activations in a neural 2025-07-17T09:05:54.0483331Z network. The idea is to attach the sparsifier to a layer (or layers) and it 2025-07-17T09:05:54.0483701Z zeroes out the activations based on the mask_fn (or sparsification function) 2025-07-17T09:05:54.0483993Z input by the user. 2025-07-17T09:05:54.0484257Z The mask_fn is applied once all the inputs are aggregated and reduced i.e. 2025-07-17T09:05:54.0484571Z mask = mask_fn(reduce_fn(aggregate_fn(activations))) 2025-07-17T09:05:54.0484745Z 2025-07-17T09:05:54.0484814Z Note:: 2025-07-17T09:05:54.0485100Z The sparsification mask is computed on the input **before it goes through the attached layer**. 2025-07-17T09:05:54.0485362Z 2025-07-17T09:05:54.0485425Z Args: 2025-07-17T09:05:54.0485585Z model (nn.Module): 2025-07-17T09:05:54.0485846Z The model whose layers will be sparsified. The layers that needs to be 2025-07-17T09:05:54.0486427Z sparsified should be added separately using the register_layer() function 2025-07-17T09:05:54.0486829Z aggregate_fn (Optional, Callable): 2025-07-17T09:05:54.0487137Z default aggregate_fn that is used if not specified while registering the layer. 2025-07-17T09:05:54.0487473Z specifies how inputs should be aggregated over time. 2025-07-17T09:05:54.0487958Z The aggregate_fn should usually take 2 torch tensors and return the aggregated tensor. 2025-07-17T09:05:54.0488266Z Example 2025-07-17T09:05:54.0488487Z def add_agg_fn(tensor1, tensor2): return tensor1 + tensor2 2025-07-17T09:05:54.0488747Z reduce_fn (Optional, Callable): 2025-07-17T09:05:54.0489038Z default reduce_fn that is used if not specified while registering the layer. 2025-07-17T09:05:54.0489406Z reduce_fn will be called on the aggregated tensor i.e. the tensor obtained after 2025-07-17T09:05:54.0489709Z calling agg_fn() on all inputs. 2025-07-17T09:05:54.0489922Z Example 2025-07-17T09:05:54.0490163Z def mean_reduce_fn(agg_tensor): return agg_tensor.mean(dim=0) 2025-07-17T09:05:54.0490430Z mask_fn (Optional, Callable): 2025-07-17T09:05:54.0490754Z default mask_fn that is used to create the sparsification mask using the tensor obtained after 2025-07-17T09:05:54.0491167Z calling the reduce_fn(). This is used by default if a custom one is passed in the 2025-07-17T09:05:54.0491467Z register_layer(). 2025-07-17T09:05:54.0491805Z Note that the mask_fn() definition should contain the sparse arguments that is passed in sparse_config 2025-07-17T09:05:54.0492145Z arguments. 2025-07-17T09:05:54.0492331Z features (Optional, list): 2025-07-17T09:05:54.0492556Z default selected features to sparsify. 2025-07-17T09:05:54.0492872Z If this is non-empty, then the mask_fn will be applied for each feature of the input. 2025-07-17T09:05:54.0493171Z For example, 2025-07-17T09:05:54.0493444Z mask = [mask_fn(reduce_fn(aggregated_fn(input[feature])) for feature in features] 2025-07-17T09:05:54.0493734Z feature_dim (Optional, int): 2025-07-17T09:05:54.0494045Z default dimension of input features. Again, features along this dim will be chosen 2025-07-17T09:05:54.0494363Z for sparsification. 2025-07-17T09:05:54.0494575Z sparse_config (Dict): 2025-07-17T09:05:54.0494836Z Default configuration for the mask_fn. This config will be passed 2025-07-17T09:05:54.0495107Z with the mask_fn() 2025-07-17T09:05:54.0495230Z 2025-07-17T09:05:54.0495305Z Example: 2025-07-17T09:05:54.0495464Z >>> # xdoctest: +SKIP 2025-07-17T09:05:54.0495638Z >>> model = SomeModel() 2025-07-17T09:05:54.0495907Z >>> act_sparsifier = ActivationSparsifier(...) # init activation sparsifier 2025-07-17T09:05:54.0496193Z >>> # Initialize aggregate_fn 2025-07-17T09:05:54.0496393Z >>> def agg_fn(x, y): 2025-07-17T09:05:54.0496574Z >>> return x + y 2025-07-17T09:05:54.0496749Z >>> 2025-07-17T09:05:54.0496917Z >>> # Initialize reduce_fn 2025-07-17T09:05:54.0497110Z >>> def reduce_fn(x): 2025-07-17T09:05:54.0497297Z >>> return torch.mean(x, dim=0) 2025-07-17T09:05:54.0497496Z >>> 2025-07-17T09:05:54.0497696Z >>> # Initialize mask_fn 2025-07-17T09:05:54.0497972Z >>> def mask_fn(data): 2025-07-17T09:05:54.0498262Z >>> return torch.eye(data.shape).to(data.device) 2025-07-17T09:05:54.0498588Z >>> 2025-07-17T09:05:54.0498738Z >>> 2025-07-17T09:05:54.0498895Z >>> act_sparsifier.register_layer( 2025-07-17T09:05:54.0499099Z ... model.some_layer, 2025-07-17T09:05:54.0499289Z ... aggregate_fn=agg_fn, 2025-07-17T09:05:54.0499483Z ... reduce_fn=reduce_fn, 2025-07-17T09:05:54.0499668Z ... mask_fn=mask_fn, 2025-07-17T09:05:54.0499924Z ... ) 2025-07-17T09:05:54.0500067Z >>> 2025-07-17T09:05:54.0500266Z >>> # start training process 2025-07-17T09:05:54.0500461Z >>> for _ in [...]: 2025-07-17T09:05:54.0500632Z >>> # epoch starts 2025-07-17T09:05:54.0500850Z >>> # model.forward(), compute_loss() and model.backwards() 2025-07-17T09:05:54.0501085Z >>> # epoch ends 2025-07-17T09:05:54.0501261Z >>> act_sparsifier.step() 2025-07-17T09:05:54.0501592Z >>> # end training process 2025-07-17T09:05:54.0501788Z >>> sparsifier.squash_mask() 2025-07-17T09:05:54.0501908Z 2025-07-17T09:05:54.0502074Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:54.0502283Z 2025-07-17T09:05:54.0502784Z msg = Cannot scrape callname=BaseDataScheduler.get_schedule_param in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py line=91. 2025-07-17T09:05:54.0503453Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:54.0503671Z 2025-07-17T09:05:54.0503807Z Abstract method that needs to be implemented by the child class. 2025-07-17T09:05:54.0504153Z The expected return type should is a dictionary of name to schedule_param value 2025-07-17T09:05:54.0504525Z The returned values will be updated in sparsifier when the scheduler step() function 2025-07-17T09:05:54.0504821Z is called. 2025-07-17T09:05:54.0504904Z 2025-07-17T09:05:54.0504976Z Example: 2025-07-17T09:05:54.0505129Z >>> def get_schedule_param(self): 2025-07-17T09:05:54.0505430Z ... new_param = {} 2025-07-17T09:05:54.0505664Z ... for name in self.sparsifier.data_groups.keys(): 2025-07-17T09:05:54.0505889Z ... new_param[name] = ( 2025-07-17T09:05:54.0506141Z ... self.sparsifier.data_groups[name][self.schedule_param] * 0.5 2025-07-17T09:05:54.0506377Z ... ) 2025-07-17T09:05:54.0506555Z ... return new_param 2025-07-17T09:05:54.0506710Z 2025-07-17T09:05:54.0506980Z When the step() function is called, the value in self.sparsifier.data_groups[name][self.schedule_param] 2025-07-17T09:05:54.0507398Z would be halved 2025-07-17T09:05:54.0507496Z 2025-07-17T09:05:54.0507649Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:54.0507873Z 2025-07-17T09:05:54.0668453Z msg = Cannot scrape callname=BaseSparsifier.squash_mask in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py line=229. 2025-07-17T09:05:54.0669218Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:54.0669566Z Squashes the sparse masks into the appropriate tensors. 2025-07-17T09:05:54.0669749Z 2025-07-17T09:05:54.0669883Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2025-07-17T09:05:54.0670191Z the module will have a `sparse_params` dict attached to it. 2025-07-17T09:05:54.0670376Z 2025-07-17T09:05:54.0670445Z Args: 2025-07-17T09:05:54.0670659Z params_to_keep: List of keys to save in the module or a dict 2025-07-17T09:05:54.0670957Z representing the modules and keys that will have 2025-07-17T09:05:54.0671212Z sparsity parameters saved 2025-07-17T09:05:54.0671494Z params_to_keep_per_layer: Dict to specify the params that should be 2025-07-17T09:05:54.0671792Z saved for specific layers. The keys in the dict 2025-07-17T09:05:54.0672061Z should be the module fqn, while the values should 2025-07-17T09:05:54.0672331Z be a list of strings with the names of the variables 2025-07-17T09:05:54.0672587Z to save in the `sparse_params` 2025-07-17T09:05:54.0672736Z 2025-07-17T09:05:54.0672809Z Examples: 2025-07-17T09:05:54.0673008Z >>> # xdoctest: +SKIP("locals are undefined") 2025-07-17T09:05:54.0673549Z >>> # Don't save any sparse params 2025-07-17T09:05:54.0673859Z >>> sparsifier.squash_mask() 2025-07-17T09:05:54.0674096Z >>> hasattr(model.submodule1, "sparse_params") 2025-07-17T09:05:54.0674308Z False 2025-07-17T09:05:54.0674403Z 2025-07-17T09:05:54.0674485Z >>> # Keep sparse params per layer 2025-07-17T09:05:54.0674698Z >>> sparsifier.squash_mask( 2025-07-17T09:05:54.0675068Z ... params_to_keep_per_layer={ 2025-07-17T09:05:54.0675295Z ... "submodule1.linear1": ("foo", "bar"), 2025-07-17T09:05:54.0675535Z ... "submodule2.linear42": ("baz",), 2025-07-17T09:05:54.0675736Z ... } 2025-07-17T09:05:54.0675899Z ... ) 2025-07-17T09:05:54.0676094Z >>> print(model.submodule1.linear1.sparse_params) 2025-07-17T09:05:54.0676333Z {'foo': 42, 'bar': 24} 2025-07-17T09:05:54.0676561Z >>> print(model.submodule2.linear42.sparse_params) 2025-07-17T09:05:54.0676784Z {'baz': 0.1} 2025-07-17T09:05:54.0676892Z 2025-07-17T09:05:54.0676978Z >>> # Keep sparse params for all layers 2025-07-17T09:05:54.0677247Z >>> sparsifier.squash_mask(params_to_keep=("foo", "bar")) 2025-07-17T09:05:54.0677521Z >>> print(model.submodule1.linear1.sparse_params) 2025-07-17T09:05:54.0677749Z {'foo': 42, 'bar': 24} 2025-07-17T09:05:54.0677976Z >>> print(model.submodule2.linear42.sparse_params) 2025-07-17T09:05:54.0678206Z {'foo': 42, 'bar': 24} 2025-07-17T09:05:54.0678325Z 2025-07-17T09:05:54.0678462Z >>> # Keep some sparse params for all layers, and specific ones for 2025-07-17T09:05:54.0678724Z >>> # some other layers 2025-07-17T09:05:54.0678934Z >>> sparsifier.squash_mask( 2025-07-17T09:05:54.0679145Z ... params_to_keep=("foo", "bar"), 2025-07-17T09:05:54.0679394Z ... params_to_keep_per_layer={"submodule2.linear42": ("baz",)}, 2025-07-17T09:05:54.0679640Z ... ) 2025-07-17T09:05:54.0679842Z >>> print(model.submodule1.linear1.sparse_params) 2025-07-17T09:05:54.0680067Z {'foo': 42, 'bar': 24} 2025-07-17T09:05:54.0680288Z >>> print(model.submodule2.linear42.sparse_params) 2025-07-17T09:05:54.0680523Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2025-07-17T09:05:54.0680715Z 2025-07-17T09:05:54.0680960Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:54.0681173Z 2025-07-17T09:05:55.1613227Z msg = Cannot scrape callname=AveragedModel in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py line=120. 2025-07-17T09:05:55.1613844Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:55.1614303Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2025-07-17T09:05:55.1614590Z 2025-07-17T09:05:55.1614776Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2025-07-17T09:05:55.1615160Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2025-07-17T09:05:55.1615508Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2025-07-17T09:05:55.1615779Z (UAI 2018). 2025-07-17T09:05:55.1615876Z 2025-07-17T09:05:55.1616031Z Exponential Moving Average is a variation of `Polyak averaging`_, 2025-07-17T09:05:55.1616406Z but using exponential weights instead of equal weights across iterations. 2025-07-17T09:05:55.1616614Z 2025-07-17T09:05:55.1616774Z AveragedModel class creates a copy of the provided module :attr:`model` 2025-07-17T09:05:55.1617127Z on the device :attr:`device` and allows to compute running averages of the 2025-07-17T09:05:55.1617409Z parameters of the :attr:`model`. 2025-07-17T09:05:55.1617538Z 2025-07-17T09:05:55.1617609Z Args: 2025-07-17T09:05:55.1617807Z model (torch.nn.Module): model to use with SWA/EMA 2025-07-17T09:05:55.1618662Z device (torch.device, optional): if provided, the averaged model will be 2025-07-17T09:05:55.1619101Z stored on the :attr:`device` 2025-07-17T09:05:55.1619366Z avg_fn (function, optional): the averaging function used to update 2025-07-17T09:05:55.1619678Z parameters; the function must take in the current value of the 2025-07-17T09:05:55.1620203Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2025-07-17T09:05:55.1620527Z parameter, and the number of models already averaged; if None, 2025-07-17T09:05:55.1620822Z an equally weighted average is used (default: None) 2025-07-17T09:05:55.1621129Z multi_avg_fn (function, optional): the averaging function used to update 2025-07-17T09:05:55.1621484Z parameters inplace; the function must take in the current values of the 2025-07-17T09:05:55.1621863Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2025-07-17T09:05:55.1622255Z parameters as a list, and the number of models already averaged; if None, 2025-07-17T09:05:55.1622562Z an equally weighted average is used (default: None) 2025-07-17T09:05:55.1622862Z use_buffers (bool): if ``True``, it will compute running averages for 2025-07-17T09:05:55.1623230Z both the parameters and the buffers of the model. (default: ``False``) 2025-07-17T09:05:55.1623429Z 2025-07-17T09:05:55.1623505Z Example: 2025-07-17T09:05:55.1623697Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T09:05:55.1623934Z >>> loader, optimizer, model, loss_fn = ... 2025-07-17T09:05:55.1624197Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2025-07-17T09:05:55.1624519Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2025-07-17T09:05:55.1624810Z >>> T_max=300) 2025-07-17T09:05:55.1625021Z >>> swa_start = 160 2025-07-17T09:05:55.1625241Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2025-07-17T09:05:55.1625576Z >>> for i in range(300): 2025-07-17T09:05:55.1625792Z >>> for input, target in loader: 2025-07-17T09:05:55.1626013Z >>> optimizer.zero_grad() 2025-07-17T09:05:55.1626249Z >>> loss_fn(model(input), target).backward() 2025-07-17T09:05:55.1626489Z >>> optimizer.step() 2025-07-17T09:05:55.1626704Z >>> if i > swa_start: 2025-07-17T09:05:55.1626923Z >>> swa_model.update_parameters(model) 2025-07-17T09:05:55.1627152Z >>> swa_scheduler.step() 2025-07-17T09:05:55.1627352Z >>> else: 2025-07-17T09:05:55.1627519Z >>> scheduler.step() 2025-07-17T09:05:55.1627710Z >>> 2025-07-17T09:05:55.1627904Z >>> # Update bn statistics for the swa_model at the end 2025-07-17T09:05:55.1628178Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2025-07-17T09:05:55.1628350Z 2025-07-17T09:05:55.1628530Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2025-07-17T09:05:55.1628906Z If no averaging function is provided, the default is to compute 2025-07-17T09:05:55.1629193Z equally-weighted average of the weights (SWA). 2025-07-17T09:05:55.1629358Z 2025-07-17T09:05:55.1629422Z Example: 2025-07-17T09:05:55.1629604Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T09:05:55.1629890Z >>> # Compute exponential moving averages of the weights and buffers 2025-07-17T09:05:55.1630202Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2025-07-17T09:05:55.1630511Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2025-07-17T09:05:55.1630705Z 2025-07-17T09:05:55.1630797Z .. note:: 2025-07-17T09:05:55.1631031Z When using SWA/EMA with models containing Batch Normalization you may 2025-07-17T09:05:55.1631456Z need to update the activation statistics for Batch Normalization. 2025-07-17T09:05:55.1631872Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2025-07-17T09:05:55.1632216Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2025-07-17T09:05:55.1632568Z statistics in a post-training step by passing data through the model. The 2025-07-17T09:05:55.1633054Z second does it during the parameter update phase by averaging all buffers. 2025-07-17T09:05:55.1633416Z Empirical evidence has shown that updating the statistics in normalization 2025-07-17T09:05:55.1633762Z layers increases accuracy, but you may wish to empirically test which 2025-07-17T09:05:55.1634068Z approach yields the best results in your problem. 2025-07-17T09:05:55.1634233Z 2025-07-17T09:05:55.1634300Z .. note:: 2025-07-17T09:05:55.1634548Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2025-07-17T09:05:55.1634761Z 2025-07-17T09:05:55.1634836Z .. note:: 2025-07-17T09:05:55.1635050Z When :meth:`update_parameters` is called for the first time (i.e. 2025-07-17T09:05:55.1635360Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2025-07-17T09:05:55.1635668Z to the parameters of :class:`AveragedModel`. For every subsequent 2025-07-17T09:05:55.1635983Z call of :meth:`update_parameters` the function `avg_fn` is used 2025-07-17T09:05:55.1636246Z to update the parameters. 2025-07-17T09:05:55.1636373Z 2025-07-17T09:05:55.1636527Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2025-07-17T09:05:55.1636820Z https://arxiv.org/abs/1803.05407 2025-07-17T09:05:55.1637120Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2025-07-17T09:05:55.1637397Z Average: 2025-07-17T09:05:55.1637568Z https://arxiv.org/abs/1806.05594 2025-07-17T09:05:55.1637832Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2025-07-17T09:05:55.1638103Z https://arxiv.org/abs/1904.11943 2025-07-17T09:05:55.1638376Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2025-07-17T09:05:55.1638644Z Generalizes Well: 2025-07-17T09:05:55.1638833Z https://arxiv.org/abs/2001.02312 2025-07-17T09:05:55.1639036Z .. _Polyak averaging: 2025-07-17T09:05:55.1639262Z https://paperswithcode.com/method/polyak-averaging 2025-07-17T09:05:55.1639480Z 2025-07-17T09:05:55.1639716Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:55.1639934Z 2025-07-17T09:05:55.1640213Z msg = Cannot scrape callname=SWALR in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py line=375. 2025-07-17T09:05:55.1640699Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:55.1641059Z Anneals the learning rate in each parameter group to a fixed value. 2025-07-17T09:05:55.1641262Z 2025-07-17T09:05:55.1641409Z This learning rate scheduler is meant to be used with Stochastic Weight 2025-07-17T09:05:55.1641744Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2025-07-17T09:05:55.1641938Z 2025-07-17T09:05:55.1642000Z Args: 2025-07-17T09:05:55.1642206Z optimizer (torch.optim.Optimizer): wrapped optimizer 2025-07-17T09:05:55.1642516Z swa_lrs (float or list): the learning rate value for all param groups 2025-07-17T09:05:55.1642798Z together or separately for each group. 2025-07-17T09:05:55.1643076Z annealing_epochs (int): number of epochs in the annealing phase 2025-07-17T09:05:55.1643335Z (default: 10) 2025-07-17T09:05:55.1643587Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2025-07-17T09:05:55.1643922Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2025-07-17T09:05:55.1644190Z (default: "cos") 2025-07-17T09:05:55.1644501Z last_epoch (int): the index of the last epoch (default: -1) 2025-07-17T09:05:55.1644743Z 2025-07-17T09:05:55.1644863Z The :class:`SWALR` scheduler can be used together with other 2025-07-17T09:05:55.1645184Z schedulers to switch to a constant learning rate late in the training 2025-07-17T09:05:55.1645473Z as in the example below. 2025-07-17T09:05:55.1645599Z 2025-07-17T09:05:55.1645663Z Example: 2025-07-17T09:05:55.1645954Z >>> # xdoctest: +SKIP("Undefined variables") 2025-07-17T09:05:55.1646182Z >>> loader, optimizer, model = ... 2025-07-17T09:05:55.1646406Z >>> lr_lambda = lambda epoch: 0.9 2025-07-17T09:05:55.1646687Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2025-07-17T09:05:55.1646963Z >>> lr_lambda=lr_lambda) 2025-07-17T09:05:55.1647206Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2025-07-17T09:05:55.1647500Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2025-07-17T09:05:55.1647753Z >>> swa_start = 160 2025-07-17T09:05:55.1647939Z >>> for i in range(300): 2025-07-17T09:05:55.1648147Z >>> for input, target in loader: 2025-07-17T09:05:55.1648363Z >>> optimizer.zero_grad() 2025-07-17T09:05:55.1648590Z >>> loss_fn(model(input), target).backward() 2025-07-17T09:05:55.1648821Z >>> optimizer.step() 2025-07-17T09:05:55.1649032Z >>> if i > swa_start: 2025-07-17T09:05:55.1649233Z >>> swa_scheduler.step() 2025-07-17T09:05:55.1649430Z >>> else: 2025-07-17T09:05:55.1649606Z >>> scheduler.step() 2025-07-17T09:05:55.1649725Z 2025-07-17T09:05:55.1649869Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2025-07-17T09:05:55.1650156Z https://arxiv.org/abs/1803.05407 2025-07-17T09:05:55.1650361Z 2025-07-17T09:05:55.1650584Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:55.1650801Z 2025-07-17T09:05:55.1792707Z msg = Cannot scrape callname=Optimizer.load_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/optimizer.py line=867. 2025-07-17T09:05:55.1793324Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:55.1793646Z Load the optimizer state. 2025-07-17T09:05:55.1793787Z 2025-07-17T09:05:55.1793884Z Args: 2025-07-17T09:05:55.1794105Z state_dict (dict): optimizer state. Should be an object returned 2025-07-17T09:05:55.1794386Z from a call to :meth:`state_dict`. 2025-07-17T09:05:55.1794540Z 2025-07-17T09:05:55.1794616Z .. warning:: 2025-07-17T09:05:55.1794923Z Make sure this method is called after initializing :class:`torch.optim.lr_scheduler.LRScheduler`, 2025-07-17T09:05:55.1795319Z as calling it beforehand will overwrite the loaded learning rates. 2025-07-17T09:05:55.1795522Z 2025-07-17T09:05:55.1795584Z .. note:: 2025-07-17T09:05:55.1795872Z The names of the parameters (if they exist under the "param_names" key of each param group 2025-07-17T09:05:55.1796226Z in :meth:`state_dict`) will not affect the loading process. 2025-07-17T09:05:55.1796589Z To use the parameters' names for custom cases (such as when the parameters in the loaded state dict 2025-07-17T09:05:55.1796955Z differ from those initialized in the optimizer), 2025-07-17T09:05:55.1797309Z a custom ``register_load_state_dict_pre_hook`` should be implemented to adapt the loaded dict 2025-07-17T09:05:55.1797626Z accordingly. 2025-07-17T09:05:55.1797915Z If ``param_names`` exist in loaded state dict ``param_groups`` they will be saved and override 2025-07-17T09:05:55.1798335Z the current names, if present, in the optimizer state. If they do not exist in loaded state dict, 2025-07-17T09:05:55.1798699Z the optimizer ``param_names`` will remain unchanged. 2025-07-17T09:05:55.1799156Z 2025-07-17T09:05:55.1799228Z Example: 2025-07-17T09:05:55.1799389Z >>> # xdoctest: +SKIP 2025-07-17T09:05:55.1799607Z >>> model = torch.nn.Linear(10, 10) 2025-07-17T09:05:55.1799867Z >>> optim = torch.optim.SGD(model.parameters(), lr=3e-4) 2025-07-17T09:05:55.1800150Z >>> scheduler1 = torch.optim.lr_scheduler.LinearLR( 2025-07-17T09:05:55.1800556Z ... optim, 2025-07-17T09:05:55.1800751Z ... start_factor=0.1, 2025-07-17T09:05:55.1800951Z ... end_factor=1, 2025-07-17T09:05:55.1801147Z ... total_iters=20, 2025-07-17T09:05:55.1801340Z ... ) 2025-07-17T09:05:55.1801559Z >>> scheduler2 = torch.optim.lr_scheduler.CosineAnnealingLR( 2025-07-17T09:05:55.1801812Z ... optim, 2025-07-17T09:05:55.1801987Z ... T_max=80, 2025-07-17T09:05:55.1802175Z ... eta_min=3e-5, 2025-07-17T09:05:55.1802371Z ... ) 2025-07-17T09:05:55.1802569Z >>> lr = torch.optim.lr_scheduler.SequentialLR( 2025-07-17T09:05:55.1802788Z ... optim, 2025-07-17T09:05:55.1802988Z ... schedulers=[scheduler1, scheduler2], 2025-07-17T09:05:55.1803211Z ... milestones=[20], 2025-07-17T09:05:55.1803400Z ... ) 2025-07-17T09:05:55.1803597Z >>> lr.load_state_dict(torch.load("./save_seq.pt")) 2025-07-17T09:05:55.1803894Z >>> # now load the optimizer checkpoint after loading the LRScheduler 2025-07-17T09:05:55.1804195Z >>> optim.load_state_dict(torch.load("./save_optim.pt")) 2025-07-17T09:05:55.1804364Z 2025-07-17T09:05:55.1804436Z 2025-07-17T09:05:55.1804669Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:55.1804889Z 2025-07-17T09:05:55.1982689Z msg = Cannot scrape callname=SequentialLR in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py line=808. 2025-07-17T09:05:55.1983368Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:55.1983851Z Contains a list of schedulers expected to be called sequentially during the optimization process. 2025-07-17T09:05:55.1984121Z 2025-07-17T09:05:55.1984344Z Specifically, the schedulers will be called according to the milestone points, which should provide exact 2025-07-17T09:05:55.1984781Z intervals by which each scheduler should be called at a given epoch. 2025-07-17T09:05:55.1984990Z 2025-07-17T09:05:55.1985053Z Args: 2025-07-17T09:05:55.1985248Z optimizer (Optimizer): Wrapped optimizer. 2025-07-17T09:05:55.1985608Z schedulers (list): List of chained schedulers. 2025-07-17T09:05:55.1985907Z milestones (list): List of integers that reflects milestone points. 2025-07-17T09:05:55.1986234Z last_epoch (int): The index of last epoch. Default: -1. 2025-07-17T09:05:55.1986407Z 2025-07-17T09:05:55.1986491Z Example: 2025-07-17T09:05:55.1986681Z >>> # xdoctest: +SKIP 2025-07-17T09:05:55.1986909Z >>> # Assuming optimizer uses lr = 0.05 for all groups 2025-07-17T09:05:55.1987154Z >>> # lr = 0.005 if epoch == 0 2025-07-17T09:05:55.1987359Z >>> # lr = 0.005 if epoch == 1 2025-07-17T09:05:55.1987556Z >>> # lr = 0.005 if epoch == 2 2025-07-17T09:05:55.1987743Z >>> # ... 2025-07-17T09:05:55.1987918Z >>> # lr = 0.05 if epoch == 20 2025-07-17T09:05:55.1988118Z >>> # lr = 0.045 if epoch == 21 2025-07-17T09:05:55.1988326Z >>> # lr = 0.0405 if epoch == 22 2025-07-17T09:05:55.1988581Z >>> scheduler1 = ConstantLR(optimizer, factor=0.1, total_iters=20) 2025-07-17T09:05:55.1988883Z >>> scheduler2 = ExponentialLR(optimizer, gamma=0.9) 2025-07-17T09:05:55.1989132Z >>> scheduler = SequentialLR( 2025-07-17T09:05:55.1989331Z ... optimizer, 2025-07-17T09:05:55.1989534Z ... schedulers=[scheduler1, scheduler2], 2025-07-17T09:05:55.1990433Z ... milestones=[20], 2025-07-17T09:05:55.1990657Z ... ) 2025-07-17T09:05:55.1990822Z >>> for epoch in range(100): 2025-07-17T09:05:55.1991017Z >>> train(...) 2025-07-17T09:05:55.1991194Z >>> validate(...) 2025-07-17T09:05:55.1991391Z >>> scheduler.step() 2025-07-17T09:05:55.1991515Z 2025-07-17T09:05:55.1991879Z .. image:: ../scripts/lr_scheduler_images/SequentialLR.png 2025-07-17T09:05:55.1992124Z 2025-07-17T09:05:55.1992363Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:55.1992583Z 2025-07-17T09:05:55.2006730Z msg = Cannot scrape callname=ReduceLROnPlateau in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py line=1233. 2025-07-17T09:05:55.2007325Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-07-17T09:05:55.2007686Z Reduce learning rate when a metric has stopped improving. 2025-07-17T09:05:55.2007884Z 2025-07-17T09:05:55.2008020Z Models often benefit from reducing the learning rate by a factor 2025-07-17T09:05:55.2008343Z of 2-10 once learning stagnates. This scheduler reads a metrics 2025-07-17T09:05:55.2008656Z quantity and if no improvement is seen for a 'patience' number 2025-07-17T09:05:55.2008936Z of epochs, the learning rate is reduced. 2025-07-17T09:05:55.2009090Z 2025-07-17T09:05:55.2009162Z Args: 2025-07-17T09:05:55.2009348Z optimizer (Optimizer): Wrapped optimizer. 2025-07-17T09:05:55.2009608Z mode (str): One of `min`, `max`. In `min` mode, lr will 2025-07-17T09:05:55.2009880Z be reduced when the quantity monitored has stopped 2025-07-17T09:05:55.2010157Z decreasing; in `max` mode it will be reduced when the 2025-07-17T09:05:55.2010453Z quantity monitored has stopped increasing. Default: 'min'. 2025-07-17T09:05:55.2010760Z factor (float): Factor by which the learning rate will be 2025-07-17T09:05:55.2011037Z reduced. new_lr = lr * factor. Default: 0.1. 2025-07-17T09:05:55.2011333Z patience (int): The number of allowed epochs with no improvement after 2025-07-17T09:05:55.2011618Z which the learning rate will be reduced. 2025-07-17T09:05:55.2011921Z For example, consider the case of having no patience (`patience = 0`). 2025-07-17T09:05:55.2012368Z In the first epoch, a baseline is established and is always considered good as there's no previous baseline. 2025-07-17T09:05:55.2012792Z In the second epoch, if the performance is worse than the baseline, 2025-07-17T09:05:55.2013092Z we have what is considered an intolerable epoch. 2025-07-17T09:05:55.2013421Z Since the count of intolerable epochs (1) is greater than the patience level (0), 2025-07-17T09:05:55.2013765Z the learning rate is reduced at the end of this epoch. 2025-07-17T09:05:55.2014136Z From the third epoch onwards, the learning rate continues to be reduced at the end of each epoch 2025-07-17T09:05:55.2014593Z if the performance is worse than the baseline. If the performance improves or remains the same, 2025-07-17T09:05:55.2014935Z the learning rate is not adjusted. 2025-07-17T09:05:55.2015151Z Default: 10. 2025-07-17T09:05:55.2015394Z threshold (float): Threshold for measuring the new optimum, 2025-07-17T09:05:55.2015696Z to only focus on significant changes. Default: 1e-4. 2025-07-17T09:05:55.2015975Z threshold_mode (str): One of `rel`, `abs`. In `rel` mode, 2025-07-17T09:05:55.2016258Z dynamic_threshold = best * ( 1 + threshold ) in 'max' 2025-07-17T09:05:55.2016511Z mode or best * ( 1 - threshold ) in `min` mode. 2025-07-17T09:05:55.2016773Z In `abs` mode, dynamic_threshold = best + threshold in 2025-07-17T09:05:55.2017055Z `max` mode or best - threshold in `min` mode. Default: 'rel'. 2025-07-17T09:05:55.2017489Z cooldown (int): Number of epochs to wait before resuming 2025-07-17T09:05:55.2017852Z normal operation after lr has been reduced. Default: 0. 2025-07-17T09:05:55.2018130Z min_lr (float or list): A scalar or a list of scalars. A 2025-07-17T09:05:55.2018403Z lower bound on the learning rate of all param groups 2025-07-17T09:05:55.2018657Z or each group respectively. Default: 0. 2025-07-17T09:05:55.2019048Z eps (float): Minimal decay applied to lr. If the difference 2025-07-17T09:05:55.2019340Z between new and old lr is smaller than eps, the update is 2025-07-17T09:05:55.2019598Z ignored. Default: 1e-8. 2025-07-17T09:05:55.2019728Z 2025-07-17T09:05:55.2019804Z Example: 2025-07-17T09:05:55.2019977Z >>> # xdoctest: +SKIP 2025-07-17T09:05:55.2020262Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) 2025-07-17T09:05:55.2020562Z >>> scheduler = ReduceLROnPlateau(optimizer, "min") 2025-07-17T09:05:55.2020805Z >>> for epoch in range(10): 2025-07-17T09:05:55.2021003Z >>> train(...) 2025-07-17T09:05:55.2021186Z >>> val_loss = validate(...) 2025-07-17T09:05:55.2021422Z >>> # Note that step should be called after validate() 2025-07-17T09:05:55.2021657Z >>> scheduler.step(val_loss) 2025-07-17T09:05:55.2021798Z 2025-07-17T09:05:55.2021944Z .. image:: ../scripts/lr_scheduler_images/ReduceLROnPlateau.png 2025-07-17T09:05:55.2022200Z 2025-07-17T09:05:55.2022537Z Original Error: IndentationError('unexpected indent', ('', 8, 4, ' scheduler.step(val_loss)\n', 8, -1)) 2025-07-17T09:05:55.2022830Z 2025-07-17T09:05:55.2022913Z scheduler.step(val_loss) 2025-07-17T09:05:55.2023093Z ^ 2025-07-17T09:05:55.2023495Z msg = Cannot scrape callname=CyclicLR in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py line=1430. 2025-07-17T09:05:55.2023998Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:55.2024421Z Sets the learning rate of each parameter group according to cyclical learning rate policy (CLR). 2025-07-17T09:05:55.2024681Z 2025-07-17T09:05:55.2024857Z The policy cycles the learning rate between two boundaries with a constant frequency, 2025-07-17T09:05:55.2025255Z as detailed in the paper `Cyclical Learning Rates for Training Neural Networks`_. 2025-07-17T09:05:55.2025723Z The distance between the two boundaries can be scaled on a per-iteration 2025-07-17T09:05:55.2026001Z or per-cycle basis. 2025-07-17T09:05:55.2026107Z 2025-07-17T09:05:55.2026265Z Cyclical learning rate policy changes the learning rate after every batch. 2025-07-17T09:05:55.2026598Z `step` should be called after a batch has been used for training. 2025-07-17T09:05:55.2026780Z 2025-07-17T09:05:55.2026923Z This class has three built-in policies, as put forth in the paper: 2025-07-17T09:05:55.2027116Z 2025-07-17T09:05:55.2027265Z * "triangular": A basic triangular cycle without amplitude scaling. 2025-07-17T09:05:55.2027641Z * "triangular2": A basic triangular cycle that scales initial amplitude by half each cycle. 2025-07-17T09:05:55.2028068Z * "exp_range": A cycle that scales initial amplitude by :math:`\text{gamma}^{\text{cycle iterations}}` 2025-07-17T09:05:55.2028394Z at each cycle iteration. 2025-07-17T09:05:55.2028515Z 2025-07-17T09:05:55.2028692Z This implementation was adapted from the github repo: `bckenstler/CLR`_ 2025-07-17T09:05:55.2028897Z 2025-07-17T09:05:55.2028961Z Args: 2025-07-17T09:05:55.2029139Z optimizer (Optimizer): Wrapped optimizer. 2025-07-17T09:05:55.2029404Z base_lr (float or list): Initial learning rate which is the 2025-07-17T09:05:55.2029695Z lower boundary in the cycle for each parameter group. 2025-07-17T09:05:55.2030004Z max_lr (float or list): Upper learning rate boundaries in the cycle 2025-07-17T09:05:55.2030375Z for each parameter group. Functionally, 2025-07-17T09:05:55.2030692Z it defines the cycle amplitude (max_lr - base_lr). 2025-07-17T09:05:55.2030951Z The lr at any cycle is the sum of base_lr 2025-07-17T09:05:55.2031240Z and some scaling of the amplitude; therefore 2025-07-17T09:05:55.2031493Z max_lr may not actually be reached depending on 2025-07-17T09:05:55.2031727Z scaling function. 2025-07-17T09:05:55.2032091Z step_size_up (int): Number of training iterations in the 2025-07-17T09:05:55.2032358Z increasing half of a cycle. Default: 2000 2025-07-17T09:05:55.2032614Z step_size_down (int): Number of training iterations in the 2025-07-17T09:05:55.2032888Z decreasing half of a cycle. If step_size_down is None, 2025-07-17T09:05:55.2033154Z it is set to step_size_up. Default: None 2025-07-17T09:05:55.2033411Z mode (str): One of {triangular, triangular2, exp_range}. 2025-07-17T09:05:55.2033680Z Values correspond to policies detailed above. 2025-07-17T09:05:55.2034027Z If scale_fn is not None, this argument is ignored. 2025-07-17T09:05:55.2034275Z Default: 'triangular' 2025-07-17T09:05:55.2034511Z gamma (float): Constant in 'exp_range' scaling function: 2025-07-17T09:05:55.2034753Z gamma**(cycle iterations) 2025-07-17T09:05:55.2034952Z Default: 1.0 2025-07-17T09:05:55.2035201Z scale_fn (function): Custom scaling policy defined by a single 2025-07-17T09:05:55.2035467Z argument lambda function, where 2025-07-17T09:05:55.2035689Z 0 <= scale_fn(x) <= 1 for all x >= 0. 2025-07-17T09:05:55.2035902Z If specified, then 'mode' is ignored. 2025-07-17T09:05:55.2036112Z Default: None 2025-07-17T09:05:55.2036316Z scale_mode (str): {'cycle', 'iterations'}. 2025-07-17T09:05:55.2036553Z Defines whether scale_fn is evaluated on 2025-07-17T09:05:55.2036795Z cycle number or cycle iterations (training 2025-07-17T09:05:55.2037030Z iterations since start of cycle). 2025-07-17T09:05:55.2037279Z Default: 'cycle' 2025-07-17T09:05:55.2037523Z cycle_momentum (bool): If ``True``, momentum is cycled inversely 2025-07-17T09:05:55.2037824Z to learning rate between 'base_momentum' and 'max_momentum'. 2025-07-17T09:05:55.2038075Z Default: True 2025-07-17T09:05:55.2038317Z base_momentum (float or list): Lower momentum boundaries in the cycle 2025-07-17T09:05:55.2038642Z for each parameter group. Note that momentum is cycled inversely 2025-07-17T09:05:55.2038933Z to learning rate; at the peak of a cycle, momentum is 2025-07-17T09:05:55.2039189Z 'base_momentum' and learning rate is 'max_lr'. 2025-07-17T09:05:55.2039412Z Default: 0.8 2025-07-17T09:05:55.2039663Z max_momentum (float or list): Upper momentum boundaries in the cycle 2025-07-17T09:05:55.2039941Z for each parameter group. Functionally, 2025-07-17T09:05:55.2040214Z it defines the cycle amplitude (max_momentum - base_momentum). 2025-07-17T09:05:55.2040512Z The momentum at any cycle is the difference of max_momentum 2025-07-17T09:05:55.2040777Z and some scaling of the amplitude; therefore 2025-07-17T09:05:55.2041043Z base_momentum may not actually be reached depending on 2025-07-17T09:05:55.2041331Z scaling function. Note that momentum is cycled inversely 2025-07-17T09:05:55.2041635Z to learning rate; at the start of a cycle, momentum is 'max_momentum' 2025-07-17T09:05:55.2041907Z and learning rate is 'base_lr' 2025-07-17T09:05:55.2042100Z Default: 0.9 2025-07-17T09:05:55.2042340Z last_epoch (int): The index of the last batch. This parameter is used when 2025-07-17T09:05:55.2042665Z resuming a training job. Since `step()` should be invoked after each 2025-07-17T09:05:55.2043087Z batch instead of after each epoch, this number represents the total 2025-07-17T09:05:55.2043476Z number of *batches* computed, not the total number of epochs computed. 2025-07-17T09:05:55.2043796Z When last_epoch=-1, the schedule is started from the beginning. 2025-07-17T09:05:55.2044052Z Default: -1 2025-07-17T09:05:55.2044171Z 2025-07-17T09:05:55.2044235Z Example: 2025-07-17T09:05:55.2044542Z >>> # xdoctest: +SKIP 2025-07-17T09:05:55.2044810Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) 2025-07-17T09:05:55.2045102Z >>> scheduler = torch.optim.lr_scheduler.CyclicLR( 2025-07-17T09:05:55.2045335Z ... optimizer, 2025-07-17T09:05:55.2045512Z ... base_lr=0.01, 2025-07-17T09:05:55.2045687Z ... max_lr=0.1, 2025-07-17T09:05:55.2045873Z ... step_size_up=10, 2025-07-17T09:05:55.2046051Z ... ) 2025-07-17T09:05:55.2046237Z >>> data_loader = torch.utils.data.DataLoader(...) 2025-07-17T09:05:55.2046474Z >>> for epoch in range(10): 2025-07-17T09:05:55.2046681Z >>> for batch in data_loader: 2025-07-17T09:05:55.2046883Z >>> train_batch(...) 2025-07-17T09:05:55.2047082Z >>> scheduler.step() 2025-07-17T09:05:55.2047200Z 2025-07-17T09:05:55.2047311Z .. image:: ../scripts/lr_scheduler_images/CyclicLR.png 2025-07-17T09:05:55.2047465Z 2025-07-17T09:05:55.2047679Z .. _Cyclical Learning Rates for Training Neural Networks: https://arxiv.org/abs/1506.01186 2025-07-17T09:05:55.2048033Z .. _bckenstler/CLR: https://github.com/bckenstler/CLR 2025-07-17T09:05:55.2048256Z 2025-07-17T09:05:55.2048493Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:55.2048702Z 2025-07-17T09:05:55.2049068Z msg = Cannot scrape callname=CosineAnnealingWarmRestarts in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py line=1713. 2025-07-17T09:05:55.2049646Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:55.2050015Z Set the learning rate of each parameter group using a cosine annealing schedule. 2025-07-17T09:05:55.2050223Z 2025-07-17T09:05:55.2050349Z The :math:`\eta_{max}` is set to the initial lr, :math:`T_{cur}` 2025-07-17T09:05:55.2050671Z is the number of epochs since the last restart and :math:`T_{i}` is the number 2025-07-17T09:05:55.2050967Z of epochs between two warm restarts in SGDR: 2025-07-17T09:05:55.2051116Z 2025-07-17T09:05:55.2051176Z .. math:: 2025-07-17T09:05:55.2051384Z \eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 + 2025-07-17T09:05:55.2051661Z \cos\left(\frac{T_{cur}}{T_{i}}\pi\right)\right) 2025-07-17T09:05:55.2051813Z 2025-07-17T09:05:55.2051920Z When :math:`T_{cur}=T_{i}`, set :math:`\eta_t = \eta_{min}`. 2025-07-17T09:05:55.2052198Z When :math:`T_{cur}=0` after restart, set :math:`\eta_t=\eta_{max}`. 2025-07-17T09:05:55.2052376Z 2025-07-17T09:05:55.2052448Z It has been proposed in 2025-07-17T09:05:55.2052672Z `SGDR: Stochastic Gradient Descent with Warm Restarts`_. 2025-07-17T09:05:55.2052835Z 2025-07-17T09:05:55.2052901Z Args: 2025-07-17T09:05:55.2053072Z optimizer (Optimizer): Wrapped optimizer. 2025-07-17T09:05:55.2053315Z T_0 (int): Number of iterations until the first restart. 2025-07-17T09:05:55.2053649Z T_mult (int, optional): A factor by which :math:`T_{i}` increases after a restart. Default: 1. 2025-07-17T09:05:55.2053991Z eta_min (float, optional): Minimum learning rate. Default: 0. 2025-07-17T09:05:55.2054292Z last_epoch (int, optional): The index of the last epoch. Default: -1. 2025-07-17T09:05:55.2054480Z 2025-07-17T09:05:55.2054593Z .. _SGDR\: Stochastic Gradient Descent with Warm Restarts: 2025-07-17T09:05:55.2054830Z https://arxiv.org/abs/1608.03983 2025-07-17T09:05:55.2054969Z 2025-07-17T09:05:55.2069399Z Example: 2025-07-17T09:05:55.2069758Z >>> # xdoctest: +SKIP 2025-07-17T09:05:55.2070103Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.05) 2025-07-17T09:05:55.2070448Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts( 2025-07-17T09:05:55.2070736Z ... optimizer, T_0=20 2025-07-17T09:05:55.2070934Z ... ) 2025-07-17T09:05:55.2071103Z >>> for epoch in range(100): 2025-07-17T09:05:55.2071306Z >>> train(...) 2025-07-17T09:05:55.2071628Z >>> validate(...) 2025-07-17T09:05:55.2071827Z >>> scheduler.step() 2025-07-17T09:05:55.2071950Z 2025-07-17T09:05:55.2072123Z .. image:: ../scripts/lr_scheduler_images/CosineAnnealingWarmRestarts.png 2025-07-17T09:05:55.2072397Z 2025-07-17T09:05:55.2072640Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:55.2072853Z 2025-07-17T09:05:55.2073192Z msg = Cannot scrape callname=OneCycleLR in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py line=1863. 2025-07-17T09:05:55.2073722Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:55.2074131Z Sets the learning rate of each parameter group according to the 1cycle learning rate policy. 2025-07-17T09:05:55.2074379Z 2025-07-17T09:05:55.2074574Z The 1cycle policy anneals the learning rate from an initial learning rate to some maximum 2025-07-17T09:05:55.2075000Z learning rate and then from that maximum learning rate to some minimum learning rate much 2025-07-17T09:05:55.2075314Z lower than the initial learning rate. 2025-07-17T09:05:55.2075604Z This policy was initially described in the paper `Super-Convergence: 2025-07-17T09:05:55.2075945Z Very Fast Training of Neural Networks Using Large Learning Rates`_. 2025-07-17T09:05:55.2076137Z 2025-07-17T09:05:55.2076300Z The 1cycle learning rate policy changes the learning rate after every batch. 2025-07-17T09:05:55.2076640Z `step` should be called after a batch has been used for training. 2025-07-17T09:05:55.2076846Z 2025-07-17T09:05:55.2076924Z This scheduler is not chainable. 2025-07-17T09:05:55.2077065Z 2025-07-17T09:05:55.2077213Z Note also that the total number of steps in the cycle can be determined in one 2025-07-17T09:05:55.2077516Z of two ways (listed in order of precedence): 2025-07-17T09:05:55.2077665Z 2025-07-17T09:05:55.2077766Z #. A value for total_steps is explicitly provided. 2025-07-17T09:05:55.2078065Z #. A number of epochs (epochs) and a number of steps per epoch 2025-07-17T09:05:55.2078326Z (steps_per_epoch) are provided. 2025-07-17T09:05:55.2078569Z In this case, the number of total steps is inferred by 2025-07-17T09:05:55.2078843Z total_steps = epochs * steps_per_epoch 2025-07-17T09:05:55.2078983Z 2025-07-17T09:05:55.2079142Z You must either provide a value for total_steps or provide a value for both 2025-07-17T09:05:55.2079435Z epochs and steps_per_epoch. 2025-07-17T09:05:55.2079560Z 2025-07-17T09:05:55.2079751Z The default behaviour of this scheduler follows the fastai implementation of 1cycle, which 2025-07-17T09:05:55.2080175Z claims that "unpublished work has shown even better results by using only two phases". To 2025-07-17T09:05:55.2080568Z mimic the behaviour of the original paper instead, set ``three_phase=True``. 2025-07-17T09:05:55.2080786Z 2025-07-17T09:05:55.2080867Z Args: 2025-07-17T09:05:55.2081056Z optimizer (Optimizer): Wrapped optimizer. 2025-07-17T09:05:55.2081339Z max_lr (float or list): Upper learning rate boundaries in the cycle 2025-07-17T09:05:55.2081599Z for each parameter group. 2025-07-17T09:05:55.2081866Z total_steps (int): The total number of steps in the cycle. Note that 2025-07-17T09:05:55.2082187Z if a value is not provided here, then it must be inferred by providing 2025-07-17T09:05:55.2082480Z a value for epochs and steps_per_epoch. 2025-07-17T09:05:55.2082705Z Default: None 2025-07-17T09:05:55.2083024Z epochs (int): The number of epochs to train for. This is used along 2025-07-17T09:05:55.2083450Z with steps_per_epoch in order to infer the total number of steps in the cycle 2025-07-17T09:05:55.2083763Z if a value for total_steps is not provided. 2025-07-17T09:05:55.2083989Z Default: None 2025-07-17T09:05:55.2084346Z steps_per_epoch (int): The number of steps per epoch to train for. This is 2025-07-17T09:05:55.2084696Z used along with epochs in order to infer the total number of steps in the 2025-07-17T09:05:55.2085010Z cycle if a value for total_steps is not provided. 2025-07-17T09:05:55.2085245Z Default: None 2025-07-17T09:05:55.2085491Z pct_start (float): The percentage of the cycle (in number of steps) spent 2025-07-17T09:05:55.2085776Z increasing the learning rate. 2025-07-17T09:05:55.2085984Z Default: 0.3 2025-07-17T09:05:55.2086187Z anneal_strategy (str): {'cos', 'linear'} 2025-07-17T09:05:55.2086490Z Specifies the annealing strategy: "cos" for cosine annealing, "linear" for 2025-07-17T09:05:55.2086777Z linear annealing. 2025-07-17T09:05:55.2086965Z Default: 'cos' 2025-07-17T09:05:55.2087207Z cycle_momentum (bool): If ``True``, momentum is cycled inversely 2025-07-17T09:05:55.2087516Z to learning rate between 'base_momentum' and 'max_momentum'. 2025-07-17T09:05:55.2087761Z Default: True 2025-07-17T09:05:55.2088001Z base_momentum (float or list): Lower momentum boundaries in the cycle 2025-07-17T09:05:55.2088329Z for each parameter group. Note that momentum is cycled inversely 2025-07-17T09:05:55.2088633Z to learning rate; at the peak of a cycle, momentum is 2025-07-17T09:05:55.2088893Z 'base_momentum' and learning rate is 'max_lr'. 2025-07-17T09:05:55.2089125Z Default: 0.85 2025-07-17T09:05:55.2089351Z max_momentum (float or list): Upper momentum boundaries in the cycle 2025-07-17T09:05:55.2089635Z for each parameter group. Functionally, 2025-07-17T09:05:55.2089910Z it defines the cycle amplitude (max_momentum - base_momentum). 2025-07-17T09:05:55.2090181Z Note that momentum is cycled inversely 2025-07-17T09:05:55.2090451Z to learning rate; at the start of a cycle, momentum is 'max_momentum' 2025-07-17T09:05:55.2090731Z and learning rate is 'base_lr' 2025-07-17T09:05:55.2090939Z Default: 0.95 2025-07-17T09:05:55.2091176Z div_factor (float): Determines the initial learning rate via 2025-07-17T09:05:55.2091435Z initial_lr = max_lr/div_factor 2025-07-17T09:05:55.2091643Z Default: 25 2025-07-17T09:05:55.2091881Z final_div_factor (float): Determines the minimum learning rate via 2025-07-17T09:05:55.2092154Z min_lr = initial_lr/final_div_factor 2025-07-17T09:05:55.2092369Z Default: 1e4 2025-07-17T09:05:55.2092640Z three_phase (bool): If ``True``, use a third phase of the schedule to annihilate the 2025-07-17T09:05:55.2093020Z learning rate according to 'final_div_factor' instead of modifying the second 2025-07-17T09:05:55.2093396Z phase (the first two phases will be symmetrical about the step indicated by 2025-07-17T09:05:55.2093681Z 'pct_start'). 2025-07-17T09:05:55.2093942Z last_epoch (int): The index of the last batch. This parameter is used when 2025-07-17T09:05:55.2094292Z resuming a training job. Since `step()` should be invoked after each 2025-07-17T09:05:55.2094625Z batch instead of after each epoch, this number represents the total 2025-07-17T09:05:55.2094959Z number of *batches* computed, not the total number of epochs computed. 2025-07-17T09:05:55.2095283Z When last_epoch=-1, the schedule is started from the beginning. 2025-07-17T09:05:55.2095538Z Default: -1 2025-07-17T09:05:55.2095728Z 2025-07-17T09:05:55.2095804Z Example: 2025-07-17T09:05:55.2096033Z >>> # xdoctest: +SKIP 2025-07-17T09:05:55.2096257Z >>> data_loader = torch.utils.data.DataLoader(...) 2025-07-17T09:05:55.2096562Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.9) 2025-07-17T09:05:55.2096872Z >>> scheduler = torch.optim.lr_scheduler.OneCycleLR( 2025-07-17T09:05:55.2097280Z ... optimizer, max_lr=0.01, steps_per_epoch=len(data_loader), epochs=10 2025-07-17T09:05:55.2097547Z ... ) 2025-07-17T09:05:55.2097714Z >>> for epoch in range(10): 2025-07-17T09:05:55.2097924Z >>> for batch in data_loader: 2025-07-17T09:05:55.2098137Z >>> train_batch(...) 2025-07-17T09:05:55.2098340Z >>> optimizer.step() 2025-07-17T09:05:55.2098540Z >>> scheduler.step() 2025-07-17T09:05:55.2098657Z 2025-07-17T09:05:55.2098780Z .. image:: ../scripts/lr_scheduler_images/OneCycleLR.png 2025-07-17T09:05:55.2098942Z 2025-07-17T09:05:55.2099127Z .. _Super-Convergence\: Very Fast Training of Neural Networks Using Large Learning Rates: 2025-07-17T09:05:55.2099462Z https://arxiv.org/abs/1708.07120 2025-07-17T09:05:55.2099654Z 2025-07-17T09:05:55.2099893Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:55.2100119Z 2025-07-17T09:05:55.2478800Z gathering tests 2025-07-17T09:05:55.2487447Z running 719 test(s) 2025-07-17T09:05:55.2497490Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::typename:0, line 1074 <- wrt source file 2025-07-17T09:05:55.2503519Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::typename:0 2025-07-17T09:05:55.2504179Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::is_tensor:0, line 1110 <- wrt source file 2025-07-17T09:05:55.2506100Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::is_tensor:0 2025-07-17T09:05:55.2506781Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_device:0, line 1195 <- wrt source file 2025-07-17T09:05:55.2507497Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_device:0 2025-07-17T09:05:55.2508170Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_tensor_type:0, line 1244 <- wrt source file 2025-07-17T09:05:55.2508871Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_tensor_type:0 2025-07-17T09:05:55.2509534Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_dtype:0, line 1281 <- wrt source file 2025-07-17T09:05:55.2510210Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_dtype:0 2025-07-17T09:05:55.2510899Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::use_deterministic_algorithms:0, line 1436 <- wrt source file 2025-07-17T09:05:55.2511643Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::use_deterministic_algorithms:0 2025-07-17T09:05:55.2512312Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::compile:0, line 2559 <- wrt source file 2025-07-17T09:05:55.2512928Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::compile:0 2025-07-17T09:05:55.2513645Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0, line 2832 <- wrt source file 2025-07-17T09:05:55.2514394Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0 2025-07-17T09:05:55.2515599Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::add_safe_globals:0, line 299 <- wrt source file 2025-07-17T09:05:55.2516294Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::add_safe_globals:0 2025-07-17T09:05:55.2517174Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::safe_globals:0, line 324 <- wrt source file 2025-07-17T09:05:55.2517871Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::safe_globals:0 2025-07-17T09:05:55.2518526Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::skip_data:0, line 400 <- wrt source file 2025-07-17T09:05:55.2519191Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::skip_data:0 2025-07-17T09:05:55.2519865Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::register_package:0, line 472 <- wrt source file 2025-07-17T09:05:55.2520577Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::register_package:0 2025-07-17T09:05:55.2521232Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::save:0, line 950 <- wrt source file 2025-07-17T09:05:55.2521868Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::save:0 2025-07-17T09:05:55.2522517Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_ignored_functions:0, line 116 <- wrt source file 2025-07-17T09:05:55.2523217Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_ignored_functions:0 2025-07-17T09:05:55.2523893Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_testing_overrides:0, line 422 <- wrt source file 2025-07-17T09:05:55.2548960Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_testing_overrides:0 2025-07-17T09:05:55.2549749Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::wrap_torch_function:0, line 1575 <- wrt source file 2025-07-17T09:05:55.2550801Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::wrap_torch_function:0 2025-07-17T09:05:55.2551494Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::handle_torch_function:0, line 1710 <- wrt source file 2025-07-17T09:05:55.2552771Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::handle_torch_function:0 2025-07-17T09:05:55.2553487Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_method_or_property:0, line 1958 <- wrt source file 2025-07-17T09:05:55.2572349Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_method_or_property:0 2025-07-17T09:05:55.2573042Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_like:0, line 1977 <- wrt source file 2025-07-17T09:05:55.2577083Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_like:0 2025-07-17T09:05:55.2577703Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::list:0, line 468 <- wrt source file 2025-07-17T09:05:55.2578342Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::list:0 2025-07-17T09:05:55.2578933Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::help:0, line 528 <- wrt source file 2025-07-17T09:05:55.2579794Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::help:0 2025-07-17T09:05:55.2580431Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_tensors:0, line 64 <- wrt source file 2025-07-17T09:05:55.2594809Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_tensors:0 2025-07-17T09:05:55.2595572Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_shapes:0, line 92 <- wrt source file 2025-07-17T09:05:55.2596498Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_shapes:0 2025-07-17T09:05:55.2597148Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::split:0, line 193 <- wrt source file 2025-07-17T09:05:55.2610087Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::split:0 2025-07-17T09:05:55.2610737Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::einsum:0, line 307 <- wrt source file 2025-07-17T09:05:55.2625191Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::einsum:0 2025-07-17T09:05:55.2625977Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_unique_consecutive_impl:0, line 1041 <- wrt source file 2025-07-17T09:05:55.2634655Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_unique_consecutive_impl:0 2025-07-17T09:05:55.2635437Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::tensordot:0, line 1316 <- wrt source file 2025-07-17T09:05:55.2645594Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::tensordot:0 2025-07-17T09:05:55.2646434Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cartesian_prod:0, line 1400 <- wrt source file 2025-07-17T09:05:55.2650376Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cartesian_prod:0 2025-07-17T09:05:55.2651076Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::block_diag:0, line 1434 <- wrt source file 2025-07-17T09:05:55.2657648Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::block_diag:0 2025-07-17T09:05:55.2658283Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cdist:0, line 1490 <- wrt source file 2025-07-17T09:05:55.2668674Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cdist:0 2025-07-17T09:05:55.2669314Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_1d:0, line 1531 <- wrt source file 2025-07-17T09:05:55.2679297Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_1d:0 2025-07-17T09:05:55.2679934Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_2d:0, line 1567 <- wrt source file 2025-07-17T09:05:55.2690536Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_2d:0 2025-07-17T09:05:55.2691199Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_3d:0, line 1605 <- wrt source file 2025-07-17T09:05:55.2703717Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_3d:0 2025-07-17T09:05:55.2704346Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::norm:0, line 1778 <- wrt source file 2025-07-17T09:05:55.2726966Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::norm:0 2025-07-17T09:05:55.2727605Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::unravel_index:0, line 1946 <- wrt source file 2025-07-17T09:05:55.2744228Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::unravel_index:0 2025-07-17T09:05:55.2744896Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::chain_matmul:0, line 2046 <- wrt source file 2025-07-17T09:05:55.2745630Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::chain_matmul:0 2025-07-17T09:05:55.2746270Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_lu_impl:0, line 2146 <- wrt source file 2025-07-17T09:05:55.2746935Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_lu_impl:0 2025-07-17T09:05:55.2747584Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor_str.py::set_printoptions:0, line 53 <- wrt source file 2025-07-17T09:05:55.2755045Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor_str.py::set_printoptions:0 2025-07-17T09:05:55.2755761Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_namedtensor_internals.py::update_names:0, line 118 <- wrt source file 2025-07-17T09:05:55.2756500Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_namedtensor_internals.py::update_names:0 2025-07-17T09:05:55.2757173Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.define:0, line 152 <- wrt source file 2025-07-17T09:05:55.2761968Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.define:0 2025-07-17T09:05:55.2762667Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library._impl_with_aoti_compile:0, line 246 <- wrt source file 2025-07-17T09:05:55.2773117Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library._impl_with_aoti_compile:0 2025-07-17T09:05:55.2773812Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.impl:0, line 306 <- wrt source file 2025-07-17T09:05:55.2776437Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.impl:0 2025-07-17T09:05:55.2777060Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::define:0, line 511 <- wrt source file 2025-07-17T09:05:55.2788911Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::define:0 2025-07-17T09:05:55.2789549Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::impl:0, line 617 <- wrt source file 2025-07-17T09:05:55.2798785Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::impl:0 2025-07-17T09:05:55.2799423Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_kernel:0, line 798 <- wrt source file 2025-07-17T09:05:55.2800114Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_kernel:0 2025-07-17T09:05:55.2800762Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_autocast:0, line 866 <- wrt source file 2025-07-17T09:05:55.2801516Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_autocast:0 2025-07-17T09:05:55.2802323Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_torch_dispatch:0, line 1217 <- wrt source file 2025-07-17T09:05:55.3206340Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_torch_dispatch:0 2025-07-17T09:05:55.3207693Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_vmap:0, line 1306 <- wrt source file 2025-07-17T09:05:55.3308725Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_vmap:0 2025-07-17T09:05:55.3309523Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/quasirandom.py::SobolEngine:0, line 39 <- wrt source file 2025-07-17T09:05:55.3310257Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/quasirandom.py::SobolEngine:0 2025-07-17T09:05:55.3311001Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::Generator:0, line 15 <- wrt source file 2025-07-17T09:05:55.3311815Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::Generator:0 2025-07-17T09:05:55.3312545Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::_LinAlgError:0, line 5 <- wrt source file 2025-07-17T09:05:55.3313315Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::_LinAlgError:0 2025-07-17T09:05:55.3314039Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/torch_version.py::TorchVersion:0, line 19 <- wrt source file 2025-07-17T09:05:55.3314718Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/torch_version.py::TorchVersion:0 2025-07-17T09:05:55.3315398Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_hook:0, line 649 <- wrt source file 2025-07-17T09:05:55.3318315Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_hook:0 2025-07-17T09:05:55.3319047Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0, line 706 <- wrt source file 2025-07-17T09:05:55.3326413Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0 2025-07-17T09:05:55.3327165Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.refine_names:0, line 1333 <- wrt source file 2025-07-17T09:05:55.3389854Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.refine_names:0 2025-07-17T09:05:55.3390598Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.align_to:0, line 1378 <- wrt source file 2025-07-17T09:05:55.3392973Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.align_to:0 2025-07-17T09:05:55.3393718Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.rename:0, line 1451 <- wrt source file 2025-07-17T09:05:55.3397269Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.rename:0 2025-07-17T09:05:55.3397950Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0, line 1481 <- wrt source file 2025-07-17T09:05:55.3402260Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0 2025-07-17T09:05:55.3402901Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::custom_op:0, line 55 <- wrt source file 2025-07-17T09:05:55.3404044Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::custom_op:0 2025-07-17T09:05:55.3404656Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl:0, line 138 <- wrt source file 2025-07-17T09:05:55.3405549Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl:0 2025-07-17T09:05:55.3406184Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl_abstract:0, line 208 <- wrt source file 2025-07-17T09:05:55.3476871Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl_abstract:0 2025-07-17T09:05:55.3477628Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::as_nested_tensor:0, line 61 <- wrt source file 2025-07-17T09:05:55.3547769Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::as_nested_tensor:0 2025-07-17T09:05:55.3548714Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor:0, line 240 <- wrt source file 2025-07-17T09:05:55.3550320Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor:0 2025-07-17T09:05:55.3551147Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::narrow:0, line 315 <- wrt source file 2025-07-17T09:05:55.3602498Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::narrow:0 2025-07-17T09:05:55.3603351Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor_from_jagged:0, line 405 <- wrt source file 2025-07-17T09:05:55.3619967Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor_from_jagged:0 2025-07-17T09:05:55.3620760Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::masked_select:0, line 481 <- wrt source file 2025-07-17T09:05:55.3632007Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::masked_select:0 2025-07-17T09:05:55.3632820Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::check_sparse_tensor_invariants:0, line 475 <- wrt source file 2025-07-17T09:05:55.3643961Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::check_sparse_tensor_invariants:0 2025-07-17T09:05:55.3644780Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::as_sparse_gradcheck:0, line 561 <- wrt source file 2025-07-17T09:05:55.3683098Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::as_sparse_gradcheck:0 2025-07-17T09:05:55.3683982Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/__init__.py::annotate:0, line 147 <- wrt source file 2025-07-17T09:05:55.3684795Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/__init__.py::annotate:0 2025-07-17T09:05:55.3685622Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::allow_in_graph:0, line 123 <- wrt source file 2025-07-17T09:05:55.3686506Z * SKIPPED: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_compiling:0 2025-07-17T09:05:56.0542020Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0, line 459 <- wrt source file 2025-07-17T09:05:56.0542749Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0 2025-07-17T09:05:56.0543457Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_exporting:0, line 477 <- wrt source file 2025-07-17T09:05:56.0544323Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_exporting:0 2025-07-17T09:05:56.0545026Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::save_cache_artifacts:0, line 492 <- wrt source file 2025-07-17T09:05:56.0545859Z * SKIPPED: 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2025-07-17T09:05:56.0550512Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::register_dataclass:0, line 575 <- wrt source file 2025-07-17T09:05:56.0551216Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::register_dataclass:0 2025-07-17T09:05:56.0551942Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/accelerator/__init__.py::current_accelerator:0, line 113 <- wrt source file 2025-07-17T09:05:56.2595214Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/accelerator/__init__.py::current_accelerator:0 2025-07-17T09:05:56.2596108Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/accelerator/__init__.py::device_index:0, line 249 <- wrt source file 2025-07-17T09:05:56.2596883Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/accelerator/__init__.py::device_index:0 2025-07-17T09:05:56.2597575Z * DOCTEST : 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/fx_minifier.py::minifier:0, line 194 <- wrt source file 2025-07-17T09:05:56.2847023Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/fx_minifier.py::minifier:0 2025-07-17T09:05:56.2847770Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/benchmark_utils.py::benchmark_utilization:0, line 184 <- wrt source file 2025-07-17T09:05:56.2848569Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/benchmark_utils.py::benchmark_utilization:0 2025-07-17T09:05:56.2849299Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::vjp:0, line 233 <- wrt source file 2025-07-17T09:05:56.2873771Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::vjp:0 2025-07-17T09:05:56.2874549Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacrev:0, line 475 <- wrt source file 2025-07-17T09:05:56.2919056Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacrev:0 2025-07-17T09:05:56.2920033Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jvp:0, line 1023 <- wrt source file 2025-07-17T09:05:56.3543652Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jvp:0 2025-07-17T09:05:56.3545147Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0, line 1181 <- wrt source file 2025-07-17T09:05:56.3584012Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0 2025-07-17T09:05:56.3584826Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::hessian:0, line 1341 <- wrt source file 2025-07-17T09:05:56.3595395Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::hessian:0 2025-07-17T09:05:56.3596168Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::functionalize:0, line 1505 <- wrt source file 2025-07-17T09:05:56.3596957Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::functionalize:0 2025-07-17T09:05:56.3597686Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::linearize:0, line 1704 <- wrt source file 2025-07-17T09:05:56.3720426Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::linearize:0 2025-07-17T09:05:56.3721343Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/_aot_autograd/schemas.py::CompilerWrapper.post_compile:0, line 1066 <- wrt source file 2025-07-17T09:05:56.3722258Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/_aot_autograd/schemas.py::CompilerWrapper.post_compile:0 2025-07-17T09:05:56.3723034Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::custom_op:0, line 98 <- wrt source file 2025-07-17T09:05:56.4208159Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::custom_op:0 2025-07-17T09:05:56.4209025Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0, line 237 <- wrt source file 2025-07-17T09:05:56.4264078Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0 2025-07-17T09:05:56.4264944Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0, line 306 <- wrt source file 2025-07-17T09:05:56.4265947Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0 2025-07-17T09:05:56.4266734Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0, line 540 <- wrt source file 2025-07-17T09:05:56.4373454Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0 2025-07-17T09:05:56.4374329Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0, line 708 <- wrt source file 2025-07-17T09:05:56.4480694Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0 2025-07-17T09:05:56.4482141Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autocast:0, line 794 <- wrt source file 2025-07-17T09:05:56.4483113Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autocast:0 2025-07-17T09:05:56.4484069Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/infer_schema.py::infer_schema:0, line 51 <- wrt source file 2025-07-17T09:05:56.4484843Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/infer_schema.py::infer_schema:0 2025-07-17T09:05:56.4485617Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0, line 197 <- wrt source file 2025-07-17T09:05:56.4486405Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0 2025-07-17T09:05:56.4487183Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0, line 175 <- wrt source file 2025-07-17T09:05:56.4531947Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0 2025-07-17T09:05:56.4532790Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_creation.py::make_tensor:0, line 114 <- wrt source file 2025-07-17T09:05:56.4533546Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_creation.py::make_tensor:0 2025-07-17T09:05:56.4534282Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::parametrize:0, line 614 <- wrt source file 2025-07-17T09:05:56.4535050Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::parametrize:0 2025-07-17T09:05:56.4535836Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::reparametrize:0, line 735 <- wrt source file 2025-07-17T09:05:56.4536617Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::reparametrize:0 2025-07-17T09:05:56.4537363Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::decorateIf:0, line 824 <- wrt source file 2025-07-17T09:05:56.4538122Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::decorateIf:0 2025-07-17T09:05:56.4538928Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_symmetric_psd_matrix:0, line 4731 <- wrt source file 2025-07-17T09:05:56.4539776Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_symmetric_psd_matrix:0 2025-07-17T09:05:56.4540588Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0, line 4745 <- wrt source file 2025-07-17T09:05:56.4541425Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0 2025-07-17T09:05:56.4542233Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0, line 4775 <- wrt source file 2025-07-17T09:05:56.4543055Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0 2025-07-17T09:05:56.4543840Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/logging_utils.py::logs_to_string:0, line 194 <- wrt source file 2025-07-17T09:05:56.4544848Z * SKIPPED: 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2025-07-17T09:05:56.4568002Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_equal:0, line 171 <- wrt source file 2025-07-17T09:05:56.4594976Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_equal:0 2025-07-17T09:05:56.4595772Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0, line 1008 <- wrt source file 2025-07-17T09:05:56.4629343Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0 2025-07-17T09:05:56.4630160Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0, line 1073 <- wrt source file 2025-07-17T09:05:56.4630978Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0 2025-07-17T09:05:56.4631757Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0, line 1294 <- wrt source file 2025-07-17T09:05:56.4640819Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0 2025-07-17T09:05:56.4641662Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0, line 1360 <- wrt source file 2025-07-17T09:05:56.4642665Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0 2025-07-17T09:05:56.4643457Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0, line 1423 <- wrt source file 2025-07-17T09:05:56.4646228Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0 2025-07-17T09:05:56.4646933Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0, line 1468 <- wrt source file 2025-07-17T09:05:56.4647794Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0 2025-07-17T09:05:56.4648492Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_warns:0, line 1578 <- wrt source file 2025-07-17T09:05:56.4650563Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_warns:0 2025-07-17T09:05:56.4651342Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0, line 69 <- wrt source file 2025-07-17T09:05:56.4652174Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0 2025-07-17T09:05:56.4653028Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::generate_methods_for_privateuse1_backend:0, line 375 <- wrt source file 2025-07-17T09:05:56.4653949Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::generate_methods_for_privateuse1_backend:0 2025-07-17T09:05:56.4654761Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::_get_custom_mod_func:0, line 410 <- wrt source file 2025-07-17T09:05:56.4655556Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::_get_custom_mod_func:0 2025-07-17T09:05:56.4656270Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/dlpack.py::from_dlpack:0, line 80 <- wrt source file 2025-07-17T09:05:56.4664172Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/dlpack.py::from_dlpack:0 2025-07-17T09:05:56.4664856Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::register_dataclass:0, line 307 <- wrt source file 2025-07-17T09:05:56.4671881Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::register_dataclass:0 2025-07-17T09:05:56.4672569Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::register_constant:0, line 423 <- wrt source file 2025-07-17T09:05:56.4680853Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::register_constant:0 2025-07-17T09:05:56.4681536Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::tree_is_leaf:0, line 1030 <- wrt source file 2025-07-17T09:05:56.4682859Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::tree_is_leaf:0 2025-07-17T09:05:56.4683506Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::tree_map:0, line 1349 <- wrt source file 2025-07-17T09:05:56.4685941Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::tree_map:0 2025-07-17T09:05:56.4686636Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::checkpoint_sequential:0, line 547 <- wrt source file 2025-07-17T09:05:56.4687385Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::checkpoint_sequential:0 2025-07-17T09:05:56.4688131Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::set_checkpoint_early_stop:0, line 749 <- wrt source file 2025-07-17T09:05:56.4689095Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::set_checkpoint_early_stop:0 2025-07-17T09:05:56.4689938Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_is_leaf:0, line 277 <- wrt source file 2025-07-17T09:05:56.4690641Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_is_leaf:0 2025-07-17T09:05:56.4691297Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0, line 320 <- wrt source file 2025-07-17T09:05:56.4696076Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0 2025-07-17T09:05:56.4696739Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0, line 357 <- wrt source file 2025-07-17T09:05:56.4698895Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0 2025-07-17T09:05:56.4699564Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0, line 387 <- wrt source file 2025-07-17T09:05:56.4707104Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0 2025-07-17T09:05:56.4707760Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0, line 422 <- wrt source file 2025-07-17T09:05:56.4709712Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0 2025-07-17T09:05:56.4710379Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0, line 457 <- wrt source file 2025-07-17T09:05:56.4712403Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0 2025-07-17T09:05:56.4715999Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_map:0, line 494 <- wrt source file 2025-07-17T09:05:56.4716672Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_map:0 2025-07-17T09:05:56.4717357Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0, line 910 <- wrt source file 2025-07-17T09:05:56.4721595Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0 2025-07-17T09:05:56.4722382Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0, line 216 <- wrt source file 2025-07-17T09:05:56.4723200Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0 2025-07-17T09:05:56.4724002Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0, line 314 <- wrt source file 2025-07-17T09:05:56.4724820Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0 2025-07-17T09:05:56.4725627Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0, line 362 <- wrt source file 2025-07-17T09:05:56.4726434Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0 2025-07-17T09:05:56.4727413Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0, line 394 <- wrt source file 2025-07-17T09:05:56.4728296Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0 2025-07-17T09:05:56.4729208Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_tensor:0, line 441 <- wrt source file 2025-07-17T09:05:56.4730010Z * SKIPPED: 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2025-07-17T09:05:56.4734900Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0 2025-07-17T09:05:56.4735683Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0, line 648 <- wrt source file 2025-07-17T09:05:56.4736495Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0 2025-07-17T09:05:56.4737267Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0, line 811 <- wrt source file 2025-07-17T09:05:56.4738067Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0 2025-07-17T09:05:56.4738848Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0, line 878 <- wrt source file 2025-07-17T09:05:56.4739660Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0 2025-07-17T09:05:56.4740455Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0, line 989 <- wrt source file 2025-07-17T09:05:56.4741262Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0 2025-07-17T09:05:56.4742138Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0, line 1063 <- wrt source file 2025-07-17T09:05:56.4743088Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0 2025-07-17T09:05:56.4744041Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0, line 1084 <- wrt source file 2025-07-17T09:05:56.4744964Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0 2025-07-17T09:05:56.4746044Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0, line 1108 <- wrt source file 2025-07-17T09:05:56.4747033Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0 2025-07-17T09:05:56.4747848Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0, line 1154 <- wrt source file 2025-07-17T09:05:56.4748651Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0 2025-07-17T09:05:56.4749424Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0, line 439 <- wrt source file 2025-07-17T09:05:56.4750224Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0 2025-07-17T09:05:56.4750987Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0, line 535 <- wrt source file 2025-07-17T09:05:56.4751784Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0 2025-07-17T09:05:56.4752575Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_sympy/functions.py::MinMaxBase._collapse_arguments:0, line 718 <- wrt source file 2025-07-17T09:05:56.5025364Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_sympy/functions.py::MinMaxBase._collapse_arguments:0 2025-07-17T09:05:56.5026338Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::Sampler:0, line 40 <- wrt source file 2025-07-17T09:05:56.5027107Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::Sampler:0 2025-07-17T09:05:56.5027875Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::WeightedRandomSampler:0, line 238 <- wrt source file 2025-07-17T09:05:56.5031835Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::WeightedRandomSampler:0 2025-07-17T09:05:56.5032608Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::BatchSampler:0, line 301 <- wrt source file 2025-07-17T09:05:56.5033641Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::BatchSampler:0 2025-07-17T09:05:56.5034499Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::IterableDataset:0, line 94 <- wrt source file 2025-07-17T09:05:56.5036340Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::IterableDataset:0 2025-07-17T09:05:56.5037067Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::StackDataset:0, line 219 <- wrt source file 2025-07-17T09:05:56.5037782Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::StackDataset:0 2025-07-17T09:05:56.5038464Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::random_split:0, line 441 <- wrt source file 2025-07-17T09:05:56.5039168Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::random_split:0 2025-07-17T09:05:56.5039884Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_convert:0, line 39 <- wrt source file 2025-07-17T09:05:56.5040897Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_convert:0 2025-07-17T09:05:56.5041604Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::collate:0, line 137 <- wrt source file 2025-07-17T09:05:56.5042670Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::collate:0 2025-07-17T09:05:56.5043400Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_collate:0, line 364 <- wrt source file 2025-07-17T09:05:56.5044166Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_collate:0 2025-07-17T09:05:56.5044935Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/datapipe.py::IterDataPipe:0, line 97 <- wrt source file 2025-07-17T09:05:56.5045844Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/datapipe.py::IterDataPipe:0 2025-07-17T09:05:56.5046621Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/datapipe.py::MapDataPipe:0, line 264 <- wrt source file 2025-07-17T09:05:56.5047406Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/datapipe.py::MapDataPipe:0 2025-07-17T09:05:56.5048219Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0, line 29 <- wrt source file 2025-07-17T09:05:56.5049104Z * SKIPPED: 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file 2025-07-17T09:05:56.5054294Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/callable.py::MapperMapDataPipe:0 2025-07-17T09:05:56.5055161Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0, line 34 <- wrt source file 2025-07-17T09:05:56.5056079Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0 2025-07-17T09:05:56.5056964Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0, line 29 <- wrt source file 2025-07-17T09:05:56.5057809Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0 2025-07-17T09:05:56.5058828Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0, line 25 <- wrt source file 2025-07-17T09:05:56.5059886Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0 2025-07-17T09:05:56.5060866Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0, line 38 <- wrt source file 2025-07-17T09:05:56.5077211Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0 2025-07-17T09:05:56.5078069Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0, line 88 <- wrt source file 2025-07-17T09:05:56.5078947Z * SKIPPED: 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603 <- wrt source file 2025-07-17T09:05:56.5084173Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::MultiplexerIterDataPipe:0 2025-07-17T09:05:56.5085016Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ZipperIterDataPipe:0, line 671 <- wrt source file 2025-07-17T09:05:56.5085875Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ZipperIterDataPipe:0 2025-07-17T09:05:56.5086740Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/fileopener.py::FileOpenerIterDataPipe:0, line 35 <- wrt source file 2025-07-17T09:05:56.5087630Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/fileopener.py::FileOpenerIterDataPipe:0 2025-07-17T09:05:56.5088496Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0, line 29 <- wrt source file 2025-07-17T09:05:56.5089382Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0 2025-07-17T09:05:56.5090229Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::BatcherIterDataPipe:0, line 53 <- wrt source file 2025-07-17T09:05:56.5091075Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::BatcherIterDataPipe:0 2025-07-17T09:05:56.5091906Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::UnBatcherIterDataPipe:0, line 113 <- wrt source file 2025-07-17T09:05:56.5092772Z * SKIPPED: 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wrt source file 2025-07-17T09:05:56.5098292Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combinatorics.py::ShufflerIterDataPipe:0 2025-07-17T09:05:56.5099141Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/callable.py::MapperIterDataPipe:0, line 52 <- wrt source file 2025-07-17T09:05:56.5099984Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/callable.py::MapperIterDataPipe:0 2025-07-17T09:05:56.5100804Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/callable.py::CollatorIterDataPipe:0, line 198 <- wrt source file 2025-07-17T09:05:56.5101652Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/callable.py::CollatorIterDataPipe:0 2025-07-17T09:05:56.5102508Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0, line 30 <- wrt source file 2025-07-17T09:05:56.5103398Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0 2025-07-17T09:05:56.5104234Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0, line 37 <- wrt source file 2025-07-17T09:05:56.5105062Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0 2025-07-17T09:05:56.5105994Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/utils/decoder.py::basichandlers:0, line 47 <- wrt source file 2025-07-17T09:05:56.5106839Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/utils/decoder.py::basichandlers:0 2025-07-17T09:05:56.5107637Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/_check.py::AttributeTypeIsSupportedChecker:0, line 36 <- wrt source file 2025-07-17T09:05:56.5108418Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/_check.py::AttributeTypeIsSupportedChecker:0 2025-07-17T09:05:56.5109196Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_load_for_lite_interpreter:0, line 22 <- wrt source file 2025-07-17T09:05:56.5109960Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_load_for_lite_interpreter:0 2025-07-17T09:05:56.5110725Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_get_mobile_model_contained_types:0, line 122 <- wrt source file 2025-07-17T09:05:56.5111515Z * SKIPPED: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0 2025-07-17T09:05:56.5117053Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/masked/_ops.py::logaddexp:0, line 1530 <- wrt source file 2025-07-17T09:05:56.5117725Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/masked/_ops.py::logaddexp:0 2025-07-17T09:05:56.5118428Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/masked/maskedtensor/core.py::is_masked_tensor:0, line 25 <- wrt source file 2025-07-17T09:05:56.5119197Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/masked/maskedtensor/core.py::is_masked_tensor:0 2025-07-17T09:05:56.5119863Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::uniform_:0, line 230 <- wrt source file 2025-07-17T09:05:56.5120485Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::uniform_:0 2025-07-17T09:05:56.5121081Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::normal_:0, line 257 <- wrt source file 2025-07-17T09:05:56.5121684Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::normal_:0 2025-07-17T09:05:56.5122294Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::trunc_normal_:0, line 292 <- wrt source file 2025-07-17T09:05:56.5122921Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::trunc_normal_:0 2025-07-17T09:05:56.5123525Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::constant_:0, line 306 <- wrt source file 2025-07-17T09:05:56.5124156Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::constant_:0 2025-07-17T09:05:56.5124764Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::ones_:0, line 323 <- wrt source file 2025-07-17T09:05:56.5125375Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::ones_:0 2025-07-17T09:05:56.5125979Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::zeros_:0, line 336 <- wrt source file 2025-07-17T09:05:56.5126581Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::zeros_:0 2025-07-17T09:05:56.5127165Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::eye_:0, line 352 <- wrt source file 2025-07-17T09:05:56.5127765Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::eye_:0 2025-07-17T09:05:56.5128336Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::dirac_:0, line 374 <- wrt source file 2025-07-17T09:05:56.5129082Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::dirac_:0 2025-07-17T09:05:56.5129713Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_uniform_:0, line 460 <- wrt source file 2025-07-17T09:05:56.5130551Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_uniform_:0 2025-07-17T09:05:56.5131196Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_normal_:0, line 492 <- wrt source file 2025-07-17T09:05:56.5132359Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_normal_:0 2025-07-17T09:05:56.5132999Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_uniform_:0, line 543 <- wrt source file 2025-07-17T09:05:56.5134611Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_uniform_:0 2025-07-17T09:05:56.5135253Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_normal_:0, line 608 <- wrt source file 2025-07-17T09:05:56.5136443Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_normal_:0 2025-07-17T09:05:56.5137076Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::orthogonal_:0, line 647 <- wrt source file 2025-07-17T09:05:56.5137721Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::orthogonal_:0 2025-07-17T09:05:56.5138331Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::sparse_:0, line 700 <- wrt source file 2025-07-17T09:05:56.5140124Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::sparse_:0 2025-07-17T09:05:56.5140748Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_input:0, line 32 <- wrt source file 2025-07-17T09:05:56.5151336Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_input:0 2025-07-17T09:05:56.5151977Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_weight:0, line 79 <- wrt source file 2025-07-17T09:05:56.5154422Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_weight:0 2025-07-17T09:05:56.5155038Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_input:0, line 130 <- wrt source file 2025-07-17T09:05:56.5162329Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_input:0 2025-07-17T09:05:56.5163276Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_weight:0, line 177 <- wrt source file 2025-07-17T09:05:56.5165971Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_weight:0 2025-07-17T09:05:56.5166878Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_input:0, line 228 <- wrt source file 2025-07-17T09:05:56.5235804Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_input:0 2025-07-17T09:05:56.5236680Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_weight:0, line 275 <- wrt source file 2025-07-17T09:05:56.5267867Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_weight:0 2025-07-17T09:05:56.5268780Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::fractional_max_pool2d_with_indices:0, line 464 <- wrt source file 2025-07-17T09:05:56.5306929Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::fractional_max_pool2d_with_indices:0 2025-07-17T09:05:56.5307919Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::fractional_max_pool3d_with_indices:0, line 583 <- wrt source file 2025-07-17T09:05:56.5805134Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::fractional_max_pool3d_with_indices:0 2025-07-17T09:05:56.5835758Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::gumbel_softmax:0, line 2178 <- wrt source file 2025-07-17T09:05:56.5847726Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::gumbel_softmax:0 2025-07-17T09:05:56.5848558Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::embedding:0, line 2482 <- wrt source file 2025-07-17T09:05:56.5853675Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::embedding:0 2025-07-17T09:05:56.5854502Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::embedding_bag:0, line 2622 <- wrt source file 2025-07-17T09:05:56.5866901Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::embedding_bag:0 2025-07-17T09:05:56.5867711Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::ctc_loss:0, line 3055 <- wrt source file 2025-07-17T09:05:56.5880849Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::ctc_loss:0 2025-07-17T09:05:56.5881637Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::nll_loss:0, line 3125 <- wrt source file 2025-07-17T09:05:56.5886921Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::nll_loss:0 2025-07-17T09:05:56.5887733Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::cross_entropy:0, line 3434 <- wrt source file 2025-07-17T09:05:56.5895219Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::cross_entropy:0 2025-07-17T09:05:56.5896098Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy:0, line 3499 <- wrt source file 2025-07-17T09:05:56.5899723Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy:0 2025-07-17T09:05:56.5900653Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0, line 3569 <- wrt source file 2025-07-17T09:05:56.5904960Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0 2025-07-17T09:05:56.5905895Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::pad:0, line 5267 <- wrt source file 2025-07-17T09:05:56.5912223Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::pad:0 2025-07-17T09:05:56.5913089Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0, line 119 <- wrt source file 2025-07-17T09:05:56.5913973Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0 2025-07-17T09:05:56.5914827Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/bias.py::CausalBias:0, line 95 <- wrt source file 2025-07-17T09:05:56.5915852Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/bias.py::CausalBias:0 2025-07-17T09:05:56.5916859Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/stateless.py::functional_call:0, line 196 <- wrt source file 2025-07-17T09:05:56.5917923Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/stateless.py::functional_call:0 2025-07-17T09:05:56.5918847Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_per_sample_grad.py::call_for_per_sample_grads:0, line 35 <- wrt source file 2025-07-17T09:05:56.5919831Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_per_sample_grad.py::call_for_per_sample_grads:0 2025-07-17T09:05:56.5920772Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0, line 314 <- wrt source file 2025-07-17T09:05:56.5923637Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0 2025-07-17T09:05:56.5924556Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0, line 346 <- wrt source file 2025-07-17T09:05:56.5930042Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0 2025-07-17T09:05:56.5930910Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_sequence:0, line 431 <- wrt source file 2025-07-17T09:05:56.5934691Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_sequence:0 2025-07-17T09:05:56.5945730Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0, line 492 <- wrt source file 2025-07-17T09:05:56.5946579Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0 2025-07-17T09:05:56.5947390Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pack_sequence:0, line 548 <- wrt source file 2025-07-17T09:05:56.5952147Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pack_sequence:0 2025-07-17T09:05:56.5952984Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0, line 576 <- wrt source file 2025-07-17T09:05:56.5964026Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0 2025-07-17T09:05:56.5964836Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/init.py::skip_init:0, line 33 <- wrt source file 2025-07-17T09:05:56.5974646Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/init.py::skip_init:0 2025-07-17T09:05:56.5975452Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::identity:0, line 849 <- wrt source file 2025-07-17T09:05:56.5976282Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::identity:0 2025-07-17T09:05:56.5977112Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::random_unstructured:0, line 885 <- wrt source file 2025-07-17T09:05:56.5977999Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::random_unstructured:0 2025-07-17T09:05:56.5978844Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::l1_unstructured:0, line 928 <- wrt source file 2025-07-17T09:05:56.5979861Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::l1_unstructured:0 2025-07-17T09:05:56.5980776Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::random_structured:0, line 968 <- wrt source file 2025-07-17T09:05:56.5981655Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::random_structured:0 2025-07-17T09:05:56.5982621Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::remove:0, line 1197 <- wrt source file 2025-07-17T09:05:56.5988280Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::remove:0 2025-07-17T09:05:56.5989060Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::is_pruned:0, line 1225 <- wrt source file 2025-07-17T09:05:56.5994110Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::is_pruned:0 2025-07-17T09:05:56.5994972Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0, line 265 <- wrt source file 2025-07-17T09:05:56.5995717Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0 2025-07-17T09:05:56.5996442Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::weight_norm:0, line 360 <- wrt source file 2025-07-17T09:05:56.6001361Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::weight_norm:0 2025-07-17T09:05:56.6002096Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0, line 591 <- wrt source file 2025-07-17T09:05:56.6003049Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0 2025-07-17T09:05:56.6003871Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0, line 134 <- wrt source file 2025-07-17T09:05:56.6009041Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0 2025-07-17T09:05:56.6027038Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0, line 156 <- wrt source file 2025-07-17T09:05:56.6028086Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0 2025-07-17T09:05:56.6029065Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0, line 315 <- wrt source file 2025-07-17T09:05:56.6030030Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0 2025-07-17T09:05:56.6031105Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_expanded_weights/expanded_weights_utils.py::sum_over_all_but_batch_and_last_n:0, line 178 <- wrt source file 2025-07-17T09:05:56.6032321Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_expanded_weights/expanded_weights_utils.py::sum_over_all_but_batch_and_last_n:0 2025-07-17T09:05:56.6033405Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0, line 642 <- wrt source file 2025-07-17T09:05:56.6034399Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0 2025-07-17T09:05:56.6035399Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0, line 1446 <- wrt source file 2025-07-17T09:05:56.6036760Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0 2025-07-17T09:05:56.6038025Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0, line 1999 <- wrt source file 2025-07-17T09:05:56.6039182Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0 2025-07-17T09:05:56.6040282Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1, line 2009 <- wrt source file 2025-07-17T09:05:56.6041422Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1 2025-07-17T09:05:56.6042590Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0, line 2044 <- wrt source file 2025-07-17T09:05:56.6043788Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0 2025-07-17T09:05:56.6044810Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0, line 127 <- wrt source file 2025-07-17T09:05:56.6045726Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0 2025-07-17T09:05:56.6046595Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0, line 330 <- wrt source file 2025-07-17T09:05:56.6047464Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0 2025-07-17T09:05:56.6048317Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0, line 441 <- wrt source file 2025-07-17T09:05:56.6193693Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0 2025-07-17T09:05:56.6194611Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0, line 552 <- wrt source file 2025-07-17T09:05:56.7950767Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0 2025-07-17T09:05:56.8204866Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0, line 38 <- wrt source file 2025-07-17T09:05:56.8211575Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0 2025-07-17T09:05:56.8212433Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0, line 78 <- wrt source file 2025-07-17T09:05:56.8216204Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0 2025-07-17T09:05:56.8216973Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.register_buffer:0, line 551 <- wrt source file 2025-07-17T09:05:56.8217762Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.register_buffer:0 2025-07-17T09:05:56.8218483Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.apply:0, line 1037 <- wrt source file 2025-07-17T09:05:56.8230914Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.apply:0 2025-07-17T09:05:56.8231726Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.to:0, line 1288 <- wrt source file 2025-07-17T09:05:56.8248472Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.to:0 2025-07-17T09:05:56.8249189Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.state_dict:0, line 2224 <- wrt source file 2025-07-17T09:05:56.8249944Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.state_dict:0 2025-07-17T09:05:56.8250668Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.parameters:0, line 2665 <- wrt source file 2025-07-17T09:05:56.8251420Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.parameters:0 2025-07-17T09:05:56.8252150Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_parameters:0, line 2693 <- wrt source file 2025-07-17T09:05:56.8252911Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_parameters:0 2025-07-17T09:05:56.8253629Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.buffers:0, line 2720 <- wrt source file 2025-07-17T09:05:56.8254353Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.buffers:0 2025-07-17T09:05:56.8255063Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_buffers:0, line 2747 <- wrt source file 2025-07-17T09:05:56.8255814Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_buffers:0 2025-07-17T09:05:56.8256534Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_children:0, line 2778 <- wrt source file 2025-07-17T09:05:56.8257281Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_children:0 2025-07-17T09:05:56.8257980Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.modules:0, line 2802 <- wrt source file 2025-07-17T09:05:56.8258699Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.modules:0 2025-07-17T09:05:56.8259405Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_modules:0, line 2840 <- wrt source file 2025-07-17T09:05:56.8260161Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_modules:0 2025-07-17T09:05:56.8260868Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer:0, line 90 <- wrt source file 2025-07-17T09:05:57.5191160Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer:0 2025-07-17T09:05:57.5202267Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0, line 336 <- wrt source file 2025-07-17T09:05:57.6216533Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0 2025-07-17T09:05:57.6338094Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0, line 562 <- wrt source file 2025-07-17T09:05:57.8307743Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0 2025-07-17T09:05:57.8311070Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0, line 686 <- wrt source file 2025-07-17T09:05:57.8723314Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0 2025-07-17T09:05:57.8724441Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0, line 995 <- wrt source file 2025-07-17T09:05:57.9401068Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0 2025-07-17T09:05:57.9402133Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Identity:0, line 34 <- wrt source file 2025-07-17T09:05:57.9404540Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Identity:0 2025-07-17T09:05:57.9405372Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Linear:0, line 80 <- wrt source file 2025-07-17T09:05:57.9409508Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Linear:0 2025-07-17T09:05:57.9410343Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Bilinear:0, line 179 <- wrt source file 2025-07-17T09:05:57.9454047Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Bilinear:0 2025-07-17T09:05:57.9454921Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::Upsample:0, line 77 <- wrt source file 2025-07-17T09:05:57.9471172Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::Upsample:0 2025-07-17T09:05:57.9472098Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0, line 223 <- wrt source file 2025-07-17T09:05:57.9478807Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0 2025-07-17T09:05:57.9484307Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0, line 273 <- wrt source file 2025-07-17T09:05:57.9485289Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0 2025-07-17T09:05:57.9486196Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout:0, line 60 <- wrt source file 2025-07-17T09:05:57.9487060Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout:0 2025-07-17T09:05:57.9487889Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout1d:0, line 105 <- wrt source file 2025-07-17T09:05:57.9491477Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout1d:0 2025-07-17T09:05:57.9492323Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout2d:0, line 157 <- wrt source file 2025-07-17T09:05:57.9534527Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout2d:0 2025-07-17T09:05:57.9535365Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout3d:0, line 202 <- wrt source file 2025-07-17T09:05:57.9659772Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout3d:0 2025-07-17T09:05:57.9660727Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0, line 245 <- wrt source file 2025-07-17T09:05:57.9662627Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0 2025-07-17T09:05:57.9663563Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0, line 294 <- wrt source file 2025-07-17T09:05:57.9816457Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0 2025-07-17T09:05:57.9817446Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential:0, line 81 <- wrt source file 2025-07-17T09:05:57.9818399Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential:0 2025-07-17T09:05:57.9819310Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential.append:0, line 254 <- wrt source file 2025-07-17T09:05:57.9823854Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential.append:0 2025-07-17T09:05:57.9824814Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential.insert:0, line 277 <- wrt source file 2025-07-17T09:05:57.9827322Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential.insert:0 2025-07-17T09:05:57.9828238Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential.extend:0, line 308 <- wrt source file 2025-07-17T09:05:57.9832324Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential.extend:0 2025-07-17T09:05:57.9833245Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleList:0, line 337 <- wrt source file 2025-07-17T09:05:57.9834194Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleList:0 2025-07-17T09:05:57.9835080Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleDict:0, line 517 <- wrt source file 2025-07-17T09:05:57.9835962Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleDict:0 2025-07-17T09:05:57.9836839Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterList:0, line 647 <- wrt source file 2025-07-17T09:05:57.9837764Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterList:0 2025-07-17T09:05:57.9838651Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterDict:0, line 799 <- wrt source file 2025-07-17T09:05:57.9839563Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterDict:0 2025-07-17T09:05:57.9840471Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0, line 21 <- wrt source file 2025-07-17T09:05:57.9849791Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0 2025-07-17T09:05:57.9850687Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py::Flatten:0, line 30 <- wrt source file 2025-07-17T09:05:57.9853772Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py::Flatten:0 2025-07-17T09:05:57.9854663Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0, line 187 <- wrt source file 2025-07-17T09:05:57.9863674Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0 2025-07-17T09:05:57.9864603Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0, line 303 <- wrt source file 2025-07-17T09:05:58.0064150Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0 2025-07-17T09:05:58.0065186Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0, line 419 <- wrt source file 2025-07-17T09:05:58.1296747Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0 2025-07-17T09:05:58.1437637Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad1d:0, line 70 <- wrt source file 2025-07-17T09:05:58.1444571Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad1d:0 2025-07-17T09:05:58.1445351Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad2d:0, line 122 <- wrt source file 2025-07-17T09:05:58.1461784Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad2d:0 2025-07-17T09:05:58.5652366Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad3d:0, line 187 <- wrt source file 2025-07-17T09:05:58.5653352Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad3d:0 2025-07-17T09:05:58.5961691Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0, line 241 <- wrt source file 2025-07-17T09:05:58.5969760Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0 2025-07-17T09:05:58.5970546Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0, line 294 <- wrt source file 2025-07-17T09:05:58.5974658Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0 2025-07-17T09:05:58.5975437Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0, line 350 <- wrt source file 2025-07-17T09:05:58.5991892Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0 2025-07-17T09:05:58.5992860Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0, line 395 <- wrt source file 2025-07-17T09:05:58.5997495Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0 2025-07-17T09:05:58.5998427Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0, line 439 <- wrt source file 2025-07-17T09:05:58.6002211Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0 2025-07-17T09:05:58.6003178Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0, line 497 <- wrt source file 2025-07-17T09:05:58.6005151Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0 2025-07-17T09:05:58.6006072Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0, line 556 <- wrt source file 2025-07-17T09:05:58.6011088Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0 2025-07-17T09:05:58.6012029Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0, line 600 <- wrt source file 2025-07-17T09:05:58.6017261Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0 2025-07-17T09:05:58.6018155Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0, line 658 <- wrt source file 2025-07-17T09:05:58.8945043Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0 2025-07-17T09:05:58.9267678Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0, line 692 <- wrt source file 2025-07-17T09:05:58.9276122Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0 2025-07-17T09:05:58.9279357Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0, line 747 <- wrt source file 2025-07-17T09:05:58.9280078Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0 2025-07-17T09:05:58.9280769Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0, line 806 <- wrt source file 2025-07-17T09:05:58.9298719Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0 2025-07-17T09:05:58.9299565Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0, line 129 <- wrt source file 2025-07-17T09:05:58.9307337Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0 2025-07-17T09:05:58.9308177Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0, line 206 <- wrt source file 2025-07-17T09:05:58.9347996Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0 2025-07-17T09:05:58.9348850Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0, line 289 <- wrt source file 2025-07-17T09:05:59.0192613Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0 2025-07-17T09:05:59.0193605Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0, line 363 <- wrt source file 2025-07-17T09:05:59.0201403Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0 2025-07-17T09:05:59.0202306Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0, line 545 <- wrt source file 2025-07-17T09:05:59.0531522Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0 2025-07-17T09:05:59.0532470Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0, line 636 <- wrt source file 2025-07-17T09:05:59.0540121Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0 2025-07-17T09:05:59.0541132Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0, line 731 <- wrt source file 2025-07-17T09:05:59.0618445Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0 2025-07-17T09:05:59.0619378Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0, line 847 <- wrt source file 2025-07-17T09:05:59.1585725Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0 2025-07-17T09:05:59.1586750Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0, line 937 <- wrt source file 2025-07-17T09:05:59.1611978Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0 2025-07-17T09:05:59.1612977Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0, line 1024 <- wrt source file 2025-07-17T09:05:59.1958184Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0 2025-07-17T09:05:59.1959157Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool1d:0, line 1143 <- wrt source file 2025-07-17T09:05:59.1964303Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool1d:0 2025-07-17T09:05:59.1965160Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool2d:0, line 1194 <- wrt source file 2025-07-17T09:05:59.2002632Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool2d:0 2025-07-17T09:05:59.2003491Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool3d:0, line 1253 <- wrt source file 2025-07-17T09:05:59.2976422Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool3d:0 2025-07-17T09:05:59.2977480Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0, line 1308 <- wrt source file 2025-07-17T09:05:59.2985978Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0 2025-07-17T09:05:59.2986931Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0, line 1342 <- wrt source file 2025-07-17T09:05:59.2992176Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0 2025-07-17T09:05:59.2993118Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0, line 1385 <- wrt source file 2025-07-17T09:05:59.3012985Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0 2025-07-17T09:05:59.3013918Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0, line 1432 <- wrt source file 2025-07-17T09:05:59.3017875Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0 2025-07-17T09:05:59.3018799Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0, line 1463 <- wrt source file 2025-07-17T09:05:59.3024326Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0 2025-07-17T09:05:59.3025482Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0, line 1502 <- wrt source file 2025-07-17T09:05:59.3046804Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0 2025-07-17T09:05:59.3047715Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding:0, line 71 <- wrt source file 2025-07-17T09:05:59.3055891Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding:0 2025-07-17T09:05:59.3056797Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0, line 243 <- wrt source file 2025-07-17T09:05:59.3059499Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0 2025-07-17T09:05:59.3060454Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0, line 521 <- wrt source file 2025-07-17T09:05:59.3064762Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0 2025-07-17T09:05:59.3065790Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0, line 77 <- wrt source file 2025-07-17T09:05:59.3069136Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0 2025-07-17T09:05:59.3069957Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Fold:0, line 224 <- wrt source file 2025-07-17T09:05:59.3070760Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Fold:0 2025-07-17T09:05:59.3071544Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Unfold:0, line 389 <- wrt source file 2025-07-17T09:05:59.3083940Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Unfold:0 2025-07-17T09:05:59.3084807Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Threshold:0, line 72 <- wrt source file 2025-07-17T09:05:59.3088379Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Threshold:0 2025-07-17T09:05:59.3089249Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU:0, line 114 <- wrt source file 2025-07-17T09:05:59.3092520Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU:0 2025-07-17T09:05:59.3093464Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::RReLU:0, line 173 <- wrt source file 2025-07-17T09:05:59.3096163Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::RReLU:0 2025-07-17T09:05:59.3096845Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardtanh:0, line 229 <- wrt source file 2025-07-17T09:05:59.3099365Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardtanh:0 2025-07-17T09:05:59.3101992Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU6:0, line 294 <- wrt source file 2025-07-17T09:05:59.3102677Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU6:0 2025-07-17T09:05:59.3104776Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Sigmoid:0, line 322 <- wrt source file 2025-07-17T09:05:59.3105552Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Sigmoid:0 2025-07-17T09:05:59.3106373Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0, line 354 <- wrt source file 2025-07-17T09:05:59.3107856Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0 2025-07-17T09:05:59.3108540Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanh:0, line 387 <- wrt source file 2025-07-17T09:05:59.3110642Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanh:0 2025-07-17T09:05:59.3111309Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SiLU:0, line 420 <- wrt source file 2025-07-17T09:05:59.3113424Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SiLU:0 2025-07-17T09:05:59.3114087Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Mish:0, line 459 <- wrt source file 2025-07-17T09:05:59.3116124Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Mish:0 2025-07-17T09:05:59.3116801Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardswish:0, line 504 <- wrt source file 2025-07-17T09:05:59.3119004Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardswish:0 2025-07-17T09:05:59.3119687Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ELU:0, line 547 <- wrt source file 2025-07-17T09:05:59.3122032Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ELU:0 2025-07-17T09:05:59.3122708Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::CELU:0, line 589 <- wrt source file 2025-07-17T09:05:59.3124754Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::CELU:0 2025-07-17T09:05:59.3125456Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SELU:0, line 642 <- wrt source file 2025-07-17T09:05:59.3127476Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SELU:0 2025-07-17T09:05:59.3130652Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GLU:0, line 682 <- wrt source file 2025-07-17T09:05:59.3131354Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GLU:0 2025-07-17T09:05:59.3183545Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GELU:0, line 724 <- wrt source file 2025-07-17T09:05:59.3184449Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GELU:0 2025-07-17T09:05:59.3185431Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardshrink:0, line 767 <- wrt source file 2025-07-17T09:05:59.3186354Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardshrink:0 2025-07-17T09:05:59.3187218Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LeakyReLU:0, line 816 <- wrt source file 2025-07-17T09:05:59.3188385Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LeakyReLU:0 2025-07-17T09:05:59.3189243Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSigmoid:0, line 852 <- wrt source file 2025-07-17T09:05:59.3191317Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSigmoid:0 2025-07-17T09:05:59.3192190Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softplus:0, line 885 <- wrt source file 2025-07-17T09:05:59.3193511Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softplus:0 2025-07-17T09:05:59.3194372Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softshrink:0, line 928 <- wrt source file 2025-07-17T09:05:59.3195903Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softshrink:0 2025-07-17T09:05:59.3196826Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0, line 1036 <- wrt source file 2025-07-17T09:05:59.3198259Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0 2025-07-17T09:05:59.3199146Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::PReLU:0, line 1501 <- wrt source file 2025-07-17T09:05:59.3200019Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::PReLU:0 2025-07-17T09:05:59.3200862Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softsign:0, line 1543 <- wrt source file 2025-07-17T09:05:59.3202399Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softsign:0 2025-07-17T09:05:59.3203266Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanhshrink:0, line 1566 <- wrt source file 2025-07-17T09:05:59.3205194Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanhshrink:0 2025-07-17T09:05:59.3206061Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmin:0, line 1601 <- wrt source file 2025-07-17T09:05:59.3208300Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmin:0 2025-07-17T09:05:59.3209157Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax:0, line 1659 <- wrt source file 2025-07-17T09:05:59.3211167Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax:0 2025-07-17T09:05:59.3214287Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax2d:0, line 1700 <- wrt source file 2025-07-17T09:05:59.3215191Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax2d:0 2025-07-17T09:05:59.3217478Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSoftmax:0, line 1736 <- wrt source file 2025-07-17T09:05:59.3218372Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSoftmax:0 2025-07-17T09:05:59.3230464Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNN:0, line 595 <- wrt source file 2025-07-17T09:05:59.3231374Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNN:0 2025-07-17T09:05:59.3249638Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTM:0, line 950 <- wrt source file 2025-07-17T09:05:59.3565056Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTM:0 2025-07-17T09:05:59.3565961Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRU:0, line 1285 <- wrt source file 2025-07-17T09:05:59.3579425Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRU:0 2025-07-17T09:05:59.3580243Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNNCell:0, line 1534 <- wrt source file 2025-07-17T09:05:59.3591139Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNNCell:0 2025-07-17T09:05:59.3591974Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTMCell:0, line 1656 <- wrt source file 2025-07-17T09:05:59.3599254Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTMCell:0 2025-07-17T09:05:59.3600081Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRUCell:0, line 1770 <- wrt source file 2025-07-17T09:05:59.3611071Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRUCell:0 2025-07-17T09:05:59.3611881Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::L1Loss:0, line 115 <- wrt source file 2025-07-17T09:05:59.3617268Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::L1Loss:0 2025-07-17T09:05:59.3618063Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::NLLLoss:0, line 212 <- wrt source file 2025-07-17T09:05:59.3735813Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::NLLLoss:0 2025-07-17T09:05:59.3736750Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0, line 323 <- wrt source file 2025-07-17T09:05:59.3740763Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0 2025-07-17T09:05:59.3741646Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0, line 409 <- wrt source file 2025-07-17T09:05:59.3750849Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0 2025-07-17T09:05:59.3751708Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::KLDivLoss:0, line 523 <- wrt source file 2025-07-17T09:05:59.3757233Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::KLDivLoss:0 2025-07-17T09:05:59.3758059Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MSELoss:0, line 602 <- wrt source file 2025-07-17T09:05:59.3761434Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MSELoss:0 2025-07-17T09:05:59.3762233Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCELoss:0, line 685 <- wrt source file 2025-07-17T09:05:59.3765882Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCELoss:0 2025-07-17T09:05:59.3766740Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0, line 757 <- wrt source file 2025-07-17T09:05:59.3774477Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0 2025-07-17T09:05:59.3779022Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:1, line 805 <- wrt source file 2025-07-17T09:05:59.3780095Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:1 2025-07-17T09:05:59.3780998Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0, line 951 <- wrt source file 2025-07-17T09:05:59.3784460Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0 2025-07-17T09:05:59.3785441Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0, line 1277 <- wrt source file 2025-07-17T09:05:59.3792330Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0 2025-07-17T09:05:59.3798517Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0, line 1419 <- wrt source file 2025-07-17T09:05:59.3799449Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0 2025-07-17T09:05:59.3800340Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0, line 1485 <- wrt source file 2025-07-17T09:05:59.3803101Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0 2025-07-17T09:05:59.3803964Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0, line 1565 <- wrt source file 2025-07-17T09:05:59.3809421Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0 2025-07-17T09:05:59.3818250Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0, line 1666 <- wrt source file 2025-07-17T09:05:59.3819157Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0 2025-07-17T09:05:59.3820088Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0, line 38 <- wrt source file 2025-07-17T09:05:59.3845383Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0 2025-07-17T09:05:59.3846313Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LayerNorm:0, line 151 <- wrt source file 2025-07-17T09:05:59.3851517Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LayerNorm:0 2025-07-17T09:05:59.3857608Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::GroupNorm:0, line 262 <- wrt source file 2025-07-17T09:05:59.3858527Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::GroupNorm:0 2025-07-17T09:05:59.3859430Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::RMSNorm:0, line 355 <- wrt source file 2025-07-17T09:05:59.3861504Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::RMSNorm:0 2025-07-17T09:05:59.3862399Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0, line 40 <- wrt source file 2025-07-17T09:05:59.3865669Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0 2025-07-17T09:05:59.3866579Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0, line 93 <- wrt source file 2025-07-17T09:05:59.3869578Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0 2025-07-17T09:05:59.3870477Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/dirichlet.py::Dirichlet:0, line 42 <- wrt source file 2025-07-17T09:05:59.3872813Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/dirichlet.py::Dirichlet:0 2025-07-17T09:05:59.3877568Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0, line 30 <- wrt source file 2025-07-17T09:05:59.3878506Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0 2025-07-17T09:05:59.3883918Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/lkj_cholesky.py::LKJCholesky:0, line 43 <- wrt source file 2025-07-17T09:05:59.3884855Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/lkj_cholesky.py::LKJCholesky:0 2025-07-17T09:05:59.3885807Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/fishersnedecor.py::FisherSnedecor:0, line 21 <- wrt source file 2025-07-17T09:05:59.3887620Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/fishersnedecor.py::FisherSnedecor:0 2025-07-17T09:05:59.3888536Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/laplace.py::Laplace:0, line 20 <- wrt source file 2025-07-17T09:05:59.3890750Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/laplace.py::Laplace:0 2025-07-17T09:05:59.3891613Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/studentT.py::StudentT:0, line 22 <- wrt source file 2025-07-17T09:05:59.3894353Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/studentT.py::StudentT:0 2025-07-17T09:05:59.3897379Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/cauchy.py::Cauchy:0, line 23 <- wrt source file 2025-07-17T09:05:59.3898233Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/cauchy.py::Cauchy:0 2025-07-17T09:05:59.3899143Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/inverse_gamma.py::InverseGamma:0, line 24 <- wrt source file 2025-07-17T09:05:59.3900635Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/inverse_gamma.py::InverseGamma:0 2025-07-17T09:05:59.3903770Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/beta.py::Beta:0, line 21 <- wrt source file 2025-07-17T09:05:59.3904605Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/beta.py::Beta:0 2025-07-17T09:05:59.3905612Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/categorical.py::Categorical:0, line 42 <- wrt source file 2025-07-17T09:05:59.3907415Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/categorical.py::Categorical:0 2025-07-17T09:05:59.3910540Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/half_cauchy.py::HalfCauchy:0, line 24 <- wrt source file 2025-07-17T09:05:59.3911632Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/half_cauchy.py::HalfCauchy:0 2025-07-17T09:05:59.3914582Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/continuous_bernoulli.py::ContinuousBernoulli:0, line 35 <- wrt source file 2025-07-17T09:05:59.3915658Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/continuous_bernoulli.py::ContinuousBernoulli:0 2025-07-17T09:05:59.3918495Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/generalized_pareto.py::GeneralizedPareto:0, line 26 <- wrt source file 2025-07-17T09:05:59.3919537Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/generalized_pareto.py::GeneralizedPareto:0 2025-07-17T09:05:59.3921228Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/gamma.py::Gamma:0, line 24 <- wrt source file 2025-07-17T09:05:59.3922072Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/gamma.py::Gamma:0 2025-07-17T09:05:59.3922954Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/independent.py::Independent:0, line 27 <- wrt source file 2025-07-17T09:05:59.3925772Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/independent.py::Independent:0 2025-07-17T09:05:59.3926652Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/uniform.py::Uniform:0, line 21 <- wrt source file 2025-07-17T09:05:59.3928334Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/uniform.py::Uniform:0 2025-07-17T09:05:59.3929250Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/logistic_normal.py::LogisticNormal:0, line 28 <- wrt source file 2025-07-17T09:05:59.3932768Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/logistic_normal.py::LogisticNormal:0 2025-07-17T09:05:59.3933738Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/transforms.py::CatTransform:0, line 1065 <- wrt source file 2025-07-17T09:05:59.3934681Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/transforms.py::CatTransform:0 2025-07-17T09:05:59.3935626Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/transforms.py::StackTransform:0, line 1177 <- wrt source file 2025-07-17T09:05:59.3936599Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/transforms.py::StackTransform:0 2025-07-17T09:05:59.3937419Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/transforms.py::CumulativeDistributionTransform:0, line 1253 <- wrt source file 2025-07-17T09:05:59.3938288Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/transforms.py::CumulativeDistributionTransform:0 2025-07-17T09:05:59.3939078Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/constraints.py::is_dependent:0, line 166 <- wrt source file 2025-07-17T09:05:59.3939840Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/constraints.py::is_dependent:0 2025-07-17T09:05:59.3940599Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/constraints.py::_DependentProperty:0, line 187 <- wrt source file 2025-07-17T09:05:59.3941388Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/constraints.py::_DependentProperty:0 2025-07-17T09:05:59.3942255Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/gumbel.py::Gumbel:0, line 23 <- wrt source file 2025-07-17T09:05:59.3942954Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/gumbel.py::Gumbel:0 2025-07-17T09:05:59.3943729Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/wishart.py::Wishart:0, line 39 <- wrt source file 2025-07-17T09:05:59.3944436Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/wishart.py::Wishart:0 2025-07-17T09:05:59.3945093Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/normal.py::Normal:0, line 22 <- wrt source file 2025-07-17T09:05:59.3945929Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/normal.py::Normal:0 2025-07-17T09:05:59.3946676Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/one_hot_categorical.py::OneHotCategorical:0, line 34 <- wrt source file 2025-07-17T09:05:59.3949753Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/one_hot_categorical.py::OneHotCategorical:0 2025-07-17T09:05:59.3950624Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/multivariate_normal.py::MultivariateNormal:0, line 103 <- wrt source file 2025-07-17T09:05:59.3951482Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/multivariate_normal.py::MultivariateNormal:0 2025-07-17T09:05:59.3952239Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/half_normal.py::HalfNormal:0, line 24 <- wrt source file 2025-07-17T09:05:59.3953305Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/half_normal.py::HalfNormal:0 2025-07-17T09:05:59.3957255Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/binomial.py::Binomial:0, line 31 <- wrt source file 2025-07-17T09:05:59.3957976Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/binomial.py::Binomial:0 2025-07-17T09:05:59.3962841Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/von_mises.py::VonMises:0, line 117 <- wrt source file 2025-07-17T09:05:59.3963558Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/von_mises.py::VonMises:0 2025-07-17T09:05:59.3966336Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/pareto.py::Pareto:0, line 20 <- wrt source file 2025-07-17T09:05:59.3967020Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/pareto.py::Pareto:0 2025-07-17T09:05:59.3969372Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/log_normal.py::LogNormal:0, line 23 <- wrt source file 2025-07-17T09:05:59.3970098Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/log_normal.py::LogNormal:0 2025-07-17T09:05:59.3972064Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/bernoulli.py::Bernoulli:0, line 30 <- wrt source file 2025-07-17T09:05:59.3972775Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/bernoulli.py::Bernoulli:0 2025-07-17T09:05:59.3973455Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/poisson.py::Poisson:0, line 25 <- wrt source file 2025-07-17T09:05:59.3974151Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/poisson.py::Poisson:0 2025-07-17T09:05:59.3974985Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/chi2.py::Chi2:0, line 18 <- wrt source file 2025-07-17T09:05:59.3975667Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/chi2.py::Chi2:0 2025-07-17T09:05:59.3976501Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/multinomial.py::Multinomial:0, line 38 <- wrt source file 2025-07-17T09:05:59.3977265Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/multinomial.py::Multinomial:0 2025-07-17T09:05:59.3977980Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/geometric.py::Geometric:0, line 36 <- wrt source file 2025-07-17T09:05:59.3978709Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/geometric.py::Geometric:0 2025-07-17T09:05:59.3979401Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/utils.py::clamp_probs:0, line 114 <- wrt source file 2025-07-17T09:05:59.3985517Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/utils.py::clamp_probs:0 2025-07-17T09:05:59.3986243Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/exponential.py::Exponential:0, line 20 <- wrt source file 2025-07-17T09:05:59.3988328Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/exponential.py::Exponential:0 2025-07-17T09:05:59.3989042Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/weibull.py::Weibull:0, line 22 <- wrt source file 2025-07-17T09:05:59.3991707Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/weibull.py::Weibull:0 2025-07-17T09:05:59.3992396Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::profile:0, line 182 <- wrt source file 2025-07-17T09:05:59.3993071Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::profile:0 2025-07-17T09:05:59.3993726Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::emit_itt:0, line 880 <- wrt source file 2025-07-17T09:05:59.3994417Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::emit_itt:0 2025-07-17T09:05:59.3995064Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::emit_nvtx:0, line 953 <- wrt source file 2025-07-17T09:05:59.3995731Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::emit_nvtx:0 2025-07-17T09:05:59.3996392Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::make_dual:0, line 82 <- wrt source file 2025-07-17T09:05:59.3997060Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::make_dual:0 2025-07-17T09:05:59.3997726Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::unpack_dual:0, line 151 <- wrt source file 2025-07-17T09:05:59.3998409Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::unpack_dual:0 2025-07-17T09:05:59.3999077Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::dual_level:0, line 187 <- wrt source file 2025-07-17T09:05:59.3999748Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::dual_level:0 2025-07-17T09:05:59.4000561Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.save_for_backward:0, line 71 <- wrt source file 2025-07-17T09:05:59.4001449Z * SKIPPED: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.mark_non_differentiable:0 2025-07-17T09:05:59.4007013Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0, line 243 <- wrt source file 2025-07-17T09:05:59.4007834Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0 2025-07-17T09:05:59.4008546Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::Function:0, line 485 <- wrt source file 2025-07-17T09:05:59.4009217Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::Function:0 2025-07-17T09:05:59.4009863Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::no_grad:0, line 50 <- wrt source file 2025-07-17T09:05:59.4010514Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::no_grad:0 2025-07-17T09:05:59.4011162Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::enable_grad:0, line 108 <- wrt source file 2025-07-17T09:05:59.4011875Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::enable_grad:0 2025-07-17T09:05:59.4012552Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::set_grad_enabled:0, line 166 <- wrt source file 2025-07-17T09:05:59.4013260Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::set_grad_enabled:0 2025-07-17T09:05:59.4013941Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::inference_mode:0, line 238 <- wrt source file 2025-07-17T09:05:59.4014641Z * SKIPPED: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.register_hook:0 2025-07-17T09:05:59.4019669Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.register_prehook:0, line 147 <- wrt source file 2025-07-17T09:05:59.4027627Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.register_prehook:0 2025-07-17T09:05:59.4028333Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::saved_tensors_hooks:0, line 271 <- wrt source file 2025-07-17T09:05:59.4029066Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::saved_tensors_hooks:0 2025-07-17T09:05:59.4029781Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::save_on_cpu:0, line 341 <- wrt source file 2025-07-17T09:05:59.4030458Z * SKIPPED: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0 2025-07-17T09:05:59.4052430Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vjp:0, line 293 <- wrt source file 2025-07-17T09:05:59.4053108Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vjp:0 2025-07-17T09:05:59.4053759Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::jvp:0, line 395 <- wrt source file 2025-07-17T09:05:59.4054413Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::jvp:0 2025-07-17T09:05:59.4055081Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::jacobian:0, line 630 <- wrt source file 2025-07-17T09:05:59.4055803Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::jacobian:0 2025-07-17T09:05:59.4056471Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hessian:0, line 894 <- wrt source file 2025-07-17T09:05:59.4057160Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hessian:0 2025-07-17T09:05:59.4057815Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vhp:0, line 1010 <- wrt source file 2025-07-17T09:05:59.4058492Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vhp:0 2025-07-17T09:05:59.4059293Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hvp:0, line 1109 <- wrt source file 2025-07-17T09:05:59.4059976Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hvp:0 2025-07-17T09:05:59.4060801Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::DeviceMesh:0, line 424 <- wrt source file 2025-07-17T09:05:59.4061540Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::DeviceMesh:0 2025-07-17T09:05:59.4062294Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::DeviceMesh.get_local_rank:0, line 939 <- wrt source file 2025-07-17T09:05:59.4063092Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::DeviceMesh.get_local_rank:0 2025-07-17T09:05:59.4063853Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::init_device_mesh:0, line 1022 <- wrt source file 2025-07-17T09:05:59.4064603Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::init_device_mesh:0 2025-07-17T09:05:59.4065486Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::_coalescing_manager:0, line 2574 <- wrt source file 2025-07-17T09:05:59.4066308Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::_coalescing_manager:0 2025-07-17T09:05:59.4067078Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::_time_estimator:0, line 2676 <- wrt source file 2025-07-17T09:05:59.4067890Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::_time_estimator:0 2025-07-17T09:05:59.4068656Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_object:0, line 3143 <- wrt source file 2025-07-17T09:05:59.4069448Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_object:0 2025-07-17T09:05:59.4070214Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::send_object_list:0, line 3372 <- wrt source file 2025-07-17T09:05:59.4070991Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::send_object_list:0 2025-07-17T09:05:59.4071747Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::recv_object_list:0, line 3474 <- wrt source file 2025-07-17T09:05:59.4072515Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::recv_object_list:0 2025-07-17T09:05:59.4073298Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::broadcast_object_list:0, line 3588 <- wrt source file 2025-07-17T09:05:59.4074109Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::broadcast_object_list:0 2025-07-17T09:05:59.4074899Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::scatter_object_list:0, line 3711 <- wrt source file 2025-07-17T09:05:59.4075690Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::scatter_object_list:0 2025-07-17T09:05:59.4076470Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_into_tensor:0, line 3920 <- wrt source file 2025-07-17T09:05:59.4077457Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_into_tensor:0 2025-07-17T09:05:59.4078378Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_coalesced:0, line 4058 <- wrt source file 2025-07-17T09:05:59.4079184Z * SKIPPED: 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* SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::new_subgroups_by_enumeration:0 2025-07-17T09:05:59.4089218Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/run.py::__doc__:0, line 57 <- wrt source file 2025-07-17T09:05:59.4089870Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/run.py::__doc__:0 2025-07-17T09:05:59.4090554Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/autograd/__init__.py::context:0, line 47 <- wrt source file 2025-07-17T09:05:59.4091303Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/autograd/__init__.py::context:0 2025-07-17T09:05:59.4092064Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/utils.py::register_functional_optim:0, line 37 <- wrt source file 2025-07-17T09:05:59.4092863Z * SKIPPED: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py::_get_in_backward_optimizers:0, line 114 <- wrt source file 2025-07-17T09:05:59.4098425Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py::_get_in_backward_optimizers:0 2025-07-17T09:05:59.4099243Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/_api.py::_shard_tensor:0, line 837 <- wrt source file 2025-07-17T09:05:59.4099976Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/_api.py::_shard_tensor:0 2025-07-17T09:05:59.4100803Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/_random.py::OffsetBasedRNGTracker._set_pre_op_offset:0, line 261 <- wrt source file 2025-07-17T09:05:59.4101704Z * SKIPPED: 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: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/replicate.py::replicate:0, line 190 <- wrt source file 2025-07-17T09:05:59.4116700Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/replicate.py::replicate:0 2025-07-17T09:05:59.4117502Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/checkpoint_activation.py::checkpoint:0, line 53 <- wrt source file 2025-07-17T09:05:59.4118357Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/checkpoint_activation.py::checkpoint:0 2025-07-17T09:05:59.4119152Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/pipelining/_IR.py::pipe_split:0, line 333 <- wrt source file 2025-07-17T09:05:59.4119899Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/pipelining/_IR.py::pipe_split:0 2025-07-17T09:05:59.4120711Z * DOCTEST : 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2025-07-17T09:05:59.4284294Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/more.py::unifiable:0, line 11 <- wrt source file 2025-07-17T09:05:59.4285269Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/more.py::unifiable:0 2025-07-17T09:05:59.4286212Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/more.py::reify_object:0, line 37 <- wrt source file 2025-07-17T09:05:59.4287350Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/more.py::reify_object:0 2025-07-17T09:05:59.4288392Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/more.py::unify_object:0, line 93 <- wrt source file 2025-07-17T09:05:59.4289655Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/more.py::unify_object:0 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2025-07-17T09:05:59.4296718Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher:0, line 113 <- wrt source file 2025-07-17T09:05:59.4297920Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher:0 2025-07-17T09:05:59.4299119Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.register:0, line 138 <- wrt source file 2025-07-17T09:05:59.4300375Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.register:0 2025-07-17T09:05:59.4301588Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.add:0, line 191 <- wrt source file 2025-07-17T09:05:59.4302804Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.add:0 2025-07-17T09:05:59.4304069Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.dispatch:0, line 304 <- wrt source file 2025-07-17T09:05:59.4305434Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.dispatch:0 2025-07-17T09:05:59.4306646Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::str_signature:0, line 434 <- wrt source file 2025-07-17T09:05:59.4307852Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::str_signature:0 2025-07-17T09:05:59.4309046Z * DOCTEST : 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::reverse_dict:0 2025-07-17T09:05:59.4316200Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::groupby:0, line 87 <- wrt source file 2025-07-17T09:05:59.4317329Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::groupby:0 2025-07-17T09:05:59.4318433Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::typename:0, line 117 <- wrt source file 2025-07-17T09:05:59.4319573Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::typename:0 2025-07-17T09:05:59.4320706Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::isvariadic:0, line 47 <- wrt source file 2025-07-17T09:05:59.4321873Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::isvariadic:0 2025-07-17T09:05:59.4323010Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::Variadic:0, line 83 <- wrt source file 2025-07-17T09:05:59.4324183Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::Variadic:0 2025-07-17T09:05:59.4325294Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/core.py::dispatch:0, line 20 <- wrt source file 2025-07-17T09:05:59.4326417Z * SKIPPED: 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SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_export/wrappers.py::mark_subclass_constructor_exportable_experimental:0 2025-07-17T09:05:59.4333423Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0, line 25 <- wrt source file 2025-07-17T09:05:59.4334615Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0 2025-07-17T09:05:59.4335761Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0, line 67 <- wrt source file 2025-07-17T09:05:59.4337012Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0 2025-07-17T09:05:59.4338104Z * DOCTEST : 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2025-07-17T09:05:59.4361579Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0 2025-07-17T09:05:59.4362534Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0, line 505 <- wrt source file 2025-07-17T09:05:59.4363462Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0 2025-07-17T09:05:59.4364357Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0, line 635 <- wrt source file 2025-07-17T09:05:59.4365268Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0 2025-07-17T09:05:59.4366200Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0, line 892 <- wrt source file 2025-07-17T09:05:59.4367178Z * SKIPPED: 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/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0 2025-07-17T09:05:59.4385692Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0, line 43 <- wrt source file 2025-07-17T09:05:59.4386678Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0 2025-07-17T09:05:59.4387633Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0, line 124 <- wrt source file 2025-07-17T09:05:59.4388610Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0 2025-07-17T09:05:59.4389562Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0, line 209 <- wrt source file 2025-07-17T09:05:59.4390530Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0 2025-07-17T09:05:59.4391533Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0, line 296 <- wrt source file 2025-07-17T09:05:59.4392592Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0 2025-07-17T09:05:59.4393623Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0, line 378 <- wrt source file 2025-07-17T09:05:59.4394659Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0 2025-07-17T09:05:59.4395666Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0, line 460 <- wrt source file 2025-07-17T09:05:59.4396693Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0 2025-07-17T09:05:59.4397685Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0, line 30 <- wrt source file 2025-07-17T09:05:59.4398670Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0 2025-07-17T09:05:59.4399608Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0, line 30 <- wrt source file 2025-07-17T09:05:59.4400459Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0 2025-07-17T09:05:59.4401171Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0, line 413 <- wrt source file 2025-07-17T09:05:59.4402088Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0 2025-07-17T09:05:59.4402813Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0, line 176 <- wrt source file 2025-07-17T09:05:59.4403737Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0 2025-07-17T09:05:59.4404478Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0, line 149 <- wrt source file 2025-07-17T09:05:59.4405218Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0 2025-07-17T09:05:59.4405932Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0, line 521 <- wrt source file 2025-07-17T09:05:59.4406676Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0 2025-07-17T09:05:59.4407407Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0, line 543 <- wrt source file 2025-07-17T09:05:59.4408165Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0 2025-07-17T09:05:59.4408888Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0, line 557 <- wrt source file 2025-07-17T09:05:59.4409645Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0 2025-07-17T09:05:59.4410380Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0, line 579 <- wrt source file 2025-07-17T09:05:59.4411113Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0 2025-07-17T09:05:59.4411811Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0, line 706 <- wrt source file 2025-07-17T09:05:59.4412548Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0 2025-07-17T09:05:59.4413273Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0, line 218 <- wrt source file 2025-07-17T09:05:59.4414020Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0 2025-07-17T09:05:59.4414727Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0, line 288 <- wrt source file 2025-07-17T09:05:59.4415465Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0 2025-07-17T09:05:59.4416200Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0, line 427 <- wrt source file 2025-07-17T09:05:59.4416959Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0 2025-07-17T09:05:59.4417675Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0, line 608 <- wrt source file 2025-07-17T09:05:59.4418402Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0 2025-07-17T09:05:59.4419236Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0, line 668 <- wrt source file 2025-07-17T09:05:59.4420129Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0 2025-07-17T09:05:59.4421099Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0, line 720 <- wrt source file 2025-07-17T09:05:59.4421990Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0 2025-07-17T09:05:59.4422814Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0, line 31 <- wrt source file 2025-07-17T09:05:59.4423635Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0 2025-07-17T09:05:59.4424440Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0, line 76 <- wrt source file 2025-07-17T09:05:59.4425266Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0 2025-07-17T09:05:59.4426189Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0, line 130 <- wrt source file 2025-07-17T09:05:59.4427005Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0 2025-07-17T09:05:59.4427816Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0, line 163 <- wrt source file 2025-07-17T09:05:59.4428676Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0 2025-07-17T09:05:59.4429458Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_args:0, line 110 <- wrt source file 2025-07-17T09:05:59.4430184Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_args:0 2025-07-17T09:05:59.4430919Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0, line 132 <- wrt source file 2025-07-17T09:05:59.4431697Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0 2025-07-17T09:05:59.4432452Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_pt2e:0, line 51 <- wrt source file 2025-07-17T09:05:59.4433214Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_pt2e:0 2025-07-17T09:05:59.4433963Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0, line 130 <- wrt source file 2025-07-17T09:05:59.4434738Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0 2025-07-17T09:05:59.4435486Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0, line 228 <- wrt source file 2025-07-17T09:05:59.4436233Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0 2025-07-17T09:05:59.4437137Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/utils.py::_replace_literals_with_new_placeholders:0, line 436 <- wrt source file 2025-07-17T09:05:59.4438101Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/utils.py::_replace_literals_with_new_placeholders:0 2025-07-17T09:05:59.4439073Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/prepare.py::_get_edge_or_node_to_group_id:0, line 188 <- wrt source file 2025-07-17T09:05:59.4439909Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/prepare.py::_get_edge_or_node_to_group_id:0 2025-07-17T09:05:59.4440755Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py::_get_reduction_params:0, line 102 <- wrt source file 2025-07-17T09:05:59.4441624Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py::_get_reduction_params:0 2025-07-17T09:05:59.4442481Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py::_register_custom_op:0, line 148 <- wrt source file 2025-07-17T09:05:59.4443339Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py::_register_custom_op:0 2025-07-17T09:05:59.4444207Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/backend_config/onednn.py::_fuse_linear_bn_leaky_relu:0, line 85 <- wrt source file 2025-07-17T09:05:59.4445066Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/backend_config/onednn.py::_fuse_linear_bn_leaky_relu:0 2025-07-17T09:05:59.4445985Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0, line 55 <- wrt source file 2025-07-17T09:05:59.4446956Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0 2025-07-17T09:05:59.4447815Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0, line 24 <- wrt source file 2025-07-17T09:05:59.4448603Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0 2025-07-17T09:05:59.4449387Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0, line 47 <- wrt source file 2025-07-17T09:05:59.4450217Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0 2025-07-17T09:05:59.4450941Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_logging/_internal.py::set_logs:0, line 457 <- wrt source file 2025-07-17T09:05:59.4451606Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_logging/_internal.py::set_logs:0 2025-07-17T09:05:59.4452333Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/select_algorithm.py::add_preprocessing_fn:0, line 3289 <- wrt source file 2025-07-17T09:05:59.4453102Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/select_algorithm.py::add_preprocessing_fn:0 2025-07-17T09:05:59.4453871Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/cpp_builder.py::get_name_and_dir_from_output_file_path:0, line 1457 <- wrt source file 2025-07-17T09:05:59.4454759Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/cpp_builder.py::get_name_and_dir_from_output_file_path:0 2025-07-17T09:05:59.4455537Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py::update_bn:0, line 337 <- wrt source file 2025-07-17T09:05:59.4456194Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py::update_bn:0 2025-07-17T09:05:59.4456946Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0, line 280 <- wrt source file 2025-07-17T09:05:59.4457986Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0 2025-07-17T09:05:59.4458664Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0, line 388 <- wrt source file 2025-07-17T09:05:59.4459388Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0 2025-07-17T09:05:59.4460065Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::StepLR:0, line 491 <- wrt source file 2025-07-17T09:05:59.4460720Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::StepLR:0 2025-07-17T09:05:59.4461369Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0, line 547 <- wrt source file 2025-07-17T09:05:59.4462048Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0 2025-07-17T09:05:59.4462697Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0, line 608 <- wrt source file 2025-07-17T09:05:59.4463371Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0 2025-07-17T09:05:59.4464014Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LinearLR:0, line 683 <- wrt source file 2025-07-17T09:05:59.4464672Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LinearLR:0 2025-07-17T09:05:59.4465390Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ExponentialLR:0, line 773 <- wrt source file 2025-07-17T09:05:59.4466085Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ExponentialLR:0 2025-07-17T09:05:59.4466753Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0, line 971 <- wrt source file 2025-07-17T09:05:59.4467436Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0 2025-07-17T09:05:59.4468133Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingLR:0, line 1062 <- wrt source file 2025-07-17T09:05:59.4468868Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingLR:0 2025-07-17T09:05:59.4469579Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0, line 1134 <- wrt source file 2025-07-17T09:05:59.4470297Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0 2025-07-17T09:05:59.4471054Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0, line 1794 <- wrt source file 2025-07-17T09:05:59.4471879Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0 2025-07-17T09:05:59.4472879Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1, line 1810 <- wrt source file 2025-07-17T09:05:59.4473819Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1 2025-07-17T09:05:59.4474257Z ============ 2025-07-17T09:05:59.4474435Z Finished doctests 2025-07-17T09:05:59.4474594Z 342 / 719 passed 2025-07-17T09:05:59.4474749Z  2025-07-17T09:05:59.4474945Z === Found 136 parse-time warnings === 2025-07-17T09:05:59.4475210Z --- Parse Warning: 1 / 136 --- 2025-07-17T09:05:59.4475806Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py line=1285. 2025-07-17T09:05:59.4476487Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4476902Z load(f, map_location=None, pickle_module=pickle, *, weights_only=True, mmap=None, **pickle_load_args) 2025-07-17T09:05:59.4477209Z 2025-07-17T09:05:59.4477405Z Loads an object saved with :func:`torch.save` from a file. 2025-07-17T09:05:59.4477647Z 2025-07-17T09:05:59.4477880Z :func:`torch.load` uses Python's unpickling facilities but treats storages, 2025-07-17T09:05:59.4478234Z which underlie tensors, specially. They are first deserialized on the 2025-07-17T09:05:59.4478571Z CPU and are then moved to the device they were saved from. If this fails 2025-07-17T09:05:59.4478915Z (e.g. because the run time system doesn't have certain devices), an exception 2025-07-17T09:05:59.4479263Z is raised. However, storages can be dynamically remapped to an alternative 2025-07-17T09:05:59.4479568Z set of devices using the :attr:`map_location` argument. 2025-07-17T09:05:59.4479796Z 2025-07-17T09:05:59.4480024Z If :attr:`map_location` is a callable, it will be called once for each serialized 2025-07-17T09:05:59.4480385Z storage with two arguments: storage and location. The storage argument 2025-07-17T09:05:59.4480735Z will be the initial deserialization of the storage, residing on the CPU. 2025-07-17T09:05:59.4481073Z Each serialized storage has a location tag associated with it which 2025-07-17T09:05:59.4481405Z identifies the device it was saved from, and this tag is the second 2025-07-17T09:05:59.4481765Z argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'`` 2025-07-17T09:05:59.4482125Z for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors. 2025-07-17T09:05:59.4482443Z :attr:`map_location` should return either ``None`` or a storage. If 2025-07-17T09:05:59.4482779Z :attr:`map_location` returns a storage, it will be used as the final deserialized 2025-07-17T09:05:59.4483143Z object, already moved to the right device. Otherwise, :func:`torch.load` will 2025-07-17T09:05:59.4483500Z fall back to the default behavior, as if :attr:`map_location` wasn't specified. 2025-07-17T09:05:59.4483765Z 2025-07-17T09:05:59.4483989Z If :attr:`map_location` is a :class:`torch.device` object or a string containing 2025-07-17T09:05:59.4484333Z a device tag, it indicates the location where all tensors should be loaded. 2025-07-17T09:05:59.4484594Z 2025-07-17T09:05:59.4484840Z Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags 2025-07-17T09:05:59.4485184Z appearing in the file (keys), to ones that specify where to put the 2025-07-17T09:05:59.4485441Z storages (values). 2025-07-17T09:05:59.4485612Z 2025-07-17T09:05:59.4485827Z User extensions can register their own location tags and tagging and 2025-07-17T09:05:59.4486267Z deserialization methods using :func:`torch.serialization.register_package`. 2025-07-17T09:05:59.4486610Z 2025-07-17T09:05:59.4486834Z See :ref:`layout-control` for more advanced tools to manipulate a checkpoint. 2025-07-17T09:05:59.4487101Z 2025-07-17T09:05:59.4487238Z Args: 2025-07-17T09:05:59.4487617Z f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`), 2025-07-17T09:05:59.4487995Z or a string or os.PathLike object containing a file name 2025-07-17T09:05:59.4488355Z map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage 2025-07-17T09:05:59.4488679Z locations 2025-07-17T09:05:59.4488933Z pickle_module: module used for unpickling metadata and objects (has to 2025-07-17T09:05:59.4489245Z match the :attr:`pickle_module` used to serialize file) 2025-07-17T09:05:59.4489549Z weights_only: Indicates whether unpickler should be restricted to 2025-07-17T09:05:59.4489853Z loading only tensors, primitive types, dictionaries 2025-07-17T09:05:59.4490152Z and any types added via :func:`torch.serialization.add_safe_globals`. 2025-07-17T09:05:59.4490436Z See :ref:`weights-only` for more details. 2025-07-17T09:05:59.4490786Z mmap: Indicates whether the file should be mapped rather than loading all the storages into memory. 2025-07-17T09:05:59.4491236Z Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they 2025-07-17T09:05:59.4491704Z are moved to the location that they were tagged with when saving, or specified by ``map_location``. This 2025-07-17T09:05:59.4492165Z second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the 2025-07-17T09:05:59.4492625Z tensor storages from disk to CPU memory in the first step, ``f`` is mapped, which means tensor storages 2025-07-17T09:05:59.4492979Z will be lazily loaded when their data is accessed. 2025-07-17T09:05:59.4493292Z pickle_load_args: (Python 3 only) optional keyword arguments passed over to 2025-07-17T09:05:59.4493646Z :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g., 2025-07-17T09:05:59.4493923Z :attr:`errors=...`. 2025-07-17T09:05:59.4494102Z 2025-07-17T09:05:59.4494248Z .. warning:: 2025-07-17T09:05:59.4494490Z :func:`torch.load()` unless `weights_only` parameter is set to `True`, 2025-07-17T09:05:59.4494813Z uses ``pickle`` module implicitly, which is known to be insecure. 2025-07-17T09:05:59.4495166Z It is possible to construct malicious pickle data which will execute arbitrary code 2025-07-17T09:05:59.4495546Z during unpickling. Never load data that could have come from an untrusted 2025-07-17T09:05:59.4495934Z source in an unsafe mode, or that could have been tampered with. **Only load data you trust**. 2025-07-17T09:05:59.4496226Z 2025-07-17T09:05:59.4496374Z .. note:: 2025-07-17T09:05:59.4496631Z When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors 2025-07-17T09:05:59.4497016Z will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')`` 2025-07-17T09:05:59.4497394Z and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint. 2025-07-17T09:05:59.4497679Z 2025-07-17T09:05:59.4497823Z .. note:: 2025-07-17T09:05:59.4498071Z By default, we decode byte strings as ``utf-8``. This is to avoid a common error 2025-07-17T09:05:59.4498422Z case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...`` 2025-07-17T09:05:59.4498742Z when loading files saved by Python 2 in Python 3. If this default 2025-07-17T09:05:59.4499169Z is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how 2025-07-17T09:05:59.4499603Z these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them 2025-07-17T09:05:59.4499960Z to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them 2025-07-17T09:05:59.4500427Z as byte arrays which can be decoded later with ``byte_array.decode(...)``. 2025-07-17T09:05:59.4500695Z 2025-07-17T09:05:59.4500844Z Example: 2025-07-17T09:05:59.4501035Z >>> # xdoctest: +SKIP("undefined filepaths") 2025-07-17T09:05:59.4501291Z >>> torch.load("tensors.pt", weights_only=True) 2025-07-17T09:05:59.4501528Z # Load all tensors onto the CPU 2025-07-17T09:05:59.4501733Z >>> torch.load( 2025-07-17T09:05:59.4501903Z ... "tensors.pt", 2025-07-17T09:05:59.4502117Z ... map_location=torch.device("cpu"), 2025-07-17T09:05:59.4502336Z ... weights_only=True, 2025-07-17T09:05:59.4502529Z ... ) 2025-07-17T09:05:59.4502722Z # Load all tensors onto the CPU, using a function 2025-07-17T09:05:59.4502941Z >>> torch.load( 2025-07-17T09:05:59.4503115Z ... "tensors.pt", 2025-07-17T09:05:59.4503339Z ... map_location=lambda storage, loc: storage, 2025-07-17T09:05:59.4503578Z ... weights_only=True, 2025-07-17T09:05:59.4503770Z ... ) 2025-07-17T09:05:59.4503940Z # Load all tensors onto GPU 1 2025-07-17T09:05:59.4504141Z >>> torch.load( 2025-07-17T09:05:59.4504315Z ... "tensors.pt", 2025-07-17T09:05:59.4504530Z ... map_location=lambda storage, loc: storage.cuda(1), 2025-07-17T09:05:59.4504767Z ... weights_only=True, 2025-07-17T09:05:59.4504974Z ... ) # type: ignore[attr-defined] 2025-07-17T09:05:59.4505201Z # Map tensors from GPU 1 to GPU 0 2025-07-17T09:05:59.4505460Z >>> torch.load( 2025-07-17T09:05:59.4505632Z ... "tensors.pt", 2025-07-17T09:05:59.4505835Z ... map_location={"cuda:1": "cuda:0"}, 2025-07-17T09:05:59.4506044Z ... weights_only=True, 2025-07-17T09:05:59.4506223Z ... ) 2025-07-17T09:05:59.4506396Z # Load tensor from io.BytesIO object 2025-07-17T09:05:59.4506695Z # Loading from a buffer setting weights_only=False, warning this can be unsafe 2025-07-17T09:05:59.4506998Z >>> with open("tensor.pt", "rb") as f: 2025-07-17T09:05:59.4507219Z ... buffer = io.BytesIO(f.read()) 2025-07-17T09:05:59.4507439Z >>> torch.load(buffer, weights_only=False) 2025-07-17T09:05:59.4507689Z # Load a module with 'ascii' encoding for unpickling 2025-07-17T09:05:59.4508000Z # Loading from a module setting weights_only=False, warning this can be unsafe 2025-07-17T09:05:59.4508359Z >>> torch.load("module.pt", encoding="ascii", weights_only=False) 2025-07-17T09:05:59.4508612Z 2025-07-17T09:05:59.4508849Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4509130Z 2025-07-17T09:05:59.4509284Z warnings.warn(msg) 2025-07-17T09:05:59.4509461Z 2025-07-17T09:05:59.4509685Z --- Parse Warning: 2 / 136 --- 2025-07-17T09:05:59.4510273Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=560. 2025-07-17T09:05:59.4510922Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4511198Z 2025-07-17T09:05:59.4511390Z Load a model from a github repo or a local directory. 2025-07-17T09:05:59.4511614Z 2025-07-17T09:05:59.4511819Z Note: Loading a model is the typical use case, but this can also be used to 2025-07-17T09:05:59.4512270Z for loading other objects such as tokenizers, loss functions, etc. 2025-07-17T09:05:59.4512600Z 2025-07-17T09:05:59.4512794Z If ``source`` is 'github', ``repo_or_dir`` is expected to be 2025-07-17T09:05:59.4513076Z of the form ``repo_owner/repo_name[:ref]`` with an optional 2025-07-17T09:05:59.4513320Z ref (a tag or a branch). 2025-07-17T09:05:59.4513495Z 2025-07-17T09:05:59.4513811Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a 2025-07-17T09:05:59.4514050Z path to a local directory. 2025-07-17T09:05:59.4514224Z 2025-07-17T09:05:59.4514360Z Args: 2025-07-17T09:05:59.4514539Z repo_or_dir (str): If ``source`` is 'github', 2025-07-17T09:05:59.4514860Z this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with 2025-07-17T09:05:59.4515275Z an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified, 2025-07-17T09:05:59.4515673Z the default branch is assumed to be ``main`` if it exists, and otherwise ``master``. 2025-07-17T09:05:59.4516026Z If ``source`` is 'local' then it should be a path to a local directory. 2025-07-17T09:05:59.4516337Z model (str): the name of a callable (entrypoint) defined in the 2025-07-17T09:05:59.4516594Z repo/dir's ``hubconf.py``. 2025-07-17T09:05:59.4516851Z *args (optional): the corresponding args for callable ``model``. 2025-07-17T09:05:59.4517149Z source (str, optional): 'github' or 'local'. Specifies how 2025-07-17T09:05:59.4517423Z ``repo_or_dir`` is to be interpreted. Default is 'github'. 2025-07-17T09:05:59.4517718Z trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``. 2025-07-17T09:05:59.4518048Z This parameter was introduced in v1.12 and helps ensuring that users 2025-07-17T09:05:59.4518334Z only run code from repos that they trust. 2025-07-17T09:05:59.4518530Z 2025-07-17T09:05:59.4518732Z - If ``False``, a prompt will ask the user whether the repo should 2025-07-17T09:05:59.4518988Z be trusted. 2025-07-17T09:05:59.4519212Z - If ``True``, the repo will be added to the trusted list and loaded 2025-07-17T09:05:59.4519482Z without requiring explicit confirmation. 2025-07-17T09:05:59.4519748Z - If ``"check"``, the repo will be checked against the list of 2025-07-17T09:05:59.4520058Z trusted repos in the cache. If it is not present in that list, the 2025-07-17T09:05:59.4520375Z behaviour will fall back onto the ``trust_repo=False`` option. 2025-07-17T09:05:59.4520683Z - If ``None``: this will raise a warning, inviting the user to set 2025-07-17T09:05:59.4520977Z ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This 2025-07-17T09:05:59.4521290Z is only present for backward compatibility and will be removed in 2025-07-17T09:05:59.4521543Z v2.0. 2025-07-17T09:05:59.4521698Z 2025-07-17T09:05:59.4521906Z Default is ``None`` and will eventually change to ``"check"`` in v2.0. 2025-07-17T09:05:59.4522226Z force_reload (bool, optional): whether to force a fresh download of 2025-07-17T09:05:59.4522539Z the github repo unconditionally. Does not have any effect if 2025-07-17T09:05:59.4522819Z ``source = 'local'``. Default is ``False``. 2025-07-17T09:05:59.4523093Z verbose (bool, optional): If ``False``, mute messages about hitting 2025-07-17T09:05:59.4523409Z local caches. Note that the message about first download cannot be 2025-07-17T09:05:59.4523712Z muted. Does not have any effect if ``source = 'local'``. 2025-07-17T09:05:59.4523952Z Default is ``True``. 2025-07-17T09:05:59.4524252Z skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit 2025-07-17T09:05:59.4524753Z specified by the ``github`` argument properly belongs to the repo owner. This will make 2025-07-17T09:05:59.4525208Z requests to the GitHub API; you can specify a non-default GitHub token by setting the 2025-07-17T09:05:59.4525550Z ``GITHUB_TOKEN`` environment variable. Default is ``False``. 2025-07-17T09:05:59.4525858Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2025-07-17T09:05:59.4526114Z 2025-07-17T09:05:59.4526348Z Returns: 2025-07-17T09:05:59.4526565Z The output of the ``model`` callable when called with the given 2025-07-17T09:05:59.4526824Z ``*args`` and ``**kwargs``. 2025-07-17T09:05:59.4527009Z 2025-07-17T09:05:59.4527154Z Example: 2025-07-17T09:05:59.4527336Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-07-17T09:05:59.4527558Z >>> # from a github repo 2025-07-17T09:05:59.4527752Z >>> repo = "pytorch/vision" 2025-07-17T09:05:59.4527944Z >>> model = torch.hub.load( 2025-07-17T09:05:59.4528193Z ... repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1" 2025-07-17T09:05:59.4528436Z ... ) 2025-07-17T09:05:59.4528591Z >>> # from a local directory 2025-07-17T09:05:59.4528810Z >>> path = "/some/local/path/pytorch/vision" 2025-07-17T09:05:59.4529038Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.4529309Z >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT") 2025-07-17T09:05:59.4529572Z 2025-07-17T09:05:59.4529806Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4530091Z 2025-07-17T09:05:59.4530242Z warnings.warn(msg) 2025-07-17T09:05:59.4530411Z 2025-07-17T09:05:59.4530627Z --- Parse Warning: 3 / 136 --- 2025-07-17T09:05:59.4531234Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_load_local in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=652. 2025-07-17T09:05:59.4531901Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4532170Z 2025-07-17T09:05:59.4532365Z Load a model from a local directory with a ``hubconf.py``. 2025-07-17T09:05:59.4532607Z 2025-07-17T09:05:59.4532753Z Args: 2025-07-17T09:05:59.4532962Z hubconf_dir (str): path to a local directory that contains a 2025-07-17T09:05:59.4533210Z ``hubconf.py``. 2025-07-17T09:05:59.4533446Z model (str): name of an entrypoint defined in the directory's 2025-07-17T09:05:59.4533689Z ``hubconf.py``. 2025-07-17T09:05:59.4533931Z *args (optional): the corresponding args for callable ``model``. 2025-07-17T09:05:59.4534245Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2025-07-17T09:05:59.4534496Z 2025-07-17T09:05:59.4534627Z Returns: 2025-07-17T09:05:59.4534826Z a single model with corresponding pretrained weights. 2025-07-17T09:05:59.4535053Z 2025-07-17T09:05:59.4535195Z Example: 2025-07-17T09:05:59.4535365Z >>> # xdoctest: +SKIP("stub local path") 2025-07-17T09:05:59.4535594Z >>> path = "/some/local/path/pytorch/vision" 2025-07-17T09:05:59.4535813Z >>> model = _load_local( 2025-07-17T09:05:59.4535999Z ... path, 2025-07-17T09:05:59.4536180Z ... "resnet50", 2025-07-17T09:05:59.4536393Z ... weights="ResNet50_Weights.IMAGENET1K_V1", 2025-07-17T09:05:59.4536611Z ... ) 2025-07-17T09:05:59.4536753Z 2025-07-17T09:05:59.4536983Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4537257Z 2025-07-17T09:05:59.4537404Z warnings.warn(msg) 2025-07-17T09:05:59.4537568Z 2025-07-17T09:05:59.4537769Z --- Parse Warning: 4 / 136 --- 2025-07-17T09:05:59.4538376Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=download_url_to_file in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=691. 2025-07-17T09:05:59.4539201Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4539526Z Download object at the given URL to a local path. 2025-07-17T09:05:59.4539746Z 2025-07-17T09:05:59.4539887Z Args: 2025-07-17T09:05:59.4540068Z url (str): URL of the object to download 2025-07-17T09:05:59.4540478Z dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file`` 2025-07-17T09:05:59.4540883Z hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``. 2025-07-17T09:05:59.4541218Z Default: None 2025-07-17T09:05:59.4541491Z progress (bool, optional): whether or not to display a progress bar to stderr 2025-07-17T09:05:59.4541774Z Default: True 2025-07-17T09:05:59.4541941Z 2025-07-17T09:05:59.4542090Z Example: 2025-07-17T09:05:59.4542282Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-07-17T09:05:59.4542519Z >>> # xdoctest: +REQUIRES(POSIX) 2025-07-17T09:05:59.4542747Z >>> torch.hub.download_url_to_file( 2025-07-17T09:05:59.4543050Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth", 2025-07-17T09:05:59.4543330Z ... "/tmp/temporary_file", 2025-07-17T09:05:59.4543522Z ... ) 2025-07-17T09:05:59.4543678Z 2025-07-17T09:05:59.4543823Z 2025-07-17T09:05:59.4544057Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4544327Z 2025-07-17T09:05:59.4544472Z warnings.warn(msg) 2025-07-17T09:05:59.4544646Z 2025-07-17T09:05:59.4544851Z --- Parse Warning: 5 / 136 --- 2025-07-17T09:05:59.4545540Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_state_dict_from_url in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=816. 2025-07-17T09:05:59.4545713Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4545828Z Loads the Torch serialized object at the given URL. 2025-07-17T09:05:59.4545887Z 2025-07-17T09:05:59.4546019Z If downloaded file is a zip file, it will be automatically 2025-07-17T09:05:59.4546089Z decompressed. 2025-07-17T09:05:59.4546152Z 2025-07-17T09:05:59.4546285Z If the object is already present in `model_dir`, it's deserialized and 2025-07-17T09:05:59.4546356Z returned. 2025-07-17T09:05:59.4546488Z The default value of ``model_dir`` is ``/checkpoints`` where 2025-07-17T09:05:59.4546621Z ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`. 2025-07-17T09:05:59.4546681Z 2025-07-17T09:05:59.4546743Z Args: 2025-07-17T09:05:59.4546842Z url (str): URL of the object to download 2025-07-17T09:05:59.4547001Z model_dir (str, optional): directory in which to save the object 2025-07-17T09:05:59.4547220Z map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load) 2025-07-17T09:05:59.4547376Z progress (bool, optional): whether or not to display a progress bar to stderr. 2025-07-17T09:05:59.4547457Z Default: True 2025-07-17T09:05:59.4547653Z check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention 2025-07-17T09:05:59.4547799Z ``filename-.ext`` where ```` is the first eight or more 2025-07-17T09:05:59.4547934Z digits of the SHA256 hash of the contents of the file. The hash is used to 2025-07-17T09:05:59.4548063Z ensure unique names and to verify the contents of the file. 2025-07-17T09:05:59.4548134Z Default: False 2025-07-17T09:05:59.4548434Z file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set. 2025-07-17T09:05:59.4548692Z weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects. 2025-07-17T09:05:59.4548857Z Recommended for untrusted sources. See :func:`~torch.load` for more details. 2025-07-17T09:05:59.4548915Z 2025-07-17T09:05:59.4548986Z Example: 2025-07-17T09:05:59.4549206Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-07-17T09:05:59.4549317Z >>> state_dict = torch.hub.load_state_dict_from_url( 2025-07-17T09:05:59.4549456Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth" 2025-07-17T09:05:59.4549530Z ... ) 2025-07-17T09:05:59.4549590Z 2025-07-17T09:05:59.4549655Z 2025-07-17T09:05:59.4549808Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4549879Z 2025-07-17T09:05:59.4549945Z warnings.warn(msg) 2025-07-17T09:05:59.4550000Z 2025-07-17T09:05:59.4550138Z --- Parse Warning: 6 / 136 --- 2025-07-17T09:05:59.4550606Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=446. 2025-07-17T09:05:59.4550773Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4550916Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2025-07-17T09:05:59.4550988Z 2025-07-17T09:05:59.4551105Z This is helpful when you want to visualize data over some 2025-07-17T09:05:59.4551218Z range of inputs. See below for a plotting example. 2025-07-17T09:05:59.4551275Z 2025-07-17T09:05:59.4551388Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2025-07-17T09:05:59.4551514Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2025-07-17T09:05:59.4551643Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2025-07-17T09:05:59.4551744Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2025-07-17T09:05:59.4551876Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2025-07-17T09:05:59.4551949Z to the result shape. 2025-07-17T09:05:59.4552015Z 2025-07-17T09:05:59.4552090Z .. note:: 2025-07-17T09:05:59.4552204Z 0D inputs are treated equivalently to 1D inputs of a 2025-07-17T09:05:59.4552273Z single element. 2025-07-17T09:05:59.4552341Z 2025-07-17T09:05:59.4552407Z .. warning:: 2025-07-17T09:05:59.4552520Z `torch.meshgrid(*tensors)` currently has the same behavior 2025-07-17T09:05:59.4552633Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2025-07-17T09:05:59.4552693Z 2025-07-17T09:05:59.4552808Z In the future `torch.meshgrid` will transition to 2025-07-17T09:05:59.4552891Z `indexing='xy'` as the default. 2025-07-17T09:05:59.4552956Z 2025-07-17T09:05:59.4553074Z https://github.com/pytorch/pytorch/issues/50276 tracks 2025-07-17T09:05:59.4553192Z this issue with the goal of migrating to NumPy's behavior. 2025-07-17T09:05:59.4553250Z 2025-07-17T09:05:59.4553326Z .. seealso:: 2025-07-17T09:05:59.4553383Z 2025-07-17T09:05:59.4553498Z :func:`torch.cartesian_prod` has the same effect but it 2025-07-17T09:05:59.4553590Z collects the data in a tensor of vectors. 2025-07-17T09:05:59.4553656Z 2025-07-17T09:05:59.4553719Z Args: 2025-07-17T09:05:59.4553892Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2025-07-17T09:05:59.4554002Z treated as tensors of size :math:`(1,)` automatically 2025-07-17T09:05:59.4554130Z 2025-07-17T09:05:59.4554241Z indexing: (str, optional): the indexing mode, either "xy" 2025-07-17T09:05:59.4554531Z or "ij", defaults to "ij". See warning for future changes. 2025-07-17T09:05:59.4554604Z 2025-07-17T09:05:59.4554705Z If "xy" is selected, the first dimension corresponds 2025-07-17T09:05:59.4554942Z to the cardinality of the second input and the second 2025-07-17T09:05:59.4555052Z dimension corresponds to the cardinality of the first 2025-07-17T09:05:59.4555127Z input. 2025-07-17T09:05:59.4555186Z 2025-07-17T09:05:59.4555285Z If "ij" is selected, the dimensions are in the same 2025-07-17T09:05:59.4555374Z order as the cardinality of the inputs. 2025-07-17T09:05:59.4555446Z 2025-07-17T09:05:59.4555511Z Returns: 2025-07-17T09:05:59.4555630Z seq (sequence of Tensors): If the input has :math:`N` 2025-07-17T09:05:59.4555731Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2025-07-17T09:05:59.4555851Z output will also have :math:`N` tensors, where each tensor 2025-07-17T09:05:59.4555937Z is of shape :math:`(S_0, ..., S_{N-1})`. 2025-07-17T09:05:59.4556004Z 2025-07-17T09:05:59.4556069Z Example:: 2025-07-17T09:05:59.4556137Z 2025-07-17T09:05:59.4556221Z >>> x = torch.tensor([1, 2, 3]) 2025-07-17T09:05:59.4556294Z >>> y = torch.tensor([4, 5, 6]) 2025-07-17T09:05:59.4556362Z 2025-07-17T09:05:59.4556479Z Observe the element-wise pairings across the grid, (1, 4), 2025-07-17T09:05:59.4556580Z (1, 5), ..., (3, 6). This is the same thing as the 2025-07-17T09:05:59.4556653Z cartesian product. 2025-07-17T09:05:59.4556770Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2025-07-17T09:05:59.4556831Z >>> grid_x 2025-07-17T09:05:59.4556913Z tensor([[1, 1, 1], 2025-07-17T09:05:59.4556979Z [2, 2, 2], 2025-07-17T09:05:59.4557054Z [3, 3, 3]]) 2025-07-17T09:05:59.4557115Z >>> grid_y 2025-07-17T09:05:59.4557186Z tensor([[4, 5, 6], 2025-07-17T09:05:59.4557250Z [4, 5, 6], 2025-07-17T09:05:59.4557322Z [4, 5, 6]]) 2025-07-17T09:05:59.4557385Z 2025-07-17T09:05:59.4557491Z This correspondence can be seen when these grids are 2025-07-17T09:05:59.4557566Z stacked properly. 2025-07-17T09:05:59.4557694Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2025-07-17T09:05:59.4557790Z ... torch.cartesian_prod(x, y)) 2025-07-17T09:05:59.4557853Z True 2025-07-17T09:05:59.4557924Z 2025-07-17T09:05:59.4558049Z `torch.meshgrid` is commonly used to produce a grid for 2025-07-17T09:05:59.4558126Z plotting. 2025-07-17T09:05:59.4558218Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2025-07-17T09:05:59.4558304Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2025-07-17T09:05:59.4558394Z >>> import matplotlib.pyplot as plt 2025-07-17T09:05:59.4558485Z >>> xs = torch.linspace(-5, 5, steps=100) 2025-07-17T09:05:59.4558566Z >>> ys = torch.linspace(-5, 5, steps=100) 2025-07-17T09:05:59.4558667Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2025-07-17T09:05:59.4558748Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2025-07-17T09:05:59.4558838Z >>> ax = plt.axes(projection='3d') 2025-07-17T09:05:59.4558936Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2025-07-17T09:05:59.4559014Z >>> plt.show() 2025-07-17T09:05:59.4559073Z 2025-07-17T09:05:59.4559165Z .. image:: ../_static/img/meshgrid.png 2025-07-17T09:05:59.4559302Z :width: 512 2025-07-17T09:05:59.4559409Z 2025-07-17T09:05:59.4559482Z 2025-07-17T09:05:59.4559636Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4559701Z 2025-07-17T09:05:59.4559766Z warnings.warn(msg) 2025-07-17T09:05:59.4559840Z 2025-07-17T09:05:59.4559962Z --- Parse Warning: 7 / 136 --- 2025-07-17T09:05:59.4560551Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=842. 2025-07-17T09:05:59.4560707Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4560933Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> tuple[Tensor, Tensor, Tensor] 2025-07-17T09:05:59.4560988Z 2025-07-17T09:05:59.4561093Z Returns the unique elements of the input tensor. 2025-07-17T09:05:59.4561149Z 2025-07-17T09:05:59.4561331Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2025-07-17T09:05:59.4561462Z this function also eliminates non-consecutive duplicate values. 2025-07-17T09:05:59.4561525Z 2025-07-17T09:05:59.4561660Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2025-07-17T09:05:59.4561840Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2025-07-17T09:05:59.4562007Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2025-07-17T09:05:59.4562124Z :func:`torch.unique_consecutive` which avoids the sorting. 2025-07-17T09:05:59.4562180Z 2025-07-17T09:05:59.4562242Z Args: 2025-07-17T09:05:59.4562321Z input (Tensor): the input tensor 2025-07-17T09:05:59.4562456Z sorted (bool): Whether to sort the unique elements in ascending order 2025-07-17T09:05:59.4582277Z before returning as output. 2025-07-17T09:05:59.4582463Z return_inverse (bool): Whether to also return the indices for where 2025-07-17T09:05:59.4582611Z elements in the original input ended up in the returned unique list. 2025-07-17T09:05:59.4582766Z return_counts (bool): Whether to also return the counts for each unique 2025-07-17T09:05:59.4582835Z element. 2025-07-17T09:05:59.4582969Z dim (int, optional): the dimension to operate upon. If ``None``, the 2025-07-17T09:05:59.4583098Z unique of the flattened input is returned. Otherwise, each of the 2025-07-17T09:05:59.4583218Z tensors indexed by the given dimension is treated as one of the 2025-07-17T09:05:59.4583348Z elements to apply the unique operation upon. See examples for more 2025-07-17T09:05:59.4583424Z details. Default: ``None`` 2025-07-17T09:05:59.4583480Z 2025-07-17T09:05:59.4583541Z Returns: 2025-07-17T09:05:59.4583715Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2025-07-17T09:05:59.4583768Z 2025-07-17T09:05:59.4583897Z - **output** (*Tensor*): the output list of unique scalar elements. 2025-07-17T09:05:59.4583995Z - **inverse_indices** (*Tensor*): (optional) if 2025-07-17T09:05:59.4584113Z :attr:`return_inverse` is True, there will be an additional 2025-07-17T09:05:59.4584237Z returned tensor (same shape as input) representing the indices 2025-07-17T09:05:59.4584366Z for where elements in the original input map to in the output; 2025-07-17T09:05:59.4584490Z otherwise, this function will only return a single tensor. 2025-07-17T09:05:59.4584578Z - **counts** (*Tensor*): (optional) if 2025-07-17T09:05:59.4584863Z :attr:`return_counts` is True, there will be an additional 2025-07-17T09:05:59.4585042Z returned tensor (same shape as output or output.size(dim), 2025-07-17T09:05:59.4585156Z if dim was specified) representing the number of occurrences 2025-07-17T09:05:59.4585238Z for each unique value or tensor. 2025-07-17T09:05:59.4585382Z 2025-07-17T09:05:59.4585448Z Example:: 2025-07-17T09:05:59.4585679Z 2025-07-17T09:05:59.4585815Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2025-07-17T09:05:59.4585886Z >>> output 2025-07-17T09:05:59.4585959Z tensor([1, 2, 3]) 2025-07-17T09:05:59.4586024Z 2025-07-17T09:05:59.4586115Z >>> output, inverse_indices = torch.unique( 2025-07-17T09:05:59.4586266Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2025-07-17T09:05:59.4586327Z >>> output 2025-07-17T09:05:59.4586402Z tensor([1, 2, 3]) 2025-07-17T09:05:59.4586467Z >>> inverse_indices 2025-07-17T09:05:59.4586541Z tensor([0, 2, 1, 2]) 2025-07-17T09:05:59.4586597Z 2025-07-17T09:05:59.4586681Z >>> output, inverse_indices = torch.unique( 2025-07-17T09:05:59.4586837Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2025-07-17T09:05:59.4586896Z >>> output 2025-07-17T09:05:59.4586968Z tensor([1, 2, 3]) 2025-07-17T09:05:59.4587033Z >>> inverse_indices 2025-07-17T09:05:59.4587113Z tensor([[0, 2], 2025-07-17T09:05:59.4587182Z [1, 2]]) 2025-07-17T09:05:59.4587242Z 2025-07-17T09:05:59.4587313Z >>> a = torch.tensor([ 2025-07-17T09:05:59.4587378Z ... [ 2025-07-17T09:05:59.4587446Z ... [1, 1, 0, 0], 2025-07-17T09:05:59.4587519Z ... [1, 1, 0, 0], 2025-07-17T09:05:59.4587582Z ... [0, 0, 1, 1], 2025-07-17T09:05:59.4587641Z ... ], 2025-07-17T09:05:59.4587704Z ... [ 2025-07-17T09:05:59.4587771Z ... [0, 0, 1, 1], 2025-07-17T09:05:59.4587834Z ... [0, 0, 1, 1], 2025-07-17T09:05:59.4587893Z ... [1, 1, 1, 1], 2025-07-17T09:05:59.4587959Z ... ], 2025-07-17T09:05:59.4588025Z ... [ 2025-07-17T09:05:59.4588098Z ... [1, 1, 0, 0], 2025-07-17T09:05:59.4588156Z ... [1, 1, 0, 0], 2025-07-17T09:05:59.4588227Z ... [0, 0, 1, 1], 2025-07-17T09:05:59.4588285Z ... ], 2025-07-17T09:05:59.4588345Z ... ]) 2025-07-17T09:05:59.4588400Z 2025-07-17T09:05:59.4588543Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2025-07-17T09:05:59.4588669Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2025-07-17T09:05:59.4588771Z >>> # each other, so one of them will be removed. 2025-07-17T09:05:59.4588848Z >>> (a[0, :, :] == a[2, :, :]).all() 2025-07-17T09:05:59.4588918Z tensor(True) 2025-07-17T09:05:59.4588999Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2025-07-17T09:05:59.4589070Z >>> a_unique_dim0 2025-07-17T09:05:59.4589140Z tensor([[[0, 0, 1, 1], 2025-07-17T09:05:59.4589205Z [0, 0, 1, 1], 2025-07-17T09:05:59.4589276Z [1, 1, 1, 1]], 2025-07-17T09:05:59.4589339Z [[1, 1, 0, 0], 2025-07-17T09:05:59.4589410Z [1, 1, 0, 0], 2025-07-17T09:05:59.4589473Z [0, 0, 1, 1]]]) 2025-07-17T09:05:59.4589532Z 2025-07-17T09:05:59.4589664Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2025-07-17T09:05:59.4589738Z >>> # `a_unique_dim0`: 2025-07-17T09:05:59.4589821Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2025-07-17T09:05:59.4590017Z tensor(True) 2025-07-17T09:05:59.4590164Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2025-07-17T09:05:59.4590231Z tensor(True) 2025-07-17T09:05:59.4590284Z 2025-07-17T09:05:59.4590417Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2025-07-17T09:05:59.4590648Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2025-07-17T09:05:59.4590729Z >>> # them will be removed. 2025-07-17T09:05:59.4590802Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2025-07-17T09:05:59.4590862Z tensor(True) 2025-07-17T09:05:59.4590935Z >>> torch.unique(a, dim=1) 2025-07-17T09:05:59.4590999Z tensor([[[0, 0, 1, 1], 2025-07-17T09:05:59.4591066Z [1, 1, 0, 0]], 2025-07-17T09:05:59.4591126Z [[1, 1, 1, 1], 2025-07-17T09:05:59.4591195Z [0, 0, 1, 1]], 2025-07-17T09:05:59.4591257Z [[0, 0, 1, 1], 2025-07-17T09:05:59.4591328Z [1, 1, 0, 0]]]) 2025-07-17T09:05:59.4591386Z 2025-07-17T09:05:59.4591517Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2025-07-17T09:05:59.4591619Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2025-07-17T09:05:59.4591742Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2025-07-17T09:05:59.4591819Z >>> # sub-tensors will be removed. 2025-07-17T09:05:59.4591891Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2025-07-17T09:05:59.4591955Z tensor(True) 2025-07-17T09:05:59.4592031Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2025-07-17T09:05:59.4592093Z tensor(True) 2025-07-17T09:05:59.4592167Z >>> torch.unique(a, dim=2) 2025-07-17T09:05:59.4592231Z tensor([[[0, 1], 2025-07-17T09:05:59.4592286Z [0, 1], 2025-07-17T09:05:59.4592357Z [1, 0]], 2025-07-17T09:05:59.4592419Z [[1, 0], 2025-07-17T09:05:59.4592486Z [1, 0], 2025-07-17T09:05:59.4592541Z [1, 1]], 2025-07-17T09:05:59.4592606Z [[0, 1], 2025-07-17T09:05:59.4592666Z [0, 1], 2025-07-17T09:05:59.4592735Z [1, 0]]]) 2025-07-17T09:05:59.4592789Z 2025-07-17T09:05:59.4592955Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4593015Z 2025-07-17T09:05:59.4593085Z warnings.warn(msg) 2025-07-17T09:05:59.4593142Z 2025-07-17T09:05:59.4593318Z --- Parse Warning: 8 / 136 --- 2025-07-17T09:05:59.4593815Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Library.fallback in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=374. 2025-07-17T09:05:59.4593979Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-07-17T09:05:59.4594129Z Registers the function implementation as the fallback for the given key. 2025-07-17T09:05:59.4594185Z 2025-07-17T09:05:59.4594324Z This function only works for a library with global namespace ("_"). 2025-07-17T09:05:59.4594378Z 2025-07-17T09:05:59.4594443Z Args: 2025-07-17T09:05:59.4594616Z fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel` 2025-07-17T09:05:59.4594697Z to register a fallthrough. 2025-07-17T09:05:59.4594893Z dispatch_key: dispatch key that the input function should be registered for. By default, it uses 2025-07-17T09:05:59.4595010Z the dispatch key that the library was created with. 2025-07-17T09:05:59.4595229Z with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument 2025-07-17T09:05:59.4595496Z to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls. 2025-07-17T09:05:59.4595625Z 2025-07-17T09:05:59.4595699Z Example:: 2025-07-17T09:05:59.4595753Z 2025-07-17T09:05:59.4595841Z >>> my_lib = Library("_", "IMPL") 2025-07-17T09:05:59.4595935Z >>> def fallback_kernel(op, *args, **kwargs): 2025-07-17T09:05:59.4596127Z >>> # Handle all autocast ops generically 2025-07-17T09:05:59.4596196Z >>> # ... 2025-07-17T09:05:59.4596301Z >>> my_lib.fallback(fallback_kernel, "Autocast") 2025-07-17T09:05:59.4596356Z 2025-07-17T09:05:59.4596724Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7)) 2025-07-17T09:05:59.4596778Z 2025-07-17T09:05:59.4596864Z my_lib.fallback(fallback_kernel, "Autocast") 2025-07-17T09:05:59.4596925Z ^ 2025-07-17T09:05:59.4596992Z warnings.warn(msg) 2025-07-17T09:05:59.4597053Z 2025-07-17T09:05:59.4597175Z --- Parse Warning: 9 / 136 --- 2025-07-17T09:05:59.4597669Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_fake in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=933. 2025-07-17T09:05:59.4597822Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-07-17T09:05:59.4597974Z Register a FakeTensor implementation ("fake impl") for this operator. 2025-07-17T09:05:59.4598031Z 2025-07-17T09:05:59.4598152Z Also sometimes known as a "meta kernel", "abstract impl". 2025-07-17T09:05:59.4598207Z 2025-07-17T09:05:59.4598360Z An "FakeTensor implementation" specifies the behavior of this operator on 2025-07-17T09:05:59.4598496Z Tensors that carry no data ("FakeTensor"). Given some input Tensors with 2025-07-17T09:05:59.4598649Z certain properties (sizes/strides/storage_offset/device), it specifies 2025-07-17T09:05:59.4598740Z what the properties of the output Tensors are. 2025-07-17T09:05:59.4598806Z 2025-07-17T09:05:59.4598943Z The FakeTensor implementation has the same signature as the operator. 2025-07-17T09:05:59.4599073Z It is run for both FakeTensors and meta tensors. To write a FakeTensor 2025-07-17T09:05:59.4599205Z implementation, assume that all Tensor inputs to the operator are 2025-07-17T09:05:59.4599338Z regular CPU/CUDA/Meta tensors, but they do not have storage, and 2025-07-17T09:05:59.4599458Z you are trying to return regular CPU/CUDA/Meta tensor(s) as output. 2025-07-17T09:05:59.4599599Z The FakeTensor implementation must consist of only PyTorch operations 2025-07-17T09:05:59.4599719Z (and may not directly access the storage or data of any input or 2025-07-17T09:05:59.4599798Z intermediate Tensors). 2025-07-17T09:05:59.4599857Z 2025-07-17T09:05:59.4599966Z This API may be used as a decorator (see examples). 2025-07-17T09:05:59.4600024Z 2025-07-17T09:05:59.4600114Z For a detailed guide on custom ops, please see 2025-07-17T09:05:59.4600263Z https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html 2025-07-17T09:05:59.4600319Z 2025-07-17T09:05:59.4600384Z Args: 2025-07-17T09:05:59.4600518Z op_name: Operator name (along with the overload) or OpOverload object. 2025-07-17T09:05:59.4600607Z func: Fake tensor implementation. 2025-07-17T09:05:59.4600727Z lib (Optional[Library]): Library to register the fake tensor to. 2025-07-17T09:05:59.4600849Z allow_override: Flag controlling if we want to override an 2025-07-17T09:05:59.4600955Z existing registered fake impl. This is by default off, 2025-07-17T09:05:59.4601138Z and will error you're trying to register a fake impl to 2025-07-17T09:05:59.4601299Z an operator that already has a fake impl. This also only 2025-07-17T09:05:59.4601403Z applies if the custom operator was not created via 2025-07-17T09:05:59.4601514Z torch.library.custom_op, as overriding and existing fake 2025-07-17T09:05:59.4601709Z impl is already allowed. 2025-07-17T09:05:59.4601768Z 2025-07-17T09:05:59.4601839Z Examples: 2025-07-17T09:05:59.4601901Z >>> import torch 2025-07-17T09:05:59.4601973Z >>> import numpy as np 2025-07-17T09:05:59.4602049Z >>> from torch import Tensor 2025-07-17T09:05:59.4602112Z >>> 2025-07-17T09:05:59.4602222Z >>> # Example 1: an operator without data-dependent output shape 2025-07-17T09:05:59.4602359Z >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=()) 2025-07-17T09:05:59.4602495Z >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2025-07-17T09:05:59.4602602Z >>> raise NotImplementedError("Implementation goes here") 2025-07-17T09:05:59.4602659Z >>> 2025-07-17T09:05:59.4602765Z >>> @torch.library.register_fake("mylib::custom_linear") 2025-07-17T09:05:59.4602845Z >>> def _(x, weight, bias): 2025-07-17T09:05:59.4602919Z >>> assert x.dim() == 2 2025-07-17T09:05:59.4602995Z >>> assert weight.dim() == 2 2025-07-17T09:05:59.4603068Z >>> assert bias.dim() == 1 2025-07-17T09:05:59.4603157Z >>> assert x.shape[1] == weight.shape[1] 2025-07-17T09:05:59.4603241Z >>> assert weight.shape[0] == bias.shape[0] 2025-07-17T09:05:59.4603326Z >>> assert x.device == weight.device 2025-07-17T09:05:59.4603386Z >>> 2025-07-17T09:05:59.4603475Z >>> return (x @ weight.t()) + bias 2025-07-17T09:05:59.4603535Z >>> 2025-07-17T09:05:59.4603654Z >>> with torch._subclasses.fake_tensor.FakeTensorMode(): 2025-07-17T09:05:59.4603731Z >>> x = torch.randn(2, 3) 2025-07-17T09:05:59.4603801Z >>> w = torch.randn(3, 3) 2025-07-17T09:05:59.4603867Z >>> b = torch.randn(3) 2025-07-17T09:05:59.4603967Z >>> y = torch.ops.mylib.custom_linear(x, w, b) 2025-07-17T09:05:59.4604027Z >>> 2025-07-17T09:05:59.4604099Z >>> assert y.shape == (2, 3) 2025-07-17T09:05:59.4604162Z >>> 2025-07-17T09:05:59.4604269Z >>> # Example 2: an operator with data-dependent output shape 2025-07-17T09:05:59.4604405Z >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=()) 2025-07-17T09:05:59.4604491Z >>> def custom_nonzero(x: Tensor) -> Tensor: 2025-07-17T09:05:59.4604575Z >>> x_np = x.numpy(force=True) 2025-07-17T09:05:59.4604665Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2025-07-17T09:05:59.4604761Z >>> return torch.tensor(res, device=x.device) 2025-07-17T09:05:59.4604818Z >>> 2025-07-17T09:05:59.4604938Z >>> @torch.library.register_fake("mylib::custom_nonzero") 2025-07-17T09:05:59.4604998Z >>> def _(x): 2025-07-17T09:05:59.4605099Z >>> # Number of nonzero-elements is data-dependent. 2025-07-17T09:05:59.4605198Z >>> # Since we cannot peek at the data in an fake impl, 2025-07-17T09:05:59.4605303Z >>> # we use the ctx object to construct a new symint that 2025-07-17T09:05:59.4605392Z >>> # represents the data-dependent size. 2025-07-17T09:05:59.4605472Z >>> ctx = torch.library.get_ctx() 2025-07-17T09:05:59.4605545Z >>> nnz = ctx.new_dynamic_size() 2025-07-17T09:05:59.4605624Z >>> shape = [nnz, x.dim()] 2025-07-17T09:05:59.4605725Z >>> result = x.new_empty(shape, dtype=torch.int64) 2025-07-17T09:05:59.4605878Z >>> return result 2025-07-17T09:05:59.4605987Z >>> 2025-07-17T09:05:59.4606110Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2025-07-17T09:05:59.4606173Z >>> 2025-07-17T09:05:59.4606249Z >>> x = torch.tensor([0, 1, 2, 3, 4, 0]) 2025-07-17T09:05:59.4606505Z >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x) 2025-07-17T09:05:59.4606581Z >>> trace.print_readable() 2025-07-17T09:05:59.4606647Z >>> 2025-07-17T09:05:59.4606776Z >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x)) 2025-07-17T09:05:59.4606836Z 2025-07-17T09:05:59.4606889Z 2025-07-17T09:05:59.4607198Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('', 38, 1, '_._ = None\n', 38, 2)) 2025-07-17T09:05:59.4607254Z 2025-07-17T09:05:59.4607323Z _._ = None 2025-07-17T09:05:59.4607385Z ^ 2025-07-17T09:05:59.4607456Z warnings.warn(msg) 2025-07-17T09:05:59.4607509Z 2025-07-17T09:05:59.4607647Z --- Parse Warning: 10 / 136 --- 2025-07-17T09:05:59.4608130Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_autograd in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=1068. 2025-07-17T09:05:59.4608294Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4608391Z Register a backward formula for this custom op. 2025-07-17T09:05:59.4608452Z 2025-07-17T09:05:59.4608574Z In order for an operator to work with autograd, you need to register 2025-07-17T09:05:59.4608640Z a backward formula: 2025-07-17T09:05:59.4608770Z 1. You must tell us how to compute gradients during the backward pass 2025-07-17T09:05:59.4608849Z by providing us a "backward" function. 2025-07-17T09:05:59.4608985Z 2. If you need any values from the forward to compute gradients, you can 2025-07-17T09:05:59.4609082Z use `setup_context` to save values for backward. 2025-07-17T09:05:59.4609143Z 2025-07-17T09:05:59.4609280Z ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``: 2025-07-17T09:05:59.4609404Z - ``grads`` is one or more gradients. The number of gradients matches 2025-07-17T09:05:59.4609482Z the number of outputs of the operator. 2025-07-17T09:05:59.4609620Z The ``ctx`` object is `the same ctx object `_ used by 2025-07-17T09:05:59.4609760Z :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the 2025-07-17T09:05:59.4609872Z same as :meth:`torch.autograd.Function.backward`. 2025-07-17T09:05:59.4609929Z 2025-07-17T09:05:59.4610063Z ``setup_context(ctx, inputs, output)`` runs during the forward pass. 2025-07-17T09:05:59.4610200Z Please save quantities needed for backward onto the ``ctx`` object via 2025-07-17T09:05:59.4610339Z either :meth:`torch.autograd.function.FunctionCtx.save_for_backward` 2025-07-17T09:05:59.4610461Z or assigning them as attributes of ``ctx``. If your custom op has 2025-07-17T09:05:59.4610592Z kwarg-only arguments, we expect the signature of ``setup_context`` 2025-07-17T09:05:59.4610714Z to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``. 2025-07-17T09:05:59.4610773Z 2025-07-17T09:05:59.4610899Z Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is, 2025-07-17T09:05:59.4611040Z they may not directly access :meth:`torch.Tensor.data_ptr` and they must 2025-07-17T09:05:59.4611181Z not depend on or mutate global state. If you need a non-traceable backward, 2025-07-17T09:05:59.4611318Z you can make it a separate custom_op that you call inside ``backward_fn``. 2025-07-17T09:05:59.4611370Z 2025-07-17T09:05:59.4611565Z If you need different autograd behavior on different devices, then we 2025-07-17T09:05:59.4611762Z recommend creating two different custom operators, one for each device 2025-07-17T09:05:59.4611890Z that needs different behavior, and switching between them at runtime. 2025-07-17T09:05:59.4611948Z 2025-07-17T09:05:59.4612007Z Examples: 2025-07-17T09:05:59.4612078Z >>> import torch 2025-07-17T09:05:59.4612253Z >>> import numpy as np 2025-07-17T09:05:59.4612331Z >>> from torch import Tensor 2025-07-17T09:05:59.4612392Z >>> 2025-07-17T09:05:59.4612521Z >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=()) 2025-07-17T09:05:59.4612616Z >>> def numpy_sin(x: Tensor) -> Tensor: 2025-07-17T09:05:59.4612690Z >>> x_np = x.cpu().numpy() 2025-07-17T09:05:59.4612765Z >>> y_np = np.sin(x_np) 2025-07-17T09:05:59.4612867Z >>> return torch.from_numpy(y_np).to(device=x.device) 2025-07-17T09:05:59.4612933Z >>> 2025-07-17T09:05:59.4613033Z >>> def setup_context(ctx, inputs, output) -> Tensor: 2025-07-17T09:05:59.4613106Z >>> x, = inputs 2025-07-17T09:05:59.4613178Z >>> ctx.save_for_backward(x) 2025-07-17T09:05:59.4613239Z >>> 2025-07-17T09:05:59.4613307Z >>> def backward(ctx, grad): 2025-07-17T09:05:59.4613385Z >>> x, = ctx.saved_tensors 2025-07-17T09:05:59.4613451Z >>> return grad * x.cos() 2025-07-17T09:05:59.4613514Z >>> 2025-07-17T09:05:59.4613596Z >>> torch.library.register_autograd( 2025-07-17T09:05:59.4613711Z ... "mylib::numpy_sin", backward, setup_context=setup_context 2025-07-17T09:05:59.4613770Z ... ) 2025-07-17T09:05:59.4613830Z >>> 2025-07-17T09:05:59.4613924Z >>> x = torch.randn(3, requires_grad=True) 2025-07-17T09:05:59.4613990Z >>> y = numpy_sin(x) 2025-07-17T09:05:59.4614102Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2025-07-17T09:05:59.4614184Z >>> assert torch.allclose(grad_x, x.cos()) 2025-07-17T09:05:59.4614243Z >>> 2025-07-17T09:05:59.4614324Z >>> # Example with a keyword-only arg 2025-07-17T09:05:59.4614445Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2025-07-17T09:05:59.4614548Z >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor: 2025-07-17T09:05:59.4614620Z >>> x_np = x.cpu().numpy() 2025-07-17T09:05:59.4614688Z >>> y_np = x_np * val 2025-07-17T09:05:59.4614789Z >>> return torch.from_numpy(y_np).to(device=x.device) 2025-07-17T09:05:59.4614847Z >>> 2025-07-17T09:05:59.4614988Z >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor: 2025-07-17T09:05:59.4615073Z >>> ctx.val = keyword_only_inputs["val"] 2025-07-17T09:05:59.4615137Z >>> 2025-07-17T09:05:59.4615205Z >>> def backward(ctx, grad): 2025-07-17T09:05:59.4615275Z >>> return grad * ctx.val 2025-07-17T09:05:59.4615329Z >>> 2025-07-17T09:05:59.4615405Z >>> torch.library.register_autograd( 2025-07-17T09:05:59.4615517Z ... "mylib::numpy_mul", backward, setup_context=setup_context 2025-07-17T09:05:59.4615575Z ... ) 2025-07-17T09:05:59.4615635Z >>> 2025-07-17T09:05:59.4615711Z >>> x = torch.randn(3, requires_grad=True) 2025-07-17T09:05:59.4615789Z >>> y = numpy_mul(x, val=3.14) 2025-07-17T09:05:59.4615890Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2025-07-17T09:05:59.4616010Z >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14)) 2025-07-17T09:05:59.4616067Z 2025-07-17T09:05:59.4616126Z 2025-07-17T09:05:59.4616287Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4616436Z 2025-07-17T09:05:59.4616558Z warnings.warn(msg) 2025-07-17T09:05:59.4616618Z 2025-07-17T09:05:59.4616740Z --- Parse Warning: 11 / 136 --- 2025-07-17T09:05:59.4617204Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=opcheck in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=1482. 2025-07-17T09:05:59.4617468Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4617609Z Given an operator and some sample arguments, tests if the operator is 2025-07-17T09:05:59.4617677Z registered correctly. 2025-07-17T09:05:59.4617737Z 2025-07-17T09:05:59.4617892Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2025-07-17T09:05:59.4618041Z custom op, you specified metadata (e.g. mutability info) about the custom op 2025-07-17T09:05:59.4618173Z and these APIs require that the functions you pass them satisfy certain 2025-07-17T09:05:59.4618336Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2025-07-17T09:05:59.4618425Z ``opcheck`` tests these metadata and properties. 2025-07-17T09:05:59.4618490Z 2025-07-17T09:05:59.4618564Z Concretely, we test the following: 2025-07-17T09:05:59.4618624Z 2025-07-17T09:05:59.4618733Z - test_schema: If the schema matches the implementation of 2025-07-17T09:05:59.4618893Z the operator. For example: if the schema specifies a Tensor is mutated, 2025-07-17T09:05:59.4619014Z then we check the implementation mutates the Tensor. If the schema 2025-07-17T09:05:59.4619130Z specifies that we return a new Tensor, then we check that the 2025-07-17T09:05:59.4619256Z implementation returns a new Tensor (instead of an existing one or 2025-07-17T09:05:59.4619329Z a view of an existing one). 2025-07-17T09:05:59.4619450Z - test_autograd_registration: If the operator supports training 2025-07-17T09:05:59.4619593Z (autograd): we check that its autograd formula is registered via 2025-07-17T09:05:59.4619716Z torch.library.register_autograd or a manual registration to one 2025-07-17T09:05:59.4619859Z or more DispatchKey::Autograd keys. Any other DispatchKey-based 2025-07-17T09:05:59.4619958Z registrations may lead to undefined behavior. 2025-07-17T09:05:59.4620076Z - test_faketensor: If the operator has a FakeTensor kernel 2025-07-17T09:05:59.4620183Z (and if it is correct). The FakeTensor kernel is necessary ( 2025-07-17T09:05:59.4620322Z but not sufficient) for the operator to work with PyTorch compilation 2025-07-17T09:05:59.4620448Z APIs (torch.compile/export/FX). We check that a FakeTensor kernel 2025-07-17T09:05:59.4620566Z (also sometimes known as a meta kernel) was registered for the 2025-07-17T09:05:59.4620677Z operator and that it is correct. This test takes the result of 2025-07-17T09:05:59.4620790Z running the operator on real tensors and the result of running 2025-07-17T09:05:59.4620908Z the operator on FakeTensors and checks that they have the same 2025-07-17T09:05:59.4621002Z Tensor metadata (sizes/strides/dtype/device/etc). 2025-07-17T09:05:59.4621126Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2025-07-17T09:05:59.4621234Z with PyTorch compilation APIs (torch.compile/export/FX). 2025-07-17T09:05:59.4621359Z This checks that the outputs (and gradients, if applicable) are the 2025-07-17T09:05:59.4621452Z same under eager-mode PyTorch and torch.compile. 2025-07-17T09:05:59.4621566Z This test is a superset of ``test_faketensor`` and is an e2e test; 2025-07-17T09:05:59.4621667Z other things it tests are that the operator supports 2025-07-17T09:05:59.4621804Z functionalization and that the backward pass (if it exists) also 2025-07-17T09:05:59.4621957Z supports FakeTensor and functionalization. 2025-07-17T09:05:59.4622064Z 2025-07-17T09:05:59.4622179Z For best results, please call ``opcheck`` multiple times with a 2025-07-17T09:05:59.4622291Z representative set of inputs. If your operator supports 2025-07-17T09:05:59.4622429Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2025-07-17T09:05:59.4622665Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2025-07-17T09:05:59.4622766Z use ``opcheck`` with inputs on all supported devices. 2025-07-17T09:05:59.4622819Z 2025-07-17T09:05:59.4622874Z Args: 2025-07-17T09:05:59.4622984Z op: The operator. Must either be a function decorated with 2025-07-17T09:05:59.4623123Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2025-07-17T09:05:59.4623255Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2025-07-17T09:05:59.4623331Z args: The args to the operator 2025-07-17T09:05:59.4623412Z kwargs: The kwargs to the operator 2025-07-17T09:05:59.4623519Z test_utils: Tests that we should run. Default: all of them. 2025-07-17T09:05:59.4623607Z Example: ("test_schema", "test_faketensor") 2025-07-17T09:05:59.4623725Z raise_exception: If we should raise an exception on the first 2025-07-17T09:05:59.4623830Z error. If False, we will return a dict with information 2025-07-17T09:05:59.4623912Z on if each test passed or not. 2025-07-17T09:05:59.4624052Z rtol (Optional[float]): Relative tolerance for floating point comparisons. 2025-07-17T09:05:59.4624137Z If specified ``atol`` must also be specified. 2025-07-17T09:05:59.4624251Z If omitted, default values based on the ``dtype`` are selected 2025-07-17T09:05:59.4624353Z (see the table in :func:`torch.testing.assert_close`). 2025-07-17T09:05:59.4624488Z atol (Optional[float]): Absolute tolerance for floating point comparisons. 2025-07-17T09:05:59.4624573Z If specified ``rtol`` must also be specified. 2025-07-17T09:05:59.4624678Z If omitted, default values based on the ``dtype`` are selected 2025-07-17T09:05:59.4624773Z (see the table in :func:`torch.testing.assert_close`). 2025-07-17T09:05:59.4624827Z 2025-07-17T09:05:59.4624896Z .. warning:: 2025-07-17T09:05:59.4624949Z 2025-07-17T09:05:59.4625081Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2025-07-17T09:05:59.4625199Z opcheck tests if your usage of torch.library APIs is correct while 2025-07-17T09:05:59.4625439Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2025-07-17T09:05:59.4625565Z mathematically correct. Use both to test custom ops that support 2025-07-17T09:05:59.4625638Z gradient computation. 2025-07-17T09:05:59.4625690Z 2025-07-17T09:05:59.4625753Z Example: 2025-07-17T09:05:59.4625805Z 2025-07-17T09:05:59.4625887Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-07-17T09:05:59.4626013Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2025-07-17T09:05:59.4626100Z >>> def numpy_mul(x: Tensor, y: float) -> Tensor: 2025-07-17T09:05:59.4626176Z >>> x_np = x.numpy(force=True) 2025-07-17T09:05:59.4626245Z >>> z_np = x_np * y 2025-07-17T09:05:59.4626334Z >>> return torch.from_numpy(z_np).to(x.device) 2025-07-17T09:05:59.4626391Z >>> 2025-07-17T09:05:59.4626462Z >>> @numpy_mul.register_fake 2025-07-17T09:05:59.4626527Z >>> def _(x, y): 2025-07-17T09:05:59.4626601Z >>> return torch.empty_like(x) 2025-07-17T09:05:59.4626655Z >>> 2025-07-17T09:05:59.4626741Z >>> def setup_context(ctx, inputs, output): 2025-07-17T09:05:59.4626880Z >>> y, = inputs 2025-07-17T09:05:59.4627005Z >>> ctx.y = y 2025-07-17T09:05:59.4627061Z >>> 2025-07-17T09:05:59.4627134Z >>> def backward(ctx, grad): 2025-07-17T09:05:59.4627208Z >>> return grad * ctx.y, None 2025-07-17T09:05:59.4627263Z >>> 2025-07-17T09:05:59.4627513Z >>> numpy_mul.register_autograd(backward, setup_context=setup_context) 2025-07-17T09:05:59.4627568Z >>> 2025-07-17T09:05:59.4627639Z >>> sample_inputs = [ 2025-07-17T09:05:59.4627713Z >>> (torch.randn(3), 3.14), 2025-07-17T09:05:59.4627800Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2025-07-17T09:05:59.4627895Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2025-07-17T09:05:59.4628011Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2025-07-17T09:05:59.4628066Z >>> ] 2025-07-17T09:05:59.4628123Z >>> 2025-07-17T09:05:59.4628197Z >>> for args in sample_inputs: 2025-07-17T09:05:59.4628285Z >>> torch.library.opcheck(numpy_mul, args) 2025-07-17T09:05:59.4628339Z 2025-07-17T09:05:59.4628397Z 2025-07-17T09:05:59.4628544Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4628605Z 2025-07-17T09:05:59.4628668Z warnings.warn(msg) 2025-07-17T09:05:59.4628727Z 2025-07-17T09:05:59.4628855Z --- Parse Warning: 12 / 136 --- 2025-07-17T09:05:59.4629329Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Tensor.dim_order in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py line=1493. 2025-07-17T09:05:59.4629490Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4629543Z 2025-07-17T09:05:59.4629627Z dim_order(ambiguity_check=False) -> tuple 2025-07-17T09:05:59.4629684Z 2025-07-17T09:05:59.4629832Z Returns the uniquely determined tuple of int describing the dim order or 2025-07-17T09:05:59.4629904Z physical layout of :attr:`self`. 2025-07-17T09:05:59.4629959Z 2025-07-17T09:05:59.4630108Z The dim order represents how dimensions are laid out in memory of dense tensors, 2025-07-17T09:05:59.4630216Z starting from the outermost to the innermost dimension. 2025-07-17T09:05:59.4630274Z 2025-07-17T09:05:59.4630390Z Note that the dim order may not always be uniquely determined. 2025-07-17T09:05:59.4630614Z If `ambiguity_check` is True, this function raises a RuntimeError when the dim order cannot be uniquely determined; 2025-07-17T09:05:59.4630841Z If `ambiguity_check` is a list of memory formats, this function raises a RuntimeError when tensor can not be interpreted 2025-07-17T09:05:59.4630988Z into exactly one of the given memory formats, or it cannot be uniquely determined. 2025-07-17T09:05:59.4631179Z If `ambiguity_check` is False, it will return one of legal dim order(s) without checking its uniqueness. 2025-07-17T09:05:59.4631252Z Otherwise, it will raise TypeError. 2025-07-17T09:05:59.4631306Z 2025-07-17T09:05:59.4631362Z Args: 2025-07-17T09:05:59.4631555Z ambiguity_check (bool or List[torch.memory_format]): The check method for ambiguity of dim order. 2025-07-17T09:05:59.4631610Z 2025-07-17T09:05:59.4631676Z Examples:: 2025-07-17T09:05:59.4631730Z 2025-07-17T09:05:59.4631807Z >>> torch.empty((2, 3, 5, 7)).dim_order() 2025-07-17T09:05:59.4631869Z (0, 1, 2, 3) 2025-07-17T09:05:59.4631965Z >>> torch.empty((2, 3, 5, 7)).transpose(1, 2).dim_order() 2025-07-17T09:05:59.4632026Z (0, 2, 1, 3) 2025-07-17T09:05:59.4632145Z >>> torch.empty((2, 3, 5, 7), memory_format=torch.channels_last).dim_order() 2025-07-17T09:05:59.4632208Z (0, 2, 3, 1) 2025-07-17T09:05:59.4632277Z >>> torch.empty((1, 2, 3, 4)).dim_order() 2025-07-17T09:05:59.4632403Z (0, 1, 2, 3) 2025-07-17T09:05:59.4632508Z >>> try: 2025-07-17T09:05:59.4632619Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check=True) 2025-07-17T09:05:59.4632691Z ... except RuntimeError as e: 2025-07-17T09:05:59.4632758Z ... print(e) 2025-07-17T09:05:59.4632938Z The tensor does not have unique dim order, or cannot map to exact one of the given memory formats. 2025-07-17T09:05:59.4633135Z >>> torch.empty((1, 2, 3, 4)).dim_order( 2025-07-17T09:05:59.4633262Z ... ambiguity_check=[torch.contiguous_format, torch.channels_last] 2025-07-17T09:05:59.4633348Z ... ) # It can be mapped to contiguous format 2025-07-17T09:05:59.4633403Z (0, 1, 2, 3) 2025-07-17T09:05:59.4633462Z >>> try: 2025-07-17T09:05:59.4633574Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check="ILLEGAL") 2025-07-17T09:05:59.4633646Z ... except TypeError as e: 2025-07-17T09:05:59.4633712Z ... print(e) 2025-07-17T09:05:59.4633853Z The ambiguity_check argument must be a bool or a list of memory formats. 2025-07-17T09:05:59.4633916Z 2025-07-17T09:05:59.4633980Z .. warning:: 2025-07-17T09:05:59.4634105Z The dim_order tensor API is experimental and subject to change. 2025-07-17T09:05:59.4634161Z 2025-07-17T09:05:59.4634310Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4634367Z 2025-07-17T09:05:59.4634433Z warnings.warn(msg) 2025-07-17T09:05:59.4634485Z 2025-07-17T09:05:59.4634612Z --- Parse Warning: 13 / 136 --- 2025-07-17T09:05:59.4635066Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=sum in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py line=202. 2025-07-17T09:05:59.4635222Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4635326Z Return the sum of each row of the given sparse tensor. 2025-07-17T09:05:59.4635386Z 2025-07-17T09:05:59.4635519Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2025-07-17T09:05:59.4635644Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2025-07-17T09:05:59.4635766Z reduce over all of them. When sum over all ``sparse_dim``, this method 2025-07-17T09:05:59.4635866Z returns a dense tensor instead of a sparse tensor. 2025-07-17T09:05:59.4635919Z 2025-07-17T09:05:59.4636074Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2025-07-17T09:05:59.4636190Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2025-07-17T09:05:59.4636242Z 2025-07-17T09:05:59.4636375Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2025-07-17T09:05:59.4636515Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2025-07-17T09:05:59.4636578Z 2025-07-17T09:05:59.4636635Z Args: 2025-07-17T09:05:59.4636727Z input (Tensor): the input sparse tensor 2025-07-17T09:05:59.4636891Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2025-07-17T09:05:59.4636956Z over all dims. 2025-07-17T09:05:59.4637112Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2025-07-17T09:05:59.4637188Z Default: dtype of :attr:`input`. 2025-07-17T09:05:59.4637240Z 2025-07-17T09:05:59.4637305Z Example:: 2025-07-17T09:05:59.4637359Z 2025-07-17T09:05:59.4637422Z >>> nnz = 3 2025-07-17T09:05:59.4637488Z >>> dims = [5, 5, 2, 3] 2025-07-17T09:05:59.4637587Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2025-07-17T09:05:59.4637702Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2025-07-17T09:05:59.4637843Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2025-07-17T09:05:59.4637964Z >>> size = torch.Size(dims) 2025-07-17T09:05:59.4638058Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-07-17T09:05:59.4638148Z >>> S = torch.sparse_coo_tensor(I, V, size) 2025-07-17T09:05:59.4638203Z >>> S 2025-07-17T09:05:59.4638279Z tensor(indices=tensor([[2, 0, 3], 2025-07-17T09:05:59.4638453Z [2, 4, 1]]), 2025-07-17T09:05:59.4638541Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2025-07-17T09:05:59.4638615Z [ 0.3411, 0.0918, -0.2312]], 2025-07-17T09:05:59.4638669Z 2025-07-17T09:05:59.4638740Z [[ 0.5348, 0.0634, -2.0494], 2025-07-17T09:05:59.4638812Z [-0.7125, -1.0646, 2.1844]], 2025-07-17T09:05:59.4638864Z 2025-07-17T09:05:59.4638935Z [[ 0.1276, 0.1874, -0.6334], 2025-07-17T09:05:59.4639012Z [-1.9682, -0.5340, 0.7483]]]), 2025-07-17T09:05:59.4639110Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2025-07-17T09:05:59.4639169Z 2025-07-17T09:05:59.4639287Z # when sum over only part of sparse_dims, return a sparse tensor 2025-07-17T09:05:59.4639363Z >>> torch.sparse.sum(S, [1, 3]) 2025-07-17T09:05:59.4639437Z tensor(indices=tensor([[0, 2, 3]]), 2025-07-17T09:05:59.4639520Z values=tensor([[-1.4512, 0.4073], 2025-07-17T09:05:59.4639586Z [-0.8901, 0.2017], 2025-07-17T09:05:59.4639655Z [-0.3183, -1.7539]]), 2025-07-17T09:05:59.4639737Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2025-07-17T09:05:59.4639792Z 2025-07-17T09:05:59.4639888Z # when sum over all sparse dim, return a dense tensor 2025-07-17T09:05:59.4639962Z # with summed dims squeezed 2025-07-17T09:05:59.4640037Z >>> torch.sparse.sum(S, [0, 1, 3]) 2025-07-17T09:05:59.4640109Z tensor([-2.6596, -1.1450]) 2025-07-17T09:05:59.4640188Z 2025-07-17T09:05:59.4640344Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4640400Z 2025-07-17T09:05:59.4640467Z warnings.warn(msg) 2025-07-17T09:05:59.4640524Z 2025-07-17T09:05:59.4640655Z --- Parse Warning: 14 / 136 --- 2025-07-17T09:05:59.4641138Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=is_available in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/accelerator/__init__.py line=66. 2025-07-17T09:05:59.4641285Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-07-17T09:05:59.4641431Z Check if the current accelerator is available at runtime: it was build, all the 2025-07-17T09:05:59.4641565Z required drivers are available and at least one device is visible. 2025-07-17T09:05:59.4641660Z See :ref:`accelerator` for details. 2025-07-17T09:05:59.4641715Z 2025-07-17T09:05:59.4641770Z Returns: 2025-07-17T09:05:59.4641935Z bool: A boolean indicating if there is an available :ref:`accelerator`. 2025-07-17T09:05:59.4641995Z 2025-07-17T09:05:59.4642142Z .. note:: This API delegates to the device-specific version of `is_available`. 2025-07-17T09:05:59.4642299Z On CUDA, when the environment variable ``PYTORCH_NVML_BASED_CUDA_CHECK=1`` is set, 2025-07-17T09:05:59.4642448Z this function will NOT poison fork. Otherwise, it will. For more details, see 2025-07-17T09:05:59.4642547Z :ref:`multiprocessing-poison-fork-note`. 2025-07-17T09:05:59.4642601Z 2025-07-17T09:05:59.4642661Z Example:: 2025-07-17T09:05:59.4642715Z 2025-07-17T09:05:59.4642874Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2025-07-17T09:05:59.4643048Z 2025-07-17T09:05:59.4643362Z Original Error: SyntaxError('invalid syntax', ('', 1, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 1, 78)) 2025-07-17T09:05:59.4643417Z 2025-07-17T09:05:59.4643573Z assert torch.accelerator.is_available() "No available accelerators detected." 2025-07-17T09:05:59.4643743Z ^ 2025-07-17T09:05:59.4643816Z warnings.warn(msg) 2025-07-17T09:05:59.4643872Z 2025-07-17T09:05:59.4643998Z --- Parse Warning: 15 / 136 --- 2025-07-17T09:05:59.4644486Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=synchronize in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/accelerator/__init__.py line=212. 2025-07-17T09:05:59.4644637Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-07-17T09:05:59.4644772Z Wait for all kernels in all streams on the given device to complete. 2025-07-17T09:05:59.4644826Z 2025-07-17T09:05:59.4644882Z Args: 2025-07-17T09:05:59.4645071Z device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match 2025-07-17T09:05:59.4645213Z the current :ref:`accelerator` device type. If not given, 2025-07-17T09:05:59.4645337Z use :func:`torch.accelerator.current_device_index` by default. 2025-07-17T09:05:59.4645390Z 2025-07-17T09:05:59.4645569Z .. note:: This function is a no-op if the current :ref:`accelerator` is not initialized. 2025-07-17T09:05:59.4645625Z 2025-07-17T09:05:59.4645685Z Example:: 2025-07-17T09:05:59.4645740Z 2025-07-17T09:05:59.4645831Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-07-17T09:05:59.4645991Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2025-07-17T09:05:59.4646086Z >>> start_event = torch.Event(enable_timing=True) 2025-07-17T09:05:59.4646180Z >>> end_event = torch.Event(enable_timing=True) 2025-07-17T09:05:59.4646249Z >>> start_event.record() 2025-07-17T09:05:59.4646398Z >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator()) 2025-07-17T09:05:59.4646472Z >>> sum = torch.sum(tensor) 2025-07-17T09:05:59.4646541Z >>> end_event.record() 2025-07-17T09:05:59.4646626Z >>> torch.accelerator.synchronize() 2025-07-17T09:05:59.4646736Z >>> elapsed_time_ms = start_event.elapsed_time(end_event) 2025-07-17T09:05:59.4646792Z 2025-07-17T09:05:59.4647106Z Original Error: SyntaxError('invalid syntax', ('', 2, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 2, 78)) 2025-07-17T09:05:59.4647160Z 2025-07-17T09:05:59.4647314Z assert torch.accelerator.is_available() "No available accelerators detected." 2025-07-17T09:05:59.4647381Z ^ 2025-07-17T09:05:59.4647450Z warnings.warn(msg) 2025-07-17T09:05:59.4647504Z 2025-07-17T09:05:59.4647619Z --- Parse Warning: 16 / 136 --- 2025-07-17T09:05:59.4648081Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=cudart in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/__init__.py line=434. 2025-07-17T09:05:59.4648224Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-07-17T09:05:59.4648302Z Retrieves the CUDA runtime API module. 2025-07-17T09:05:59.4648355Z 2025-07-17T09:05:59.4648411Z 2025-07-17T09:05:59.4648560Z This function initializes the CUDA runtime environment if it is not already 2025-07-17T09:05:59.4648708Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2025-07-17T09:05:59.4648907Z runtime API module provides access to various CUDA runtime functions. 2025-07-17T09:05:59.4649010Z 2025-07-17T09:05:59.4649067Z Args: 2025-07-17T09:05:59.4649128Z ``None`` 2025-07-17T09:05:59.4649183Z 2025-07-17T09:05:59.4649240Z Returns: 2025-07-17T09:05:59.4649334Z module: The CUDA runtime API module (_cudart). 2025-07-17T09:05:59.4649391Z 2025-07-17T09:05:59.4649548Z Raises: 2025-07-17T09:05:59.4649691Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2025-07-17T09:05:59.4649902Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2025-07-17T09:05:59.4649959Z 2025-07-17T09:05:59.4650046Z Example of CUDA operations with profiling: 2025-07-17T09:05:59.4650113Z >>> import torch 2025-07-17T09:05:59.4650201Z >>> from torch.cuda import cudart, check_error 2025-07-17T09:05:59.4650266Z >>> import os 2025-07-17T09:05:59.4650331Z >>> 2025-07-17T09:05:59.4650409Z >>> os.environ["CUDA_PROFILE"] = "1" 2025-07-17T09:05:59.4650465Z >>> 2025-07-17T09:05:59.4650551Z >>> def perform_cuda_operations_with_streams(): 2025-07-17T09:05:59.4650625Z >>> stream = torch.cuda.Stream() 2025-07-17T09:05:59.4650709Z >>> with torch.cuda.stream(stream): 2025-07-17T09:05:59.4650795Z >>> x = torch.randn(100, 100, device='cuda') 2025-07-17T09:05:59.4650869Z >>> y = torch.randn(100, 100, device='cuda') 2025-07-17T09:05:59.4650942Z >>> z = torch.mul(x, y) 2025-07-17T09:05:59.4651005Z >>> return z 2025-07-17T09:05:59.4651069Z >>> 2025-07-17T09:05:59.4651145Z >>> torch.cuda.synchronize() 2025-07-17T09:05:59.4651233Z >>> print("====== Start nsys profiling ======") 2025-07-17T09:05:59.4651323Z >>> check_error(cudart().cudaProfilerStart()) 2025-07-17T09:05:59.4651408Z >>> with torch.autograd.profiler.emit_nvtx(): 2025-07-17T09:05:59.4651509Z >>> result = perform_cuda_operations_with_streams() 2025-07-17T09:05:59.4651592Z >>> print("CUDA operations completed.") 2025-07-17T09:05:59.4651699Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2025-07-17T09:05:59.4651782Z >>> print("====== End nsys profiling ======") 2025-07-17T09:05:59.4651842Z 2025-07-17T09:05:59.4651964Z To run this example and save the profiling information, execute: 2025-07-17T09:05:59.4652188Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2025-07-17T09:05:59.4652246Z 2025-07-17T09:05:59.4652395Z This command profiles the CUDA operations in the provided script and saves 2025-07-17T09:05:59.4652519Z the profiling information to a file named `trace_name.prof`. 2025-07-17T09:05:59.4652661Z The `--profile-from-start off` option ensures that profiling starts only 2025-07-17T09:05:59.4652754Z after the `cudaProfilerStart` call in the script. 2025-07-17T09:05:59.4652884Z The `--csv` and `--print-summary` options format the profiling output as a 2025-07-17T09:05:59.4652968Z CSV file and print a summary, respectively. 2025-07-17T09:05:59.4653119Z The `-o` option specifies the output file name, and the `-f` option forces the 2025-07-17T09:05:59.4653216Z overwrite of the output file if it already exists. 2025-07-17T09:05:59.4653274Z 2025-07-17T09:05:59.4653631Z 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)) 2025-07-17T09:05:59.4653685Z 2025-07-17T09:05:59.4653881Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2025-07-17T09:05:59.4654006Z ^ 2025-07-17T09:05:59.4654129Z warnings.warn(msg) 2025-07-17T09:05:59.4654190Z 2025-07-17T09:05:59.4654307Z --- Parse Warning: 17 / 136 --- 2025-07-17T09:05:59.4654810Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Future.then in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py line=101. 2025-07-17T09:05:59.4655084Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4655169Z 2025-07-17T09:05:59.4655310Z Append the given callback function to this ``Future``, which will be run 2025-07-17T09:05:59.4655437Z when the ``Future`` is completed. Multiple callbacks can be added to 2025-07-17T09:05:59.4655559Z the same ``Future``, but the order in which they will be executed cannot 2025-07-17T09:05:59.4655668Z be guaranteed (to enforce a certain order consider chaining: 2025-07-17T09:05:59.4655812Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2025-07-17T09:05:59.4655938Z is the reference to this ``Future``. The callback function can use the 2025-07-17T09:05:59.4656066Z :meth:`value` method to get the value. Note that if this ``Future`` is 2025-07-17T09:05:59.4656199Z already completed, the given callback will be run immediately inline. 2025-07-17T09:05:59.4656292Z 2025-07-17T09:05:59.4656420Z If the ``Future``'s value contains tensors that reside on GPUs, the 2025-07-17T09:05:59.4656558Z callback might be invoked while the async kernels that are populating 2025-07-17T09:05:59.4656688Z those tensors haven't yet finished executing on the device. However, the 2025-07-17T09:05:59.4656815Z callback will be invoked with some dedicated streams set as current 2025-07-17T09:05:59.4656941Z (fetched from a global pool) which will be synchronized with those 2025-07-17T09:05:59.4657117Z kernels. Hence any operation performed by the callback on these tensors 2025-07-17T09:05:59.4657257Z will be scheduled on the device after the kernels complete. In other 2025-07-17T09:05:59.4657377Z words, as long as the callback doesn't switch streams, it can safely 2025-07-17T09:05:59.4657507Z manipulate the result without any additional synchronization. This is 2025-07-17T09:05:59.4657606Z similar to the non-blocking behavior of :meth:`wait`. 2025-07-17T09:05:59.4657686Z 2025-07-17T09:05:59.4657822Z Similarly, if the callback returns a value that contains tensors that 2025-07-17T09:05:59.4657936Z reside on a GPU, it can do so even if the kernels that are producing 2025-07-17T09:05:59.4658083Z these tensors are still running on the device, as long as the callback 2025-07-17T09:05:59.4658209Z didn't change streams during its execution. If one wants to change 2025-07-17T09:05:59.4658333Z streams, one must be careful to re-synchronize them with the original 2025-07-17T09:05:59.4658457Z streams, that is, those that were current when the callback was invoked. 2025-07-17T09:05:59.4658520Z 2025-07-17T09:05:59.4658600Z Args: 2025-07-17T09:05:59.4658724Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2025-07-17T09:05:59.4658818Z the only argument. 2025-07-17T09:05:59.4658871Z 2025-07-17T09:05:59.4658938Z Returns: 2025-07-17T09:05:59.4659045Z A new ``Future`` object that holds the return value of the 2025-07-17T09:05:59.4659173Z ``callback`` and will be marked as completed when the given 2025-07-17T09:05:59.4659252Z ``callback`` finishes. 2025-07-17T09:05:59.4659309Z 2025-07-17T09:05:59.4659420Z .. note:: Note that if the callback function throws, either 2025-07-17T09:05:59.4659558Z through the original future being completed with an exception and 2025-07-17T09:05:59.4659672Z calling ``fut.wait()``, or through other code in the callback, the 2025-07-17T09:05:59.4659979Z future returned by ``then`` will be marked appropriately with the 2025-07-17T09:05:59.4660151Z encountered error. However, if this callback later completes 2025-07-17T09:05:59.4660276Z additional futures, those futures are not marked as completed with 2025-07-17T09:05:59.4660393Z an error and the user is responsible for handling completion/waiting 2025-07-17T09:05:59.4660508Z on those futures independently. 2025-07-17T09:05:59.4660663Z 2025-07-17T09:05:59.4660757Z Example:: 2025-07-17T09:05:59.4660819Z 2025-07-17T09:05:59.4660912Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2025-07-17T09:05:59.4660984Z >>> def callback(fut): 2025-07-17T09:05:59.4661070Z ... print(f"RPC return value is {fut.wait()}.") 2025-07-17T09:05:59.4661180Z >>> fut = torch.futures.Future() 2025-07-17T09:05:59.4661290Z >>> # The inserted callback will print the return value when 2025-07-17T09:05:59.4661374Z >>> # receiving the response from "worker1" 2025-07-17T09:05:59.4661463Z >>> cb_fut = fut.then(callback) 2025-07-17T09:05:59.4661550Z >>> chain_cb_fut = cb_fut.then( 2025-07-17T09:05:59.4661644Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2025-07-17T09:05:59.4661707Z ... ) 2025-07-17T09:05:59.4661773Z >>> fut.set_result(5) 2025-07-17T09:05:59.4661860Z RPC return value is 5. 2025-07-17T09:05:59.4661928Z Chained cb done. None 2025-07-17T09:05:59.4661990Z 2025-07-17T09:05:59.4662143Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4662202Z 2025-07-17T09:05:59.4662286Z warnings.warn(msg) 2025-07-17T09:05:59.4662340Z 2025-07-17T09:05:59.4662472Z --- Parse Warning: 18 / 136 --- 2025-07-17T09:05:59.4662954Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Future.set_result in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py line=211. 2025-07-17T09:05:59.4663126Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4663180Z 2025-07-17T09:05:59.4663306Z Set the result for this ``Future``, which will mark this ``Future`` as 2025-07-17T09:05:59.4663433Z completed and trigger all attached callbacks. Note that a ``Future`` 2025-07-17T09:05:59.4663524Z cannot be marked completed twice. 2025-07-17T09:05:59.4663594Z 2025-07-17T09:05:59.4663732Z If the result contains tensors that reside on GPUs, this method can be 2025-07-17T09:05:59.4663854Z called even if the asynchronous kernels that are populating those 2025-07-17T09:05:59.4663988Z tensors haven't yet completed running on the device, provided that the 2025-07-17T09:05:59.4664140Z streams on which those kernels were enqueued are set as the current ones 2025-07-17T09:05:59.4664266Z when this method is called. Put simply, it's safe to call this method 2025-07-17T09:05:59.4664393Z immediately after launching those kernels, without any additional 2025-07-17T09:05:59.4664534Z synchronization, as long as one doesn't change streams in between. This 2025-07-17T09:05:59.4664678Z method will record events on all the relevant current streams and will 2025-07-17T09:05:59.4664797Z use them to ensure proper scheduling for all the consumers of this 2025-07-17T09:05:59.4664853Z ``Future``. 2025-07-17T09:05:59.4664918Z 2025-07-17T09:05:59.4664972Z Args: 2025-07-17T09:05:59.4665099Z result (object): the result object of this ``Future``. 2025-07-17T09:05:59.4665151Z 2025-07-17T09:05:59.4665211Z Example:: 2025-07-17T09:05:59.4665345Z 2025-07-17T09:05:59.4665437Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2025-07-17T09:05:59.4665507Z >>> import threading 2025-07-17T09:05:59.4665569Z >>> import time 2025-07-17T09:05:59.4665665Z >>> def slow_set_future(fut, value): 2025-07-17T09:05:59.4665807Z ... time.sleep(0.5) 2025-07-17T09:05:59.4665970Z ... fut.set_result(value) 2025-07-17T09:05:59.4666044Z >>> fut = torch.futures.Future() 2025-07-17T09:05:59.4666121Z >>> t = threading.Thread( 2025-07-17T09:05:59.4666216Z ... target=slow_set_future, 2025-07-17T09:05:59.4666303Z ... args=(fut, torch.ones(2) * 3) 2025-07-17T09:05:59.4666360Z ... ) 2025-07-17T09:05:59.4666561Z >>> t.start() 2025-07-17T09:05:59.4666632Z >>> print(fut.wait()) 2025-07-17T09:05:59.4666694Z tensor([3., 3.]) 2025-07-17T09:05:59.4666778Z >>> t.join() 2025-07-17T09:05:59.4666833Z 2025-07-17T09:05:59.4666988Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4667040Z 2025-07-17T09:05:59.4667116Z warnings.warn(msg) 2025-07-17T09:05:59.4667170Z 2025-07-17T09:05:59.4667293Z --- Parse Warning: 19 / 136 --- 2025-07-17T09:05:59.4667835Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=compute_required_storage_length in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims_common/__init__.py line=1848. 2025-07-17T09:05:59.4667993Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4668123Z Computes the minimum storage size to hold the given tensor geometry. 2025-07-17T09:05:59.4668206Z 2025-07-17T09:05:59.4668264Z Example 2025-07-17T09:05:59.4668345Z ======= 2025-07-17T09:05:59.4668396Z 2025-07-17T09:05:59.4668537Z This is the size of a newly allocated tensor's storage, in units of elements 2025-07-17T09:05:59.4668608Z 2025-07-17T09:05:59.4668688Z >>> t = torch.empty((10, 20)) 2025-07-17T09:05:59.4668831Z >>> compute_required_storage_length(t.shape, t.stride(), t.storage_offset()) 2025-07-17T09:05:59.4668887Z 200 2025-07-17T09:05:59.4668949Z 2025-07-17T09:05:59.4669020Z >>> # xdoctest: +SKIP(failing) 2025-07-17T09:05:59.4669110Z >>> t2 = torch.empty_strided((1, 2, 3), (5, 7, 11)) 2025-07-17T09:05:59.4669218Z >>> size = compute_required_storage_length( 2025-07-17T09:05:59.4669322Z ... t2.shape, t2.stride(), t2.storage_offset() 2025-07-17T09:05:59.4669379Z ... ) 2025-07-17T09:05:59.4669456Z >>> size == t.storage().size() 2025-07-17T09:05:59.4669517Z True 2025-07-17T09:05:59.4669579Z 2025-07-17T09:05:59.4669697Z A valid tensor may have a larger storage size, but never smaller 2025-07-17T09:05:59.4669802Z 2025-07-17T09:05:59.4669890Z >>> slice = torch.empty(100)[20:40] 2025-07-17T09:05:59.4669961Z >>> slice.storage().size() 2025-07-17T09:05:59.4670019Z 100 2025-07-17T09:05:59.4670080Z 2025-07-17T09:05:59.4670159Z >>> compute_required_storage_length( 2025-07-17T09:05:59.4670260Z ... slice.shape, slice.stride(), slice.storage_offset() 2025-07-17T09:05:59.4670329Z ... ) 2025-07-17T09:05:59.4670389Z 40 2025-07-17T09:05:59.4670446Z 2025-07-17T09:05:59.4670499Z 2025-07-17T09:05:59.4670669Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4670724Z 2025-07-17T09:05:59.4670815Z warnings.warn(msg) 2025-07-17T09:05:59.4670873Z 2025-07-17T09:05:59.4670993Z --- Parse Warning: 20 / 136 --- 2025-07-17T09:05:59.4671488Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=compile_shader in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/mps/__init__.py line=145. 2025-07-17T09:05:59.4671646Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4671771Z Compiles compute shader from source and allows one to invoke kernels 2025-07-17T09:05:59.4671870Z defined there from the comfort of Python runtime 2025-07-17T09:05:59.4672000Z Example:: 2025-07-17T09:05:59.4672126Z 2025-07-17T09:05:59.4672211Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_MPS) 2025-07-17T09:05:59.4672285Z >>> lib = torch.mps.compile_shader( 2025-07-17T09:05:59.4672508Z ... "kernel void full(device float* out, constant float& val, uint idx [[thread_position_in_grid]]) { out[idx] = val; }" 2025-07-17T09:05:59.4672564Z ... ) 2025-07-17T09:05:59.4672754Z >>> x = torch.zeros(16, device="mps") 2025-07-17T09:05:59.4672822Z >>> lib.full(x, 3.14) 2025-07-17T09:05:59.4672877Z 2025-07-17T09:05:59.4673053Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4673141Z 2025-07-17T09:05:59.4673202Z warnings.warn(msg) 2025-07-17T09:05:59.4673286Z 2025-07-17T09:05:59.4673404Z --- Parse Warning: 21 / 136 --- 2025-07-17T09:05:59.4673882Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=vmap in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py line=39. 2025-07-17T09:05:59.4674041Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4674101Z 2025-07-17T09:05:59.4674248Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2025-07-17T09:05:59.4674379Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2025-07-17T09:05:59.4674510Z pushes the map into PyTorch operations called by ``func``, effectively 2025-07-17T09:05:59.4674591Z vectorizing those operations. 2025-07-17T09:05:59.4674642Z 2025-07-17T09:05:59.4674768Z vmap is useful for handling batch dimensions: one can write a function 2025-07-17T09:05:59.4674892Z ``func`` that runs on examples and then lift it to a function that can 2025-07-17T09:05:59.4675028Z take batches of examples with ``vmap(func)``. vmap can also be used to 2025-07-17T09:05:59.4675141Z compute batched gradients when composed with autograd. 2025-07-17T09:05:59.4675196Z 2025-07-17T09:05:59.4675284Z .. note:: 2025-07-17T09:05:59.4675401Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2025-07-17T09:05:59.4675488Z convenience. Use whichever one you'd like. 2025-07-17T09:05:59.4675559Z 2025-07-17T09:05:59.4675628Z Args: 2025-07-17T09:05:59.4675761Z func (function): A Python function that takes one or more arguments. 2025-07-17T09:05:59.4675845Z Must return one or more Tensors. 2025-07-17T09:05:59.4675963Z in_dims (int or nested structure): Specifies which dimension of the 2025-07-17T09:05:59.4676094Z inputs should be mapped over. ``in_dims`` should have a 2025-07-17T09:05:59.4676215Z structure like the inputs. If the ``in_dim`` for a particular 2025-07-17T09:05:59.4676334Z input is None, then that indicates there is no map dimension. 2025-07-17T09:05:59.4676398Z Default: 0. 2025-07-17T09:05:59.4676521Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2025-07-17T09:05:59.4676634Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2025-07-17T09:05:59.4676741Z it should have one element per output. Default: 0. 2025-07-17T09:05:59.4676872Z randomness (str): Specifies whether the randomness in this 2025-07-17T09:05:59.4677011Z vmap should be the same or different across batches. If 'different', 2025-07-17T09:05:59.4677134Z the randomness for each batch will be different. If 'same', the 2025-07-17T09:05:59.4677264Z randomness will be the same across batches. If 'error', any calls to 2025-07-17T09:05:59.4677391Z random functions will error. Default: 'error'. WARNING: this flag 2025-07-17T09:05:59.4677517Z only applies to random PyTorch operations and does not apply to 2025-07-17T09:05:59.4677630Z Python's random module or numpy randomness. 2025-07-17T09:05:59.4677908Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2025-07-17T09:05:59.4678050Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2025-07-17T09:05:59.4678207Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2025-07-17T09:05:59.4678490Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2025-07-17T09:05:59.4678549Z 2025-07-17T09:05:59.4678606Z Returns: 2025-07-17T09:05:59.4678721Z Returns a new "batched" function. It takes the same inputs as 2025-07-17T09:05:59.4678838Z ``func``, except each input has an extra dimension at the index 2025-07-17T09:05:59.4678953Z specified by ``in_dims``. It takes returns the same outputs as 2025-07-17T09:05:59.4679067Z ``func``, except each output has an extra dimension at the index 2025-07-17T09:05:59.4679150Z specified by ``out_dims``. 2025-07-17T09:05:59.4679210Z 2025-07-17T09:05:59.4679267Z .. warning: 2025-07-17T09:05:59.4679388Z :func:`vmap` works best with functional-style code. Please do not 2025-07-17T09:05:59.4679539Z perform any side-effects in ``func``, with the exception of 2025-07-17T09:05:59.4679688Z in-place PyTorch operations. Examples of side-effects include mutating 2025-07-17T09:05:59.4679825Z Python data structures and assigning values to variables not captured 2025-07-17T09:05:59.4679891Z in ``func``. 2025-07-17T09:05:59.4679945Z 2025-07-17T09:05:59.4680091Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2025-07-17T09:05:59.4680245Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2025-07-17T09:05:59.4680385Z rummaging through docs, use :func:`vmap` to construct a new function. 2025-07-17T09:05:59.4680441Z 2025-07-17T09:05:59.4680515Z >>> torch.dot # [D], [D] -> [] 2025-07-17T09:05:59.4680641Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2025-07-17T09:05:59.4680723Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2025-07-17T09:05:59.4680808Z >>> batched_dot(x, y) 2025-07-17T09:05:59.4680863Z 2025-07-17T09:05:59.4681003Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2025-07-17T09:05:59.4681072Z model authoring experience. 2025-07-17T09:05:59.4681133Z 2025-07-17T09:05:59.4681211Z >>> batch_size, feature_size = 3, 5 2025-07-17T09:05:59.4681317Z >>> weights = torch.randn(feature_size, requires_grad=True) 2025-07-17T09:05:59.4681372Z >>> 2025-07-17T09:05:59.4681448Z >>> def model(feature_vec): 2025-07-17T09:05:59.4681536Z >>> # Very simple linear model with activation 2025-07-17T09:05:59.4681620Z >>> return feature_vec.dot(weights).relu() 2025-07-17T09:05:59.4681698Z >>> 2025-07-17T09:05:59.4681799Z >>> examples = torch.randn(batch_size, feature_size) 2025-07-17T09:05:59.4681899Z >>> result = torch.vmap(model)(examples) 2025-07-17T09:05:59.4681956Z 2025-07-17T09:05:59.4682105Z :func:`vmap` can also help vectorize computations that were previously difficult 2025-07-17T09:05:59.4682240Z or impossible to batch. One example is higher-order gradient computation. 2025-07-17T09:05:59.4682397Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2025-07-17T09:05:59.4682532Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2025-07-17T09:05:59.4682678Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2025-07-17T09:05:59.4682815Z we can vectorize the whole computation, computing the Jacobian in a single 2025-07-17T09:05:59.4682893Z call to ``autograd.grad``. 2025-07-17T09:05:59.4682968Z 2025-07-17T09:05:59.4683031Z >>> # Setup 2025-07-17T09:05:59.4683088Z >>> N = 5 2025-07-17T09:05:59.4683226Z >>> f = lambda x: x**2 2025-07-17T09:05:59.4683358Z >>> x = torch.randn(N, requires_grad=True) 2025-07-17T09:05:59.4683421Z >>> y = f(x) 2025-07-17T09:05:59.4683521Z >>> I_N = torch.eye(N) 2025-07-17T09:05:59.4683598Z >>> 2025-07-17T09:05:59.4683670Z >>> # Sequential approach 2025-07-17T09:05:59.4683806Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2025-07-17T09:05:59.4683984Z >>> for v in I_N.unbind()] 2025-07-17T09:05:59.4684068Z >>> jacobian = torch.stack(jacobian_rows) 2025-07-17T09:05:59.4684132Z >>> 2025-07-17T09:05:59.4684225Z >>> # vectorized gradient computation 2025-07-17T09:05:59.4684297Z >>> def get_vjp(v): 2025-07-17T09:05:59.4684372Z >>> return torch.autograd.grad(y, x, v) 2025-07-17T09:05:59.4684463Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2025-07-17T09:05:59.4684515Z 2025-07-17T09:05:59.4684691Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2025-07-17T09:05:59.4684748Z 2025-07-17T09:05:59.4684852Z >>> torch.dot # [D], [D] -> [] 2025-07-17T09:05:59.4684922Z >>> batched_dot = torch.vmap( 2025-07-17T09:05:59.4685001Z ... torch.vmap(torch.dot) 2025-07-17T09:05:59.4685078Z ... ) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2025-07-17T09:05:59.4685179Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2025-07-17T09:05:59.4685261Z >>> batched_dot(x, y) # tensor of size [2, 3] 2025-07-17T09:05:59.4685322Z 2025-07-17T09:05:59.4685490Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2025-07-17T09:05:59.4685602Z the dimension that each inputs are batched along as 2025-07-17T09:05:59.4685658Z 2025-07-17T09:05:59.4685725Z >>> torch.dot # [N], [N] -> [] 2025-07-17T09:05:59.4685900Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2025-07-17T09:05:59.4685991Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2025-07-17T09:05:59.4686067Z >>> batched_dot( 2025-07-17T09:05:59.4686124Z ... x, y 2025-07-17T09:05:59.4686247Z ... ) # output is [5] instead of [2] if batched along the 0th dimension 2025-07-17T09:05:59.4686303Z 2025-07-17T09:05:59.4686461Z If there are multiple inputs each of which is batched along different dimensions, 2025-07-17T09:05:59.4686586Z ``in_dims`` must be a tuple with the batch dimension for each input as 2025-07-17T09:05:59.4686677Z 2025-07-17T09:05:59.4686743Z >>> torch.dot # [D], [D] -> [] 2025-07-17T09:05:59.4686924Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2025-07-17T09:05:59.4687008Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2025-07-17T09:05:59.4687077Z >>> batched_dot( 2025-07-17T09:05:59.4687133Z ... x, y 2025-07-17T09:05:59.4687253Z ... ) # second arg doesn't have a batch dim because in_dim[1] was None 2025-07-17T09:05:59.4687309Z 2025-07-17T09:05:59.4687452Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2025-07-17T09:05:59.4687528Z matching the shape of the input: 2025-07-17T09:05:59.4687590Z 2025-07-17T09:05:59.4687687Z >>> f = lambda dict: torch.dot(dict["x"], dict["y"]) 2025-07-17T09:05:59.4687760Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2025-07-17T09:05:59.4687833Z >>> input = {"x": x, "y": y} 2025-07-17T09:05:59.4687942Z >>> batched_dot = torch.vmap(f, in_dims=({"x": 0, "y": None},)) 2025-07-17T09:05:59.4688011Z >>> batched_dot(input) 2025-07-17T09:05:59.4688065Z 2025-07-17T09:05:59.4688250Z By default, the output is batched along the first dimension. However, it can be batched 2025-07-17T09:05:59.4688330Z along any dimension by using ``out_dims`` 2025-07-17T09:05:59.4688390Z 2025-07-17T09:05:59.4688453Z >>> f = lambda x: x**2 2025-07-17T09:05:59.4688588Z >>> x = torch.randn(2, 5) 2025-07-17T09:05:59.4688753Z >>> batched_pow = torch.vmap(f, out_dims=1) 2025-07-17T09:05:59.4688829Z >>> batched_pow(x) # [5, 2] 2025-07-17T09:05:59.4688895Z 2025-07-17T09:05:59.4689067Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2025-07-17T09:05:59.4689150Z accept kwargs 2025-07-17T09:05:59.4689202Z 2025-07-17T09:05:59.4689381Z >>> x = torch.randn([2, 5]) 2025-07-17T09:05:59.4689452Z >>> def fn(x, scale=4.): 2025-07-17T09:05:59.4689523Z >>> return x * scale 2025-07-17T09:05:59.4689579Z >>> 2025-07-17T09:05:59.4689682Z >>> batched_pow = torch.vmap(fn) 2025-07-17T09:05:59.4689774Z >>> assert torch.allclose(batched_pow(x), x * 4) 2025-07-17T09:05:59.4689916Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2025-07-17T09:05:59.4689985Z 2025-07-17T09:05:59.4690053Z .. note:: 2025-07-17T09:05:59.4690188Z vmap does not provide general autobatching or handle variable-length 2025-07-17T09:05:59.4690258Z sequences out of the box. 2025-07-17T09:05:59.4690312Z 2025-07-17T09:05:59.4690461Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4690513Z 2025-07-17T09:05:59.4690599Z warnings.warn(msg) 2025-07-17T09:05:59.4690661Z 2025-07-17T09:05:59.4690798Z --- Parse Warning: 22 / 136 --- 2025-07-17T09:05:59.4691267Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=grad in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py line=306. 2025-07-17T09:05:59.4691451Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4691598Z ``grad`` operator helps computing gradients of ``func`` with respect to the 2025-07-17T09:05:59.4691715Z input(s) specified by ``argnums``. This operator can be nested to 2025-07-17T09:05:59.4691820Z compute higher-order gradients. 2025-07-17T09:05:59.4691871Z 2025-07-17T09:05:59.4691935Z Args: 2025-07-17T09:05:59.4692063Z func (Callable): A Python function that takes one or more arguments. 2025-07-17T09:05:59.4692235Z Must return a single-element Tensor. If specified ``has_aux`` equals ``True``, 2025-07-17T09:05:59.4692396Z function can return a tuple of single-element Tensor and other auxiliary objects: 2025-07-17T09:05:59.4692472Z ``(output, aux)``. 2025-07-17T09:05:59.4692634Z argnums (int or Tuple[int]): Specifies arguments to compute gradients with respect to. 2025-07-17T09:05:59.4692769Z ``argnums`` can be single integer or tuple of integers. Default: 0. 2025-07-17T09:05:59.4692899Z has_aux (bool): Flag indicating that ``func`` returns a tensor and other 2025-07-17T09:05:59.4693010Z auxiliary objects: ``(output, aux)``. Default: False. 2025-07-17T09:05:59.4693066Z 2025-07-17T09:05:59.4693123Z Returns: 2025-07-17T09:05:59.4693289Z Function to compute gradients with respect to its inputs. By default, the output of 2025-07-17T09:05:59.4693439Z the function is the gradient tensor(s) with respect to the first argument. 2025-07-17T09:05:59.4693605Z If specified ``has_aux`` equals ``True``, tuple of gradients and output auxiliary objects 2025-07-17T09:05:59.4693762Z is returned. If ``argnums`` is a tuple of integers, a tuple of output gradients with 2025-07-17T09:05:59.4693862Z respect to each ``argnums`` value is returned. 2025-07-17T09:05:59.4693924Z 2025-07-17T09:05:59.4693995Z Example of using ``grad``: 2025-07-17T09:05:59.4694053Z 2025-07-17T09:05:59.4694122Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.4694197Z >>> from torch.func import grad 2025-07-17T09:05:59.4694272Z >>> x = torch.randn([]) 2025-07-17T09:05:59.4694424Z >>> cos_x = grad(lambda x: torch.sin(x))(x) 2025-07-17T09:05:59.4694568Z >>> assert torch.allclose(cos_x, x.cos()) 2025-07-17T09:05:59.4694627Z >>> 2025-07-17T09:05:59.4694705Z >>> # Second-order gradients 2025-07-17T09:05:59.4694814Z >>> neg_sin_x = grad(grad(lambda x: torch.sin(x)))(x) 2025-07-17T09:05:59.4695010Z >>> assert torch.allclose(neg_sin_x, -x.sin()) 2025-07-17T09:05:59.4695067Z 2025-07-17T09:05:59.4695224Z When composed with ``vmap``, ``grad`` can be used to compute per-sample-gradients: 2025-07-17T09:05:59.4695280Z 2025-07-17T09:05:59.4695352Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.4695433Z >>> from torch.func import grad, vmap 2025-07-17T09:05:59.4695511Z >>> batch_size, feature_size = 3, 5 2025-07-17T09:05:59.4695570Z >>> 2025-07-17T09:05:59.4695648Z >>> def model(weights, feature_vec): 2025-07-17T09:05:59.4695744Z >>> # Very simple linear model with activation 2025-07-17T09:05:59.4695816Z >>> assert feature_vec.dim() == 1 2025-07-17T09:05:59.4695899Z >>> return feature_vec.dot(weights).relu() 2025-07-17T09:05:59.4695952Z >>> 2025-07-17T09:05:59.4696046Z >>> def compute_loss(weights, example, target): 2025-07-17T09:05:59.4696122Z >>> y = model(weights, example) 2025-07-17T09:05:59.4696222Z >>> return ((y - target) ** 2).mean() # MSELoss 2025-07-17T09:05:59.4696301Z >>> 2025-07-17T09:05:59.4696421Z >>> weights = torch.randn(feature_size, requires_grad=True) 2025-07-17T09:05:59.4696518Z >>> examples = torch.randn(batch_size, feature_size) 2025-07-17T09:05:59.4696600Z >>> targets = torch.randn(batch_size) 2025-07-17T09:05:59.4696680Z >>> inputs = (weights, examples, targets) 2025-07-17T09:05:59.4696820Z >>> grad_weight_per_example = vmap(grad(compute_loss), in_dims=(None, 0, 0))( 2025-07-17T09:05:59.4696887Z ... *inputs 2025-07-17T09:05:59.4696949Z ... ) 2025-07-17T09:05:59.4697005Z 2025-07-17T09:05:59.4697122Z Example of using ``grad`` with ``has_aux`` and ``argnums``: 2025-07-17T09:05:59.4697179Z 2025-07-17T09:05:59.4697253Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.4697346Z >>> from torch.func import grad 2025-07-17T09:05:59.4697442Z >>> def my_loss_func(y, y_pred): 2025-07-17T09:05:59.4697544Z >>> loss_per_sample = (0.5 * y_pred - y) ** 2 2025-07-17T09:05:59.4697624Z >>> loss = loss_per_sample.mean() 2025-07-17T09:05:59.4697712Z >>> return loss, (y_pred, loss_per_sample) 2025-07-17T09:05:59.4697768Z >>> 2025-07-17T09:05:59.4697880Z >>> fn = grad(my_loss_func, argnums=(0, 1), has_aux=True) 2025-07-17T09:05:59.4697974Z >>> y_true = torch.rand(4) 2025-07-17T09:05:59.4698070Z >>> y_preds = torch.rand(4, requires_grad=True) 2025-07-17T09:05:59.4698151Z >>> out = fn(y_true, y_preds) 2025-07-17T09:05:59.4698315Z >>> # > output is ((grads w.r.t y_true, grads w.r.t y_preds), (y_pred, loss_per_sample)) 2025-07-17T09:05:59.4698370Z 2025-07-17T09:05:59.4698471Z .. note:: 2025-07-17T09:05:59.4698577Z Using PyTorch ``torch.no_grad`` together with ``grad``. 2025-07-17T09:05:59.4698648Z 2025-07-17T09:05:59.4698774Z Case 1: Using ``torch.no_grad`` inside a function: 2025-07-17T09:05:59.4698832Z 2025-07-17T09:05:59.4698921Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.4698984Z >>> def f(x): 2025-07-17T09:05:59.4699078Z >>> with torch.no_grad(): 2025-07-17T09:05:59.4699142Z >>> c = x ** 2 2025-07-17T09:05:59.4699211Z >>> return x - c 2025-07-17T09:05:59.4699265Z 2025-07-17T09:05:59.4699434Z In this case, ``grad(f)(x)`` will respect the inner ``torch.no_grad``. 2025-07-17T09:05:59.4699670Z 2025-07-17T09:05:59.4699799Z Case 2: Using ``grad`` inside ``torch.no_grad`` context manager: 2025-07-17T09:05:59.4699853Z 2025-07-17T09:05:59.4699961Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.4700064Z >>> with torch.no_grad(): 2025-07-17T09:05:59.4700132Z >>> grad(f)(x) 2025-07-17T09:05:59.4700203Z 2025-07-17T09:05:59.4700446Z In this case, ``grad`` will respect the inner ``torch.no_grad``, but not the 2025-07-17T09:05:59.4700582Z outer one. This is because ``grad`` is a "function transform": its result 2025-07-17T09:05:59.4700732Z should not depend on the result of a context manager outside of ``f``. 2025-07-17T09:05:59.4700817Z 2025-07-17T09:05:59.4700873Z 2025-07-17T09:05:59.4701030Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4701087Z 2025-07-17T09:05:59.4701155Z warnings.warn(msg) 2025-07-17T09:05:59.4701212Z 2025-07-17T09:05:59.4701352Z --- Parse Warning: 23 / 136 --- 2025-07-17T09:05:59.4701880Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CustomOpDef.register_fake in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py line=396. 2025-07-17T09:05:59.4702037Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-07-17T09:05:59.4702166Z Register a FakeTensor implementation for this custom op. 2025-07-17T09:05:59.4702253Z 2025-07-17T09:05:59.4702400Z This is necessary to get the operator to work efficiently with torch.compile. 2025-07-17T09:05:59.4702464Z 2025-07-17T09:05:59.4702592Z The Fake impl (sometimes also known as a meta kernel or abstract impl) 2025-07-17T09:05:59.4702728Z specifies the behavior of this operator on Tensors that carry no data. 2025-07-17T09:05:59.4702833Z Given some input Tensors with certain properties 2025-07-17T09:05:59.4703000Z (sizes/strides/storage_offset/device), it specifies what the properties of 2025-07-17T09:05:59.4703080Z the output Tensors are. 2025-07-17T09:05:59.4703153Z 2025-07-17T09:05:59.4703282Z Please see :func:`torch.library.impl_abstract` for more details. 2025-07-17T09:05:59.4703345Z 2025-07-17T09:05:59.4703433Z Args: 2025-07-17T09:05:59.4703544Z fn (Callable): The function to register as the FakeTensor 2025-07-17T09:05:59.4703618Z implementation. 2025-07-17T09:05:59.4703682Z 2025-07-17T09:05:59.4703750Z Examples: 2025-07-17T09:05:59.4703816Z >>> import torch 2025-07-17T09:05:59.4703927Z >>> import numpy as np 2025-07-17T09:05:59.4704022Z >>> from torch import Tensor 2025-07-17T09:05:59.4704118Z >>> 2025-07-17T09:05:59.4704236Z >>> # Example 1: an operator without data-dependent output shape 2025-07-17T09:05:59.4704370Z >>> @torch.library.custom_op("mylib::linear", mutates_args=()) 2025-07-17T09:05:59.4704484Z >>> def linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2025-07-17T09:05:59.4704587Z >>> return (x @ weight.t()) + bias 2025-07-17T09:05:59.4704650Z >>> 2025-07-17T09:05:59.4704754Z >>> @linear.register_fake 2025-07-17T09:05:59.4704855Z >>> def _(x, weight, bias): 2025-07-17T09:05:59.4704931Z >>> assert x.dim() == 2 2025-07-17T09:05:59.4705005Z >>> assert weight.dim() == 2 2025-07-17T09:05:59.4705110Z >>> assert bias.dim() == 1 2025-07-17T09:05:59.4705193Z >>> assert x.shape[1] == weight.shape[1] 2025-07-17T09:05:59.4705343Z >>> assert weight.shape[0] == bias.shape[0] 2025-07-17T09:05:59.4705516Z >>> assert x.device == weight.device 2025-07-17T09:05:59.4705681Z >>> return x.new_empty(x.size(0), weight.size(0)) 2025-07-17T09:05:59.4705753Z >>> 2025-07-17T09:05:59.4705823Z >>> x = torch.randn(2, 2) 2025-07-17T09:05:59.4705916Z >>> weight = torch.randn(2, 2) 2025-07-17T09:05:59.4705989Z >>> bias = torch.randn(2) 2025-07-17T09:05:59.4706210Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-07-17T09:05:59.4706332Z >>> out = torch.compile(linear, fullgraph=True)(x, weight, bias) 2025-07-17T09:05:59.4706433Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-07-17T09:05:59.4706586Z >>> assert torch.allclose(out, torch.nn.functional.linear(x, weight, bias)) 2025-07-17T09:05:59.4706649Z >>> 2025-07-17T09:05:59.4706775Z >>> # Example 2: an operator with data-dependent output shape 2025-07-17T09:05:59.4706904Z >>> @torch.library.custom_op("mylib::nonzero", mutates_args=()) 2025-07-17T09:05:59.4706985Z >>> def nonzero(x: Tensor) -> Tensor: 2025-07-17T09:05:59.4707075Z >>> x_np = x.cpu().numpy() 2025-07-17T09:05:59.4707176Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2025-07-17T09:05:59.4707275Z >>> return torch.tensor(res, device=x.device) 2025-07-17T09:05:59.4707340Z >>> 2025-07-17T09:05:59.4707438Z >>> @nonzero.register_fake 2025-07-17T09:05:59.4707508Z >>> def _(x): 2025-07-17T09:05:59.4707616Z >>> # Number of nonzero-elements is data-dependent. 2025-07-17T09:05:59.4707723Z >>> # Since we cannot peek at the data in an abstract impl, 2025-07-17T09:05:59.4707825Z >>> # we use the ctx object to construct a new symint that 2025-07-17T09:05:59.4707925Z >>> # represents the data-dependent size. 2025-07-17T09:05:59.4708021Z >>> ctx = torch.library.get_ctx() 2025-07-17T09:05:59.4708111Z >>> nnz = ctx.new_dynamic_size() 2025-07-17T09:05:59.4708195Z >>> shape = [nnz, x.dim()] 2025-07-17T09:05:59.4708328Z >>> result = x.new_empty(shape, dtype=torch.int64) 2025-07-17T09:05:59.4708402Z >>> return result 2025-07-17T09:05:59.4708461Z >>> 2025-07-17T09:05:59.4708548Z >>> x = torch.tensor([0, 1, 2, 0, 0, 1]) 2025-07-17T09:05:59.4708644Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-07-17T09:05:59.4708739Z >>> out = torch.compile(nonzero, fullgraph=True)(x) 2025-07-17T09:05:59.4708850Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-07-17T09:05:59.4708940Z >>> assert torch.allclose(out, x.nonzero()) 2025-07-17T09:05:59.4709005Z 2025-07-17T09:05:59.4709066Z 2025-07-17T09:05:59.4709382Z Original Error: IndentationError('expected an indented block after function definition on line 36', ('', 37, 1, '_._ = None\n', 37, 2)) 2025-07-17T09:05:59.4709453Z 2025-07-17T09:05:59.4709513Z _._ = None 2025-07-17T09:05:59.4709569Z ^ 2025-07-17T09:05:59.4709643Z warnings.warn(msg) 2025-07-17T09:05:59.4709704Z 2025-07-17T09:05:59.4709846Z --- Parse Warning: 24 / 136 --- 2025-07-17T09:05:59.4710331Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=triton_op in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/triton.py line=21. 2025-07-17T09:05:59.4710489Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4710655Z Create a custom operator whose implementation is backed by 1+ triton kernels. 2025-07-17T09:05:59.4710711Z 2025-07-17T09:05:59.4710841Z This is a more structured way of using triton kernels with PyTorch. 2025-07-17T09:05:59.4711089Z Prefer using triton kernels with no ``torch.library`` custom operator wrappers 2025-07-17T09:05:59.4711299Z (like :func:`torch.library.custom_op`, :func:`torch.library.triton_op`) because 2025-07-17T09:05:59.4711364Z that is simpler; 2025-07-17T09:05:59.4711520Z only use :func:`torch.library.custom_op`/:func:`torch.library.triton_op` if you 2025-07-17T09:05:59.4711762Z want to create an operator that behaves like PyTorch built-in operators. 2025-07-17T09:05:59.4711911Z For example, you may use a ``torch.library`` wrapper API to define the 2025-07-17T09:05:59.4712045Z behavior of the triton kernel when passed a tensor subclass or under 2025-07-17T09:05:59.4712131Z a TorchDispatchMode. 2025-07-17T09:05:59.4712188Z 2025-07-17T09:05:59.4712339Z Use :func:`torch.library.triton_op` instead of :func:`torch.library.custom_op` 2025-07-17T09:05:59.4712410Z when the implementation 2025-07-17T09:05:59.4712542Z consists of 1+ triton kernels. :func:`torch.library.custom_op` treats 2025-07-17T09:05:59.4712650Z custom operators as opaque (:func:`torch.compile` and 2025-07-17T09:05:59.4712795Z :func:`torch.export.export` will never trace into them), but ``triton_op`` 2025-07-17T09:05:59.4712929Z makes the implementation visible to these subsystems, allowing them 2025-07-17T09:05:59.4713014Z to optimize the triton kernel(s). 2025-07-17T09:05:59.4713081Z 2025-07-17T09:05:59.4713218Z Note that ``fn`` must only consist of calls to PyTorch-understood 2025-07-17T09:05:59.4713352Z operators and triton kernels. Any triton kernels called inside ``fn`` 2025-07-17T09:05:59.4713484Z must be wrapped in a call to :func:`torch.library.wrap_triton`. 2025-07-17T09:05:59.4713545Z 2025-07-17T09:05:59.4713602Z Args: 2025-07-17T09:05:59.4713744Z name (str): A name for the custom op that looks like "{namespace}::{name}", 2025-07-17T09:05:59.4713875Z e.g. "mylib::my_linear". The name is used as the op's stable identifier 2025-07-17T09:05:59.4713989Z in PyTorch subsystems (e.g. torch.export, FX graphs). 2025-07-17T09:05:59.4714138Z To avoid name collisions, please use your project name as the namespace; 2025-07-17T09:05:59.4714273Z e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. 2025-07-17T09:05:59.4714438Z mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. 2025-07-17T09:05:59.4714603Z This MUST be accurate, otherwise, the behavior is undefined. If "unknown", 2025-07-17T09:05:59.4714788Z it pessimistically assumes that all inputs to the operator are being mutated. 2025-07-17T09:05:59.4714913Z schema (None | str): A schema string for the operator. If None 2025-07-17T09:05:59.4715060Z (recommended) we'll infer a schema for the operator from its type 2025-07-17T09:05:59.4715191Z annotations. We recommend letting us infer a schema unless you 2025-07-17T09:05:59.4715271Z have a specific reason not to. 2025-07-17T09:05:59.4715383Z Example: "(Tensor x, int y) -> (Tensor, Tensor)". 2025-07-17T09:05:59.4715450Z 2025-07-17T09:05:59.4715515Z Example:: 2025-07-17T09:05:59.4715571Z 2025-07-17T09:05:59.4715689Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-07-17T09:05:59.4715752Z >>> import torch 2025-07-17T09:05:59.4715868Z >>> from torch.library import triton_op, wrap_triton 2025-07-17T09:05:59.4715928Z >>> 2025-07-17T09:05:59.4716001Z >>> import triton 2025-07-17T09:05:59.4716086Z >>> from triton import language as tl 2025-07-17T09:05:59.4716160Z >>> 2025-07-17T09:05:59.4716225Z >>> @triton.jit 2025-07-17T09:05:59.4716298Z >>> def add_kernel( 2025-07-17T09:05:59.4716371Z >>> in_ptr0, 2025-07-17T09:05:59.4716496Z >>> in_ptr1, 2025-07-17T09:05:59.4716630Z >>> out_ptr, 2025-07-17T09:05:59.4716692Z >>> n_elements, 2025-07-17T09:05:59.4716782Z >>> BLOCK_SIZE: "tl.constexpr", 2025-07-17T09:05:59.4716848Z >>> ): 2025-07-17T09:05:59.4716962Z >>> pid = tl.program_id(axis=0) 2025-07-17T09:05:59.4717177Z >>> block_start = pid * BLOCK_SIZE 2025-07-17T09:05:59.4717290Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2025-07-17T09:05:59.4717365Z >>> mask = offsets < n_elements 2025-07-17T09:05:59.4717474Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2025-07-17T09:05:59.4717558Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2025-07-17T09:05:59.4717652Z >>> output = x + y 2025-07-17T09:05:59.4717745Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2025-07-17T09:05:59.4717822Z >>> 2025-07-17T09:05:59.4717915Z >>> @triton_op("mylib::add", mutates_args={}) 2025-07-17T09:05:59.4718041Z >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: 2025-07-17T09:05:59.4718118Z >>> output = torch.empty_like(x) 2025-07-17T09:05:59.4718190Z >>> n_elements = output.numel() 2025-07-17T09:05:59.4718253Z >>> 2025-07-17T09:05:59.4718323Z >>> def grid(meta): 2025-07-17T09:05:59.4718436Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2025-07-17T09:05:59.4718493Z >>> 2025-07-17T09:05:59.4718612Z >>> # NB: we need to wrap the triton kernel in a call to wrap_triton 2025-07-17T09:05:59.4718731Z >>> wrap_triton(add_kernel)[grid](x, y, output, n_elements, 16) 2025-07-17T09:05:59.4718808Z >>> return output 2025-07-17T09:05:59.4718866Z >>> 2025-07-17T09:05:59.4718974Z >>> @torch.compile 2025-07-17T09:05:59.4719085Z >>> def f(x, y): 2025-07-17T09:05:59.4719181Z >>> return add(x, y) 2025-07-17T09:05:59.4719241Z >>> 2025-07-17T09:05:59.4719330Z >>> x = torch.randn(3, device="cuda") 2025-07-17T09:05:59.4719402Z >>> y = torch.randn(3, device="cuda") 2025-07-17T09:05:59.4719475Z >>> 2025-07-17T09:05:59.4719536Z >>> z = f(x, y) 2025-07-17T09:05:59.4719617Z >>> assert torch.allclose(z, x + y) 2025-07-17T09:05:59.4719681Z 2025-07-17T09:05:59.4719738Z 2025-07-17T09:05:59.4719897Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4719950Z 2025-07-17T09:05:59.4720024Z warnings.warn(msg) 2025-07-17T09:05:59.4720107Z 2025-07-17T09:05:59.4720266Z --- Parse Warning: 25 / 136 --- 2025-07-17T09:05:59.4720748Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=wrap_triton in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/triton.py line=202. 2025-07-17T09:05:59.4720920Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4721058Z Allows capture of a triton kernel into a graph via make_fx or 2025-07-17T09:05:59.4721150Z non-strict ``torch.export``. 2025-07-17T09:05:59.4721211Z 2025-07-17T09:05:59.4721351Z These technologies perform Dispatcher-based tracing (via 2025-07-17T09:05:59.4721473Z ``__torch_dispatch__``) and cannot see calls to raw triton kernels. 2025-07-17T09:05:59.4721602Z The ``wrap_triton`` API wraps a triton kernel into a callable that 2025-07-17T09:05:59.4721684Z can actually be traced into a graph. 2025-07-17T09:05:59.4721757Z 2025-07-17T09:05:59.4721881Z Please use this API together with :func:`torch.library.triton_op`. 2025-07-17T09:05:59.4721936Z 2025-07-17T09:05:59.4722029Z Examples: 2025-07-17T09:05:59.4722151Z 2025-07-17T09:05:59.4722249Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.4722376Z >>> import torch 2025-07-17T09:05:59.4722448Z >>> import triton 2025-07-17T09:05:59.4722537Z >>> from triton import language as tl 2025-07-17T09:05:59.4722654Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2025-07-17T09:05:59.4722742Z >>> from torch.library import wrap_triton 2025-07-17T09:05:59.4722919Z >>> 2025-07-17T09:05:59.4722984Z >>> @triton.jit 2025-07-17T09:05:59.4723078Z >>> def add_kernel( 2025-07-17T09:05:59.4723149Z >>> in_ptr0, 2025-07-17T09:05:59.4723214Z >>> in_ptr1, 2025-07-17T09:05:59.4723276Z >>> out_ptr, 2025-07-17T09:05:59.4723345Z >>> n_elements, 2025-07-17T09:05:59.4723428Z >>> BLOCK_SIZE: "tl.constexpr", 2025-07-17T09:05:59.4723499Z >>> ): 2025-07-17T09:05:59.4723578Z >>> pid = tl.program_id(axis=0) 2025-07-17T09:05:59.4723654Z >>> block_start = pid * BLOCK_SIZE 2025-07-17T09:05:59.4723780Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2025-07-17T09:05:59.4723864Z >>> mask = offsets < n_elements 2025-07-17T09:05:59.4723955Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2025-07-17T09:05:59.4724040Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2025-07-17T09:05:59.4724130Z >>> output = x + y 2025-07-17T09:05:59.4724224Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2025-07-17T09:05:59.4724304Z >>> 2025-07-17T09:05:59.4724366Z >>> def add(x, y): 2025-07-17T09:05:59.4724453Z >>> output = torch.empty_like(x) 2025-07-17T09:05:59.4724550Z >>> n_elements = output.numel() 2025-07-17T09:05:59.4724612Z >>> 2025-07-17T09:05:59.4724704Z >>> def grid_fn(meta): 2025-07-17T09:05:59.4724813Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2025-07-17T09:05:59.4724899Z >>> 2025-07-17T09:05:59.4725028Z >>> wrap_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16) 2025-07-17T09:05:59.4725104Z >>> return output 2025-07-17T09:05:59.4725156Z >>> 2025-07-17T09:05:59.4725266Z >>> x = torch.randn(3, device="cuda") 2025-07-17T09:05:59.4725371Z >>> y = torch.randn(3, device="cuda") 2025-07-17T09:05:59.4725444Z >>> gm = make_fx(add)(x, y) 2025-07-17T09:05:59.4725513Z >>> print(gm.code) 2025-07-17T09:05:59.4725604Z >>> # def forward(self, x_1, y_1): 2025-07-17T09:05:59.4725754Z >>> # empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False) 2025-07-17T09:05:59.4725905Z >>> # triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation( 2025-07-17T09:05:59.4725993Z >>> # kernel_idx = 0, constant_args_idx = 0, 2025-07-17T09:05:59.4726086Z >>> # grid = [(1, 1, 1)], kwargs = { 2025-07-17T09:05:59.4726188Z >>> # 'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like, 2025-07-17T09:05:59.4726281Z >>> # 'n_elements': 3, 'BLOCK_SIZE': 16 2025-07-17T09:05:59.4726342Z >>> # }) 2025-07-17T09:05:59.4726415Z >>> # return empty_like 2025-07-17T09:05:59.4726473Z 2025-07-17T09:05:59.4726533Z 2025-07-17T09:05:59.4726685Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4726745Z 2025-07-17T09:05:59.4726826Z warnings.warn(msg) 2025-07-17T09:05:59.4726896Z 2025-07-17T09:05:59.4727044Z --- Parse Warning: 26 / 136 --- 2025-07-17T09:05:59.4727571Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=unsafe_generate_fake_kernels in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_profile.py line=94. 2025-07-17T09:05:59.4727856Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4727911Z 2025-07-17T09:05:59.4728092Z Registers a fake kernel based on the given operator profiles. This fake 2025-07-17T09:05:59.4728240Z kernel registration will override any existing fake kernel registrations. 2025-07-17T09:05:59.4728403Z 2025-07-17T09:05:59.4728537Z The input is a dictionary mapping operator names to a set of operator 2025-07-17T09:05:59.4728683Z profiles, which we will use to generate fake kernels. The operator profiles 2025-07-17T09:05:59.4728816Z are a record of the input and output tensor metadata. Based on this 2025-07-17T09:05:59.4734305Z information we will match a given input to the recorded profile, and return 2025-07-17T09:05:59.4734481Z an output with the same metadata as in the recorded profile. If a profile 2025-07-17T09:05:59.4734594Z doesn't exist then an exception will be thrown. 2025-07-17T09:05:59.4734652Z 2025-07-17T09:05:59.4734799Z The fake kernel generation is considered unsafe because it relies on the 2025-07-17T09:05:59.4734938Z rigid, pre-defined operator profiles that do not account for potential 2025-07-17T09:05:59.4735088Z variations in output behavior. Specifically, the generated kernels assume a 2025-07-17T09:05:59.4735247Z fixed relationship between input and output ranks. However, in reality, it's 2025-07-17T09:05:59.4735394Z possible that data-dependent operations may produce outputs of different 2025-07-17T09:05:59.4735522Z ranks even when given inputs of the same rank. The generated fake kernels 2025-07-17T09:05:59.4735655Z are inflexible and unable to accommodate these nuances, making them 2025-07-17T09:05:59.4735727Z potentially unsafe. 2025-07-17T09:05:59.4735782Z 2025-07-17T09:05:59.4735849Z Args: 2025-07-17T09:05:59.4735979Z op_profiles (dict[str, set[OpProfile]]): A dictionary mapping operator 2025-07-17T09:05:59.4736107Z name to a set of operator profiles from which we will generate fake 2025-07-17T09:05:59.4736164Z kernels. 2025-07-17T09:05:59.4736225Z 2025-07-17T09:05:59.4736282Z Examples: 2025-07-17T09:05:59.4736342Z 2025-07-17T09:05:59.4736452Z >>> # Example: Registering an op-profile from draft-export 2025-07-17T09:05:59.4736533Z >>> import torch 2025-07-17T09:05:59.4736640Z >>> from torch.export._draft_export import draft_export 2025-07-17T09:05:59.4736706Z >>> 2025-07-17T09:05:59.4736821Z >>> @torch.library.custom_op("mylib::foo", mutates_args=()) 2025-07-17T09:05:59.4736916Z >>> def foo(x: Tensor, y: Tensor) -> Tensor: 2025-07-17T09:05:59.4736985Z >>> return x + y 2025-07-17T09:05:59.4737045Z >>> 2025-07-17T09:05:59.4737116Z >>> class M(torch.nn.Module): 2025-07-17T09:05:59.4737185Z >>> def forward(self, a, b): 2025-07-17T09:05:59.4737283Z >>> res = torch.ops.mylib.foo(a, b) # no fake impl 2025-07-17T09:05:59.4737350Z >>> return res 2025-07-17T09:05:59.4737413Z >>> 2025-07-17T09:05:59.4737521Z >>> ep = draft_export(M(), (torch.ones(3, 4), torch.ones(3, 4)) 2025-07-17T09:05:59.4737578Z >>> 2025-07-17T09:05:59.4737757Z >>> with torch._library.fake_profile.unsafe_generate_fake_kernels(ep._report.op_profiles): 2025-07-17T09:05:59.4737848Z >>> decomp = ep.run_decompositions() 2025-07-17T09:05:59.4737900Z 2025-07-17T09:05:59.4737960Z 2025-07-17T09:05:59.4738109Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4738162Z 2025-07-17T09:05:59.4738224Z warnings.warn(msg) 2025-07-17T09:05:59.4738280Z 2025-07-17T09:05:59.4738429Z --- Parse Warning: 27 / 136 --- 2025-07-17T09:05:59.4738932Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_close in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_comparison.py line=1331. 2025-07-17T09:05:59.4739254Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4739355Z Asserts that ``actual`` and ``expected`` are close. 2025-07-17T09:05:59.4739408Z 2025-07-17T09:05:59.4739723Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2025-07-17T09:05:59.4739783Z 2025-07-17T09:05:59.4739844Z .. math:: 2025-07-17T09:05:59.4739903Z 2025-07-17T09:05:59.4740117Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2025-07-17T09:05:59.4740177Z 2025-07-17T09:05:59.4740383Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2025-07-17T09:05:59.4740521Z only considered equal to each other if ``equal_nan`` is ``True``. 2025-07-17T09:05:59.4740576Z 2025-07-17T09:05:59.4740700Z In addition, they are only considered close if they have the same 2025-07-17T09:05:59.4740755Z 2025-07-17T09:05:59.4740878Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2025-07-17T09:05:59.4740964Z - ``dtype`` (if ``check_dtype`` is ``True``), 2025-07-17T09:05:59.4741066Z - ``layout`` (if ``check_layout`` is ``True``), and 2025-07-17T09:05:59.4741146Z - stride (if ``check_stride`` is ``True``). 2025-07-17T09:05:59.4741199Z 2025-07-17T09:05:59.4741374Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2025-07-17T09:05:59.4741430Z 2025-07-17T09:05:59.4741635Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2025-07-17T09:05:59.4741853Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2025-07-17T09:05:59.4741999Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2025-07-17T09:05:59.4742225Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2025-07-17T09:05:59.4742281Z 2025-07-17T09:05:59.4742452Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2025-07-17T09:05:59.4742653Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2025-07-17T09:05:59.4742723Z definition above. 2025-07-17T09:05:59.4742779Z 2025-07-17T09:05:59.4742960Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2025-07-17T09:05:59.4743182Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2025-07-17T09:05:59.4743388Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2025-07-17T09:05:59.4743606Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2025-07-17T09:05:59.4743743Z their elements are considered close according to the above definition. 2025-07-17T09:05:59.4743805Z 2025-07-17T09:05:59.4743865Z .. note:: 2025-07-17T09:05:59.4743923Z 2025-07-17T09:05:59.4744114Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2025-07-17T09:05:59.4744309Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2025-07-17T09:05:59.4744475Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2025-07-17T09:05:59.4744532Z 2025-07-17T09:05:59.4744650Z Args: 2025-07-17T09:05:59.4744723Z actual (Any): Actual input. 2025-07-17T09:05:59.4744848Z expected (Any): Expected input. 2025-07-17T09:05:59.4745066Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2025-07-17T09:05:59.4745167Z are allowed. Otherwise type equality is required. 2025-07-17T09:05:59.4745587Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2025-07-17T09:05:59.4745745Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2025-07-17T09:05:59.4745949Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2025-07-17T09:05:59.4746092Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2025-07-17T09:05:59.4746247Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2025-07-17T09:05:59.4746420Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2025-07-17T09:05:59.4746578Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2025-07-17T09:05:59.4746724Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2025-07-17T09:05:59.4746920Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2025-07-17T09:05:59.4747113Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2025-07-17T09:05:59.4747224Z :func:`torch.promote_types`) before being compared. 2025-07-17T09:05:59.4747424Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2025-07-17T09:05:59.4747622Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2025-07-17T09:05:59.4747692Z compared. 2025-07-17T09:05:59.4747903Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2025-07-17T09:05:59.4748101Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2025-07-17T09:05:59.4748310Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2025-07-17T09:05:59.4748390Z should return the new message. 2025-07-17T09:05:59.4748453Z 2025-07-17T09:05:59.4748514Z Raises: 2025-07-17T09:05:59.4748665Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2025-07-17T09:05:59.4748773Z ValueError: If only ``rtol`` or ``atol`` is specified. 2025-07-17T09:05:59.4748980Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2025-07-17T09:05:59.4749185Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2025-07-17T09:05:59.4749259Z different types. 2025-07-17T09:05:59.4749462Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2025-07-17T09:05:59.4749672Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2025-07-17T09:05:59.4749858Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2025-07-17T09:05:59.4750033Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2025-07-17T09:05:59.4750112Z :attr:`~torch.Tensor.layout`. 2025-07-17T09:05:59.4750241Z AssertionError: If only one of corresponding tensors is quantized. 2025-07-17T09:05:59.4750595Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2025-07-17T09:05:59.4750768Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2025-07-17T09:05:59.4750842Z :attr:`~torch.Tensor.device`. 2025-07-17T09:05:59.4751253Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2025-07-17T09:05:59.4751462Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2025-07-17T09:05:59.4751678Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2025-07-17T09:05:59.4751730Z 2025-07-17T09:05:59.4751942Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2025-07-17T09:05:59.4752040Z ``dtype``'s, the maximum of both tolerances is used. 2025-07-17T09:05:59.4752108Z 2025-07-17T09:05:59.4752196Z +---------------------------+------------+----------+ 2025-07-17T09:05:59.4752276Z | ``dtype`` | ``rtol`` | ``atol`` | 2025-07-17T09:05:59.4752346Z +===========================+============+==========+ 2025-07-17T09:05:59.4752434Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2025-07-17T09:05:59.4752533Z +---------------------------+------------+----------+ 2025-07-17T09:05:59.4752615Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2025-07-17T09:05:59.4752697Z +---------------------------+------------+----------+ 2025-07-17T09:05:59.4752777Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2025-07-17T09:05:59.4752856Z +---------------------------+------------+----------+ 2025-07-17T09:05:59.4752937Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2025-07-17T09:05:59.4753024Z +---------------------------+------------+----------+ 2025-07-17T09:05:59.4753110Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2025-07-17T09:05:59.4753186Z +---------------------------+------------+----------+ 2025-07-17T09:05:59.4753264Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2025-07-17T09:05:59.4753343Z +---------------------------+------------+----------+ 2025-07-17T09:05:59.4753427Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2025-07-17T09:05:59.4753502Z +---------------------------+------------+----------+ 2025-07-17T09:05:59.4753580Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2025-07-17T09:05:59.4753656Z +---------------------------+------------+----------+ 2025-07-17T09:05:59.4753734Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2025-07-17T09:05:59.4753818Z +---------------------------+------------+----------+ 2025-07-17T09:05:59.4753902Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2025-07-17T09:05:59.4753983Z +---------------------------+------------+----------+ 2025-07-17T09:05:59.4754060Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2025-07-17T09:05:59.4754135Z +---------------------------+------------+----------+ 2025-07-17T09:05:59.4754212Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2025-07-17T09:05:59.4754298Z +---------------------------+------------+----------+ 2025-07-17T09:05:59.4754371Z | other | ``0.0`` | ``0.0`` | 2025-07-17T09:05:59.4754444Z +---------------------------+------------+----------+ 2025-07-17T09:05:59.4754503Z 2025-07-17T09:05:59.4754563Z .. note:: 2025-07-17T09:05:59.4754622Z 2025-07-17T09:05:59.4754844Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2025-07-17T09:05:59.4755045Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2025-07-17T09:05:59.4755320Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2025-07-17T09:05:59.4755384Z 2025-07-17T09:05:59.4755451Z >>> import functools 2025-07-17T09:05:59.4755611Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2025-07-17T09:05:59.4755785Z >>> assert_equal(1e-9, 1e-10) 2025-07-17T09:05:59.4755870Z Traceback (most recent call last): 2025-07-17T09:05:59.4755926Z ... 2025-07-17T09:05:59.4756012Z AssertionError: Scalars are not equal! 2025-07-17T09:05:59.4756077Z 2025-07-17T09:05:59.4756157Z Expected 1e-10 but got 1e-09. 2025-07-17T09:05:59.4756237Z Absolute difference: 9.000000000000001e-10 2025-07-17T09:05:59.4756311Z Relative difference: 9.0 2025-07-17T09:05:59.4756365Z 2025-07-17T09:05:59.4756425Z Examples: 2025-07-17T09:05:59.4756515Z >>> # tensor to tensor comparison 2025-07-17T09:05:59.4756607Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2025-07-17T09:05:59.4756706Z >>> actual = torch.acos(torch.cos(expected)) 2025-07-17T09:05:59.4756804Z >>> torch.testing.assert_close(actual, expected) 2025-07-17T09:05:59.4756860Z 2025-07-17T09:05:59.4756936Z >>> # scalar to scalar comparison 2025-07-17T09:05:59.4757005Z >>> import math 2025-07-17T09:05:59.4757075Z >>> expected = math.sqrt(2.0) 2025-07-17T09:05:59.4757146Z >>> actual = 2.0 / math.sqrt(2.0) 2025-07-17T09:05:59.4757232Z >>> torch.testing.assert_close(actual, expected) 2025-07-17T09:05:59.4757288Z 2025-07-17T09:05:59.4757378Z >>> # numpy array to numpy array comparison 2025-07-17T09:05:59.4757448Z >>> import numpy as np 2025-07-17T09:05:59.4757527Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2025-07-17T09:05:59.4757618Z >>> actual = np.arccos(np.cos(expected)) 2025-07-17T09:05:59.4757702Z >>> torch.testing.assert_close(actual, expected) 2025-07-17T09:05:59.4757755Z 2025-07-17T09:05:59.4757834Z >>> # sequence to sequence comparison 2025-07-17T09:05:59.4757901Z >>> import numpy as np 2025-07-17T09:05:59.4758063Z >>> # The types of the sequences do not have to match. They only have to have the same 2025-07-17T09:05:59.4758154Z >>> # length and their elements have to match. 2025-07-17T09:05:59.4758259Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2025-07-17T09:05:59.4758330Z >>> actual = tuple(expected) 2025-07-17T09:05:59.4758415Z >>> torch.testing.assert_close(actual, expected) 2025-07-17T09:05:59.4758467Z 2025-07-17T09:05:59.4758554Z >>> # mapping to mapping comparison 2025-07-17T09:05:59.4758634Z >>> from collections import OrderedDict 2025-07-17T09:05:59.4758711Z >>> import numpy as np 2025-07-17T09:05:59.4758780Z >>> foo = torch.tensor(1.0) 2025-07-17T09:05:59.4758855Z >>> bar = 2.0 2025-07-17T09:05:59.4758929Z >>> baz = np.array(3.0) 2025-07-17T09:05:59.4759087Z >>> # The types and a possible ordering of mappings do not have to match. They only 2025-07-17T09:05:59.4759220Z >>> # have to have the same set of keys and their elements have to match. 2025-07-17T09:05:59.4759355Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2025-07-17T09:05:59.4759443Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2025-07-17T09:05:59.4759539Z >>> torch.testing.assert_close(actual, expected) 2025-07-17T09:05:59.4759602Z 2025-07-17T09:05:59.4759684Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2025-07-17T09:05:59.4759762Z >>> actual = expected.clone() 2025-07-17T09:05:59.4759934Z >>> # By default, directly related instances can be compared 2025-07-17T09:05:59.4760122Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2025-07-17T09:05:59.4760257Z >>> # This check can be made more strict with allow_subclasses=False 2025-07-17T09:05:59.4760339Z >>> torch.testing.assert_close( 2025-07-17T09:05:59.4760561Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2025-07-17T09:05:59.4760626Z ... ) 2025-07-17T09:05:59.4760707Z Traceback (most recent call last): 2025-07-17T09:05:59.4760770Z ... 2025-07-17T09:05:59.4760895Z TypeError: No comparison pair was able to handle inputs of type 2025-07-17T09:05:59.4761032Z and . 2025-07-17T09:05:59.4761170Z >>> # If the inputs are not directly related, they are never considered close 2025-07-17T09:05:59.4761287Z >>> torch.testing.assert_close(actual.numpy(), expected) 2025-07-17T09:05:59.4761370Z Traceback (most recent call last): 2025-07-17T09:05:59.4761429Z ... 2025-07-17T09:05:59.4761598Z TypeError: No comparison pair was able to handle inputs of type 2025-07-17T09:05:59.4761677Z and . 2025-07-17T09:05:59.4761840Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2025-07-17T09:05:59.4761925Z >>> # their type if check_dtype=False. 2025-07-17T09:05:59.4762029Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2025-07-17T09:05:59.4762094Z 2025-07-17T09:05:59.4762166Z >>> # NaN != NaN by default. 2025-07-17T09:05:59.4762252Z >>> expected = torch.tensor(float("Nan")) 2025-07-17T09:05:59.4762324Z >>> actual = expected.clone() 2025-07-17T09:05:59.4762420Z >>> torch.testing.assert_close(actual, expected) 2025-07-17T09:05:59.4762498Z Traceback (most recent call last): 2025-07-17T09:05:59.4762553Z ... 2025-07-17T09:05:59.4762638Z AssertionError: Scalars are not close! 2025-07-17T09:05:59.4762700Z 2025-07-17T09:05:59.4762775Z Expected nan but got nan. 2025-07-17T09:05:59.4762872Z Absolute difference: nan (up to 1e-05 allowed) 2025-07-17T09:05:59.4762967Z Relative difference: nan (up to 1.3e-06 allowed) 2025-07-17T09:05:59.4763089Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2025-07-17T09:05:59.4763152Z 2025-07-17T09:05:59.4763236Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2025-07-17T09:05:59.4763320Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2025-07-17T09:05:59.4763415Z >>> # The default error message can be overwritten. 2025-07-17T09:05:59.4763590Z >>> torch.testing.assert_close(actual, expected, msg="Argh, the tensors are not close!") 2025-07-17T09:05:59.4763664Z Traceback (most recent call last): 2025-07-17T09:05:59.4763729Z ... 2025-07-17T09:05:59.4763824Z AssertionError: Argh, the tensors are not close! 2025-07-17T09:05:59.4763967Z >>> # If msg is a callable, it can be used to augment the generated message with 2025-07-17T09:05:59.4764038Z >>> # extra information 2025-07-17T09:05:59.4764119Z >>> torch.testing.assert_close( 2025-07-17T09:05:59.4764245Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2025-07-17T09:05:59.4764299Z ... ) 2025-07-17T09:05:59.4764374Z Traceback (most recent call last): 2025-07-17T09:05:59.4764427Z ... 2025-07-17T09:05:59.4764505Z AssertionError: Header 2025-07-17T09:05:59.4764569Z 2025-07-17T09:05:59.4764646Z Tensor-likes are not close! 2025-07-17T09:05:59.4764705Z 2025-07-17T09:05:59.4764843Z Mismatched elements: 2 / 3 (66.7%) 2025-07-17T09:05:59.4765053Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2025-07-17T09:05:59.4765196Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2025-07-17T09:05:59.4765253Z 2025-07-17T09:05:59.4765313Z Footer 2025-07-17T09:05:59.4765369Z 2025-07-17T09:05:59.4765623Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4765681Z 2025-07-17T09:05:59.4765747Z warnings.warn(msg) 2025-07-17T09:05:59.4765804Z 2025-07-17T09:05:59.4765935Z --- Parse Warning: 28 / 136 --- 2025-07-17T09:05:59.4766457Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=print_assert_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=286. 2025-07-17T09:05:59.4766613Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4766683Z 2025-07-17T09:05:59.4766817Z Test if two objects are equal, and print an error message if test fails. 2025-07-17T09:05:59.4766873Z 2025-07-17T09:05:59.4766976Z The test is performed with ``actual == desired``. 2025-07-17T09:05:59.4767042Z 2025-07-17T09:05:59.4767111Z Parameters 2025-07-17T09:05:59.4767172Z ---------- 2025-07-17T09:05:59.4767248Z test_string : str 2025-07-17T09:05:59.4767336Z The message supplied to AssertionError. 2025-07-17T09:05:59.4767405Z actual : object 2025-07-17T09:05:59.4767504Z The object to test for equality against `desired`. 2025-07-17T09:05:59.4767572Z desired : object 2025-07-17T09:05:59.4767641Z The expected result. 2025-07-17T09:05:59.4767699Z 2025-07-17T09:05:59.4767755Z Examples 2025-07-17T09:05:59.4767814Z -------- 2025-07-17T09:05:59.4767888Z >>> np.testing.print_assert_equal( 2025-07-17T09:05:59.4767974Z ... "Test XYZ of func xyz", [0, 1], [0, 1] 2025-07-17T09:05:59.4768044Z ... ) # doctest: +SKIP 2025-07-17T09:05:59.4768116Z >>> np.testing.print_assert_equal( 2025-07-17T09:05:59.4768188Z ... "Test XYZ of func xyz", [0, 1], [0, 2] 2025-07-17T09:05:59.4768247Z ... ) # doctest: +SKIP 2025-07-17T09:05:59.4768322Z Traceback (most recent call last): 2025-07-17T09:05:59.4768377Z ... 2025-07-17T09:05:59.4768468Z AssertionError: Test XYZ of func xyz failed 2025-07-17T09:05:59.4768526Z ACTUAL: 2025-07-17T09:05:59.4768586Z [0, 1] 2025-07-17T09:05:59.4768641Z DESIRED: 2025-07-17T09:05:59.4768705Z [0, 2] 2025-07-17T09:05:59.4768759Z 2025-07-17T09:05:59.4768814Z 2025-07-17T09:05:59.4768965Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4769023Z 2025-07-17T09:05:59.4769092Z warnings.warn(msg) 2025-07-17T09:05:59.4769144Z 2025-07-17T09:05:59.4769264Z --- Parse Warning: 29 / 136 --- 2025-07-17T09:05:59.4769776Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_almost_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=331. 2025-07-17T09:05:59.4769938Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4769992Z 2025-07-17T09:05:59.4770130Z Raises an AssertionError if two items are not equal up to desired 2025-07-17T09:05:59.4770190Z precision. 2025-07-17T09:05:59.4770245Z 2025-07-17T09:05:59.4770355Z .. note:: It is recommended to use one of `assert_allclose`, 2025-07-17T09:05:59.4770473Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-07-17T09:05:59.4770587Z instead of this function for more consistent floating point 2025-07-17T09:05:59.4770655Z comparisons. 2025-07-17T09:05:59.4770792Z 2025-07-17T09:05:59.4770927Z The test verifies that the elements of `actual` and `desired` satisfy. 2025-07-17T09:05:59.4771038Z 2025-07-17T09:05:59.4771141Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2025-07-17T09:05:59.4771192Z 2025-07-17T09:05:59.4771338Z That is a looser test than originally documented, but agrees with what the 2025-07-17T09:05:59.4771584Z actual implementation in `assert_array_almost_equal` did up to rounding 2025-07-17T09:05:59.4771729Z vagaries. An exception is raised at conflicting values. For ndarrays this 2025-07-17T09:05:59.4771808Z delegates to assert_array_almost_equal 2025-07-17T09:05:59.4771864Z 2025-07-17T09:05:59.4771928Z Parameters 2025-07-17T09:05:59.4771985Z ---------- 2025-07-17T09:05:59.4772053Z actual : array_like 2025-07-17T09:05:59.4772125Z The object to check. 2025-07-17T09:05:59.4772190Z desired : array_like 2025-07-17T09:05:59.4772262Z The expected object. 2025-07-17T09:05:59.4772330Z decimal : int, optional 2025-07-17T09:05:59.4772407Z Desired precision, default is 7. 2025-07-17T09:05:59.4772471Z err_msg : str, optional 2025-07-17T09:05:59.4772571Z The error message to be printed in case of failure. 2025-07-17T09:05:59.4772638Z verbose : bool, optional 2025-07-17T09:05:59.4772762Z If True, the conflicting values are appended to the error message. 2025-07-17T09:05:59.4772826Z 2025-07-17T09:05:59.4772884Z Raises 2025-07-17T09:05:59.4772940Z ------ 2025-07-17T09:05:59.4773000Z AssertionError 2025-07-17T09:05:59.4773120Z If actual and desired are not equal up to specified precision. 2025-07-17T09:05:59.4773171Z 2025-07-17T09:05:59.4773230Z See Also 2025-07-17T09:05:59.4773284Z -------- 2025-07-17T09:05:59.4773424Z assert_allclose: Compare two array_like objects for equality with desired 2025-07-17T09:05:59.4773510Z relative and/or absolute precision. 2025-07-17T09:05:59.4773645Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-07-17T09:05:59.4773700Z 2025-07-17T09:05:59.4773754Z Examples 2025-07-17T09:05:59.4773817Z -------- 2025-07-17T09:05:59.4773923Z >>> from torch._numpy.testing import assert_almost_equal 2025-07-17T09:05:59.4774014Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2025-07-17T09:05:59.4774119Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2025-07-17T09:05:59.4774201Z Traceback (most recent call last): 2025-07-17T09:05:59.4774257Z ... 2025-07-17T09:05:59.4774322Z AssertionError: 2025-07-17T09:05:59.4774403Z Arrays are not almost equal to 10 decimals 2025-07-17T09:05:59.4774469Z ACTUAL: 2.3333333333333 2025-07-17T09:05:59.4774534Z DESIRED: 2.33333334 2025-07-17T09:05:59.4774591Z 2025-07-17T09:05:59.4774659Z >>> assert_almost_equal( 2025-07-17T09:05:59.4774789Z ... np.array([1.0, 2.3333333333333]), np.array([1.0, 2.33333334]), decimal=9 2025-07-17T09:05:59.4774849Z ... ) 2025-07-17T09:05:59.4774928Z Traceback (most recent call last): 2025-07-17T09:05:59.4774984Z ... 2025-07-17T09:05:59.4775043Z AssertionError: 2025-07-17T09:05:59.4775122Z Arrays are not almost equal to 9 decimals 2025-07-17T09:05:59.4775179Z 2025-07-17T09:05:59.4775252Z Mismatched elements: 1 / 2 (50%) 2025-07-17T09:05:59.4775336Z Max absolute difference: 6.666699636781459e-09 2025-07-17T09:05:59.4775427Z Max relative difference: 2.8571569790287484e-09 2025-07-17T09:05:59.4775517Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2025-07-17T09:05:59.4775599Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2025-07-17T09:05:59.4775651Z 2025-07-17T09:05:59.4775706Z 2025-07-17T09:05:59.4775853Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4775906Z 2025-07-17T09:05:59.4775970Z warnings.warn(msg) 2025-07-17T09:05:59.4776027Z 2025-07-17T09:05:59.4776216Z --- Parse Warning: 30 / 136 --- 2025-07-17T09:05:59.4776780Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_approx_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=457. 2025-07-17T09:05:59.4776939Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4776991Z 2025-07-17T09:05:59.4777224Z Raises an AssertionError if two items are not equal up to significant 2025-07-17T09:05:59.4777280Z digits. 2025-07-17T09:05:59.4777333Z 2025-07-17T09:05:59.4777442Z .. note:: It is recommended to use one of `assert_allclose`, 2025-07-17T09:05:59.4777555Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-07-17T09:05:59.4777663Z instead of this function for more consistent floating point 2025-07-17T09:05:59.4777727Z comparisons. 2025-07-17T09:05:59.4777784Z 2025-07-17T09:05:59.4777911Z Given two numbers, check that they are approximately equal. 2025-07-17T09:05:59.4778049Z Approximately equal is defined as the number of significant digits 2025-07-17T09:05:59.4778114Z that agree. 2025-07-17T09:05:59.4778167Z 2025-07-17T09:05:59.4778232Z Parameters 2025-07-17T09:05:59.4778291Z ---------- 2025-07-17T09:05:59.4778350Z actual : scalar 2025-07-17T09:05:59.4778415Z The object to check. 2025-07-17T09:05:59.4778482Z desired : scalar 2025-07-17T09:05:59.4778549Z The expected object. 2025-07-17T09:05:59.4778616Z significant : int, optional 2025-07-17T09:05:59.4778699Z Desired precision, default is 7. 2025-07-17T09:05:59.4778764Z err_msg : str, optional 2025-07-17T09:05:59.4778865Z The error message to be printed in case of failure. 2025-07-17T09:05:59.4778930Z verbose : bool, optional 2025-07-17T09:05:59.4779057Z If True, the conflicting values are appended to the error message. 2025-07-17T09:05:59.4779114Z 2025-07-17T09:05:59.4779169Z Raises 2025-07-17T09:05:59.4779227Z ------ 2025-07-17T09:05:59.4779291Z AssertionError 2025-07-17T09:05:59.4779407Z If actual and desired are not equal up to specified precision. 2025-07-17T09:05:59.4779461Z 2025-07-17T09:05:59.4779515Z See Also 2025-07-17T09:05:59.4779570Z -------- 2025-07-17T09:05:59.4779717Z assert_allclose: Compare two array_like objects for equality with desired 2025-07-17T09:05:59.4779800Z relative and/or absolute precision. 2025-07-17T09:05:59.4779925Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-07-17T09:05:59.4779977Z 2025-07-17T09:05:59.4780033Z Examples 2025-07-17T09:05:59.4780088Z -------- 2025-07-17T09:05:59.4780168Z >>> np.testing.assert_approx_equal( 2025-07-17T09:05:59.4780240Z ... 0.12345677777777e-20, 0.1234567e-20 2025-07-17T09:05:59.4780308Z ... ) # doctest: +SKIP 2025-07-17T09:05:59.4780389Z >>> np.testing.assert_approx_equal( 2025-07-17T09:05:59.4780451Z ... 0.12345670e-20, 2025-07-17T09:05:59.4780526Z ... 0.12345671e-20, # doctest: +SKIP 2025-07-17T09:05:59.4780596Z ... significant=8, 2025-07-17T09:05:59.4780653Z ... ) 2025-07-17T09:05:59.4780724Z >>> np.testing.assert_approx_equal( 2025-07-17T09:05:59.4780785Z ... 0.12345670e-20, 2025-07-17T09:05:59.4780851Z ... 0.12345672e-20, # doctest: +SKIP 2025-07-17T09:05:59.4780924Z ... significant=8, 2025-07-17T09:05:59.4780982Z ... ) 2025-07-17T09:05:59.4781062Z Traceback (most recent call last): 2025-07-17T09:05:59.4781118Z ... 2025-07-17T09:05:59.4781179Z AssertionError: 2025-07-17T09:05:59.4781263Z Items are not equal to 8 significant digits: 2025-07-17T09:05:59.4781332Z ACTUAL: 1.234567e-21 2025-07-17T09:05:59.4781396Z DESIRED: 1.2345672e-21 2025-07-17T09:05:59.4781455Z 2025-07-17T09:05:59.4781557Z the evaluated condition that raises the exception is 2025-07-17T09:05:59.4781686Z 2025-07-17T09:05:59.4781862Z >>> abs(0.12345670e-20 / 1e-21 - 0.12345672e-20 / 1e-21) >= 10 ** -(8 - 1) 2025-07-17T09:05:59.4781922Z True 2025-07-17T09:05:59.4781985Z 2025-07-17T09:05:59.4782036Z 2025-07-17T09:05:59.4782186Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4782239Z 2025-07-17T09:05:59.4782309Z warnings.warn(msg) 2025-07-17T09:05:59.4782462Z 2025-07-17T09:05:59.4782585Z --- Parse Warning: 31 / 136 --- 2025-07-17T09:05:59.4783086Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_array_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=744. 2025-07-17T09:05:59.4783247Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4783301Z 2025-07-17T09:05:59.4783428Z Raises an AssertionError if two array_like objects are not equal. 2025-07-17T09:05:59.4783485Z 2025-07-17T09:05:59.4783613Z Given two array_like objects, check that the shape is equal and all 2025-07-17T09:05:59.4783743Z elements of these objects are equal (but see the Notes for the special 2025-07-17T09:05:59.4783867Z handling of a scalar). An exception is raised at shape mismatch or 2025-07-17T09:05:59.4784087Z conflicting values. In contrast to the standard usage in numpy, NaNs 2025-07-17T09:05:59.4784216Z are compared like numbers, no assertion is raised if both objects have 2025-07-17T09:05:59.4784287Z NaNs in the same positions. 2025-07-17T09:05:59.4784340Z 2025-07-17T09:05:59.4784485Z The usual caution for verifying equality with floating point numbers is 2025-07-17T09:05:59.4784543Z advised. 2025-07-17T09:05:59.4784600Z 2025-07-17T09:05:59.4784657Z Parameters 2025-07-17T09:05:59.4784720Z ---------- 2025-07-17T09:05:59.4784779Z x : array_like 2025-07-17T09:05:59.4784857Z The actual object to check. 2025-07-17T09:05:59.4784917Z y : array_like 2025-07-17T09:05:59.4784992Z The desired, expected object. 2025-07-17T09:05:59.4785057Z err_msg : str, optional 2025-07-17T09:05:59.4785161Z The error message to be printed in case of failure. 2025-07-17T09:05:59.4785229Z verbose : bool, optional 2025-07-17T09:05:59.4785420Z If True, the conflicting values are appended to the error message. 2025-07-17T09:05:59.4785492Z strict : bool, optional 2025-07-17T09:05:59.4785609Z If True, raise an AssertionError when either the shape or the data 2025-07-17T09:05:59.4785725Z type of the array_like objects does not match. The special 2025-07-17T09:05:59.4785849Z handling for scalars mentioned in the Notes section is disabled. 2025-07-17T09:05:59.4785906Z 2025-07-17T09:05:59.4785961Z Raises 2025-07-17T09:05:59.4786019Z ------ 2025-07-17T09:05:59.4786080Z AssertionError 2025-07-17T09:05:59.4786176Z If actual and desired objects are not equal. 2025-07-17T09:05:59.4786234Z 2025-07-17T09:05:59.4786294Z See Also 2025-07-17T09:05:59.4786351Z -------- 2025-07-17T09:05:59.4786497Z assert_allclose: Compare two array_like objects for equality with desired 2025-07-17T09:05:59.4786583Z relative and/or absolute precision. 2025-07-17T09:05:59.4786716Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-07-17T09:05:59.4786772Z 2025-07-17T09:05:59.4786837Z Notes 2025-07-17T09:05:59.4786891Z ----- 2025-07-17T09:05:59.4787003Z When one of `x` and `y` is a scalar and the other is array_like, the 2025-07-17T09:05:59.4787138Z function checks that each element of the array_like object is equal to 2025-07-17T09:05:59.4787270Z the scalar. This behaviour can be disabled with the `strict` parameter. 2025-07-17T09:05:59.4787324Z 2025-07-17T09:05:59.4787378Z Examples 2025-07-17T09:05:59.4787440Z -------- 2025-07-17T09:05:59.4787626Z The first assert does not raise an exception: 2025-07-17T09:05:59.4787745Z 2025-07-17T09:05:59.4787819Z >>> np.testing.assert_array_equal( 2025-07-17T09:05:59.4787918Z ... [1.0, 2.33333, np.nan], [np.exp(0), 2.33333, np.nan] 2025-07-17T09:05:59.4787971Z ... ) 2025-07-17T09:05:59.4788027Z 2025-07-17T09:05:59.4788159Z Use `assert_allclose` or one of the nulp (number of floating point values) 2025-07-17T09:05:59.4788356Z functions for these cases instead: 2025-07-17T09:05:59.4788411Z 2025-07-17T09:05:59.4788483Z >>> np.testing.assert_allclose( 2025-07-17T09:05:59.4788610Z ... [1.0, np.pi, np.nan], [1, np.sqrt(np.pi) ** 2, np.nan], rtol=1e-10, atol=0 2025-07-17T09:05:59.4788669Z ... ) 2025-07-17T09:05:59.4788731Z 2025-07-17T09:05:59.4788855Z As mentioned in the Notes section, `assert_array_equal` has special 2025-07-17T09:05:59.4788995Z handling for scalars. Here the test checks that each value in `x` is 3: 2025-07-17T09:05:59.4789049Z 2025-07-17T09:05:59.4789126Z >>> x = np.full((2, 5), fill_value=3) 2025-07-17T09:05:59.4789208Z >>> np.testing.assert_array_equal(x, 3) 2025-07-17T09:05:59.4789268Z 2025-07-17T09:05:59.4789390Z Use `strict` to raise an AssertionError when comparing a scalar with an 2025-07-17T09:05:59.4789449Z array: 2025-07-17T09:05:59.4789503Z 2025-07-17T09:05:59.4789602Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2025-07-17T09:05:59.4789677Z Traceback (most recent call last): 2025-07-17T09:05:59.4789731Z ... 2025-07-17T09:05:59.4789798Z AssertionError: 2025-07-17T09:05:59.4789863Z Arrays are not equal 2025-07-17T09:05:59.4789921Z 2025-07-17T09:05:59.4789987Z (shapes (2, 5), () mismatch) 2025-07-17T09:05:59.4790060Z x: torch.ndarray([[3, 3, 3, 3, 3], 2025-07-17T09:05:59.4790120Z [3, 3, 3, 3, 3]]) 2025-07-17T09:05:59.4790190Z y: torch.ndarray(3) 2025-07-17T09:05:59.4790245Z 2025-07-17T09:05:59.4790373Z The `strict` parameter also ensures that the array data types match: 2025-07-17T09:05:59.4790438Z 2025-07-17T09:05:59.4790510Z >>> x = np.array([2, 2, 2]) 2025-07-17T09:05:59.4790598Z >>> y = np.array([2.0, 2.0, 2.0], dtype=np.float32) 2025-07-17T09:05:59.4790696Z >>> np.testing.assert_array_equal(x, y, strict=True) 2025-07-17T09:05:59.4790767Z Traceback (most recent call last): 2025-07-17T09:05:59.4790823Z ... 2025-07-17T09:05:59.4790895Z AssertionError: 2025-07-17T09:05:59.4790961Z Arrays are not equal 2025-07-17T09:05:59.4791018Z 2025-07-17T09:05:59.4791113Z (dtypes dtype("int64"), dtype("float32") mismatch) 2025-07-17T09:05:59.4791188Z x: torch.ndarray([2, 2, 2]) 2025-07-17T09:05:59.4791254Z y: torch.ndarray([2., 2., 2.]) 2025-07-17T09:05:59.4791312Z 2025-07-17T09:05:59.4791459Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4791515Z 2025-07-17T09:05:59.4791578Z warnings.warn(msg) 2025-07-17T09:05:59.4791635Z 2025-07-17T09:05:59.4791765Z --- Parse Warning: 32 / 136 --- 2025-07-17T09:05:59.4792289Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_array_almost_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=851. 2025-07-17T09:05:59.4792449Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4792506Z 2025-07-17T09:05:59.4792635Z Raises an AssertionError if two objects are not equal up to desired 2025-07-17T09:05:59.4792699Z precision. 2025-07-17T09:05:59.4792753Z 2025-07-17T09:05:59.4792864Z .. note:: It is recommended to use one of `assert_allclose`, 2025-07-17T09:05:59.4792976Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-07-17T09:05:59.4793090Z instead of this function for more consistent floating point 2025-07-17T09:05:59.4793240Z comparisons. 2025-07-17T09:05:59.4793452Z 2025-07-17T09:05:59.4793598Z The test verifies identical shapes and that the elements of ``actual`` and 2025-07-17T09:05:59.4793661Z ``desired`` satisfy. 2025-07-17T09:05:59.4793715Z 2025-07-17T09:05:59.4793803Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2025-07-17T09:05:59.4793857Z 2025-07-17T09:05:59.4794097Z That is a looser test than originally documented, but agrees with what the 2025-07-17T09:05:59.4794244Z actual implementation did up to rounding vagaries. An exception is raised 2025-07-17T09:05:59.4794382Z at shape mismatch or conflicting values. In contrast to the standard usage 2025-07-17T09:05:59.4794515Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2025-07-17T09:05:59.4794599Z objects have NaNs in the same positions. 2025-07-17T09:05:59.4794654Z 2025-07-17T09:05:59.4794713Z Parameters 2025-07-17T09:05:59.4794773Z ---------- 2025-07-17T09:05:59.4794842Z x : array_like 2025-07-17T09:05:59.4794912Z The actual object to check. 2025-07-17T09:05:59.4794971Z y : array_like 2025-07-17T09:05:59.4795044Z The desired, expected object. 2025-07-17T09:05:59.4795110Z decimal : int, optional 2025-07-17T09:05:59.4795184Z Desired precision, default is 6. 2025-07-17T09:05:59.4795257Z err_msg : str, optional 2025-07-17T09:05:59.4795358Z The error message to be printed in case of failure. 2025-07-17T09:05:59.4795429Z verbose : bool, optional 2025-07-17T09:05:59.4795553Z If True, the conflicting values are appended to the error message. 2025-07-17T09:05:59.4795613Z 2025-07-17T09:05:59.4795666Z Raises 2025-07-17T09:05:59.4795727Z ------ 2025-07-17T09:05:59.4795788Z AssertionError 2025-07-17T09:05:59.4795908Z If actual and desired are not equal up to specified precision. 2025-07-17T09:05:59.4795964Z 2025-07-17T09:05:59.4796020Z See Also 2025-07-17T09:05:59.4796084Z -------- 2025-07-17T09:05:59.4796222Z assert_allclose: Compare two array_like objects for equality with desired 2025-07-17T09:05:59.4796309Z relative and/or absolute precision. 2025-07-17T09:05:59.4796429Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-07-17T09:05:59.4796488Z 2025-07-17T09:05:59.4796544Z Examples 2025-07-17T09:05:59.4796601Z -------- 2025-07-17T09:05:59.4796692Z the first assert does not raise an exception 2025-07-17T09:05:59.4796755Z 2025-07-17T09:05:59.4796906Z >>> np.testing.assert_array_almost_equal([1.0, 2.333, np.nan], [1.0, 2.333, np.nan]) 2025-07-17T09:05:59.4796964Z 2025-07-17T09:05:59.4797042Z >>> np.testing.assert_array_almost_equal( 2025-07-17T09:05:59.4797137Z ... [1.0, 2.33333, np.nan], [1.0, 2.33339, np.nan], decimal=5 2025-07-17T09:05:59.4797196Z ... ) 2025-07-17T09:05:59.4797269Z Traceback (most recent call last): 2025-07-17T09:05:59.4797327Z ... 2025-07-17T09:05:59.4797390Z AssertionError: 2025-07-17T09:05:59.4797469Z Arrays are not almost equal to 5 decimals 2025-07-17T09:05:59.4797526Z 2025-07-17T09:05:59.4797603Z Mismatched elements: 1 / 3 (33.3%) 2025-07-17T09:05:59.4797687Z Max absolute difference: 5.999999999994898e-05 2025-07-17T09:05:59.4797771Z Max relative difference: 2.5713661239633743e-05 2025-07-17T09:05:59.4797873Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2025-07-17T09:05:59.4797972Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2025-07-17T09:05:59.4798025Z 2025-07-17T09:05:59.4798105Z >>> np.testing.assert_array_almost_equal( 2025-07-17T09:05:59.4798192Z ... [1.0, 2.33333, np.nan], [1.0, 2.33333, 5], decimal=5 2025-07-17T09:05:59.4798248Z ... ) 2025-07-17T09:05:59.4798321Z Traceback (most recent call last): 2025-07-17T09:05:59.4798377Z ... 2025-07-17T09:05:59.4798440Z AssertionError: 2025-07-17T09:05:59.4798513Z Arrays are not almost equal to 5 decimals 2025-07-17T09:05:59.4798649Z 2025-07-17T09:05:59.4798774Z x and y nan location mismatch: 2025-07-17T09:05:59.4798883Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2025-07-17T09:05:59.4798975Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2025-07-17T09:05:59.4799029Z 2025-07-17T09:05:59.4799084Z 2025-07-17T09:05:59.4799343Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4799398Z 2025-07-17T09:05:59.4799471Z warnings.warn(msg) 2025-07-17T09:05:59.4799526Z 2025-07-17T09:05:59.4799653Z --- Parse Warning: 33 / 136 --- 2025-07-17T09:05:59.4800170Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=clear_and_catch_warnings in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=1848. 2025-07-17T09:05:59.4800327Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4800458Z Context manager that resets warning registry for catching warnings 2025-07-17T09:05:59.4800511Z 2025-07-17T09:05:59.4800651Z Warnings can be slippery, because, whenever a warning is triggered, Python 2025-07-17T09:05:59.4800781Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2025-07-17T09:05:59.4800941Z it impossible to retrigger the warning in this module, whatever you put in 2025-07-17T09:05:59.4801086Z the warnings filters. This context manager accepts a sequence of `modules` 2025-07-17T09:05:59.4801177Z as a keyword argument to its constructor and: 2025-07-17T09:05:59.4801229Z 2025-07-17T09:05:59.4801365Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2025-07-17T09:05:59.4801424Z on entry; 2025-07-17T09:05:59.4801538Z * resets ``__warningregistry__`` to its previous state on exit. 2025-07-17T09:05:59.4801595Z 2025-07-17T09:05:59.4801733Z This makes it possible to trigger any warning afresh inside the context 2025-07-17T09:05:59.4801847Z manager without disturbing the state of warnings outside. 2025-07-17T09:05:59.4801905Z 2025-07-17T09:05:59.4802042Z For compatibility with Python 3.0, please consider all arguments to be 2025-07-17T09:05:59.4802107Z keyword-only. 2025-07-17T09:05:59.4802160Z 2025-07-17T09:05:59.4802227Z Parameters 2025-07-17T09:05:59.4802287Z ---------- 2025-07-17T09:05:59.4802357Z record : bool, optional 2025-07-17T09:05:59.4802472Z Specifies whether warnings should be captured by a custom 2025-07-17T09:05:59.4802611Z implementation of ``warnings.showwarning()`` and be appended to a list 2025-07-17T09:05:59.4802736Z returned by the context manager. Otherwise None is returned by the 2025-07-17T09:05:59.4802869Z context manager. The objects appended to the list are arguments whose 2025-07-17T09:05:59.4802983Z attributes mirror the arguments to ``showwarning()``. 2025-07-17T09:05:59.4803062Z modules : sequence, optional 2025-07-17T09:05:59.4803197Z Sequence of modules for which to reset warnings registry on entry and 2025-07-17T09:05:59.4803315Z restore on exit. To work correctly, all 'ignore' filters should 2025-07-17T09:05:59.4803396Z filter by one of these modules. 2025-07-17T09:05:59.4803452Z 2025-07-17T09:05:59.4803513Z Examples 2025-07-17T09:05:59.4803572Z -------- 2025-07-17T09:05:59.4803644Z >>> import warnings 2025-07-17T09:05:59.4803754Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2025-07-17T09:05:59.4803837Z ... modules=[np.core.fromnumeric] 2025-07-17T09:05:59.4803894Z ... ): 2025-07-17T09:05:59.4803990Z ... warnings.simplefilter("always") 2025-07-17T09:05:59.4804125Z ... warnings.filterwarnings("ignore", module="np.core.fromnumeric") 2025-07-17T09:05:59.4804294Z ... # do something that raises a warning but ignore those in 2025-07-17T09:05:59.4804423Z ... # np.core.fromnumeric 2025-07-17T09:05:59.4804485Z 2025-07-17T09:05:59.4804635Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4804689Z 2025-07-17T09:05:59.4804766Z warnings.warn(msg) 2025-07-17T09:05:59.4804822Z 2025-07-17T09:05:59.4805076Z --- Parse Warning: 34 / 136 --- 2025-07-17T09:05:59.4805638Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ThroughputBenchmark in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/throughput_benchmark.py line=61. 2025-07-17T09:05:59.4805807Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4805863Z 2025-07-17T09:05:59.4806052Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2025-07-17T09:05:59.4806111Z 2025-07-17T09:05:59.4806295Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2025-07-17T09:05:59.4806443Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2025-07-17T09:05:59.4806585Z server like load. It can emulate multiple calling threads to a single module 2025-07-17T09:05:59.4806731Z provided. In the future we plan to enhance this component to support inter and 2025-07-17T09:05:59.4806882Z intra-op parallelism as well as multiple models running in a single process. 2025-07-17T09:05:59.4806936Z 2025-07-17T09:05:59.4807089Z Please note that even though nn.Module is supported, it might incur an overhead 2025-07-17T09:05:59.4807219Z from the need to hold GIL every time we execute Python code or pass around 2025-07-17T09:05:59.4807360Z inputs as Python objects. As soon as you have a ScriptModule version of your 2025-07-17T09:05:59.4807504Z model for inference deployment it is better to switch to using it in this 2025-07-17T09:05:59.4807567Z benchmark. 2025-07-17T09:05:59.4807623Z 2025-07-17T09:05:59.4807681Z Example:: 2025-07-17T09:05:59.4807734Z 2025-07-17T09:05:59.4807821Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T09:05:59.4807911Z >>> from torch.utils import ThroughputBenchmark 2025-07-17T09:05:59.4808002Z >>> bench = ThroughputBenchmark(my_module) 2025-07-17T09:05:59.4808113Z >>> # Pre-populate benchmark's data set with the inputs 2025-07-17T09:05:59.4808182Z >>> for input in inputs: 2025-07-17T09:05:59.4808320Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2025-07-17T09:05:59.4808407Z ... bench.add_input(input[0], x2=input[1]) 2025-07-17T09:05:59.4808530Z >>> # Inputs supplied above are randomly used during the execution 2025-07-17T09:05:59.4808598Z >>> stats = bench.benchmark( 2025-07-17T09:05:59.4808672Z ... num_calling_threads=4, 2025-07-17T09:05:59.4808741Z ... num_warmup_iters = 100, 2025-07-17T09:05:59.4808808Z ... num_iters = 1000, 2025-07-17T09:05:59.4808864Z ... ) 2025-07-17T09:05:59.4808985Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2025-07-17T09:05:59.4809093Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2025-07-17T09:05:59.4809157Z 2025-07-17T09:05:59.4809308Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4809369Z 2025-07-17T09:05:59.4809435Z warnings.warn(msg) 2025-07-17T09:05:59.4809492Z 2025-07-17T09:05:59.4809613Z --- Parse Warning: 35 / 136 --- 2025-07-17T09:05:59.4810113Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CppExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1147. 2025-07-17T09:05:59.4810340Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4810447Z 2025-07-17T09:05:59.4810541Z Create a :class:`setuptools.Extension` for C++. 2025-07-17T09:05:59.4810598Z 2025-07-17T09:05:59.4810750Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-07-17T09:05:59.4810996Z bare minimum (but often sufficient) arguments to build a C++ extension. 2025-07-17T09:05:59.4811057Z 2025-07-17T09:05:59.4811186Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-07-17T09:05:59.4811292Z constructor. Full list arguments can be found at 2025-07-17T09:05:59.4811487Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2025-07-17T09:05:59.4811549Z 2025-07-17T09:05:59.4811610Z .. warning:: 2025-07-17T09:05:59.4811751Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-07-17T09:05:59.4811878Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-07-17T09:05:59.4812006Z the user's responsibility in their library to not use APIs from 2025-07-17T09:05:59.4812138Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-07-17T09:05:59.4812267Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-07-17T09:05:59.4812398Z example, to give access to custom ops from python, the library should 2025-07-17T09:05:59.4812485Z register the ops through the dispatcher. 2025-07-17T09:05:59.4812541Z 2025-07-17T09:05:59.4812673Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-07-17T09:05:59.4812794Z as a compile flag when py_limited_api is specified as an option for 2025-07-17T09:05:59.4812922Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-07-17T09:05:59.4813047Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-07-17T09:05:59.4813176Z safety, and sanity in order to encourage best practices. To target a 2025-07-17T09:05:59.4813309Z different version, set min_supported_cpython to the hexcode of the 2025-07-17T09:05:59.4813383Z CPython version of choice. 2025-07-17T09:05:59.4813443Z 2025-07-17T09:05:59.4813498Z Example: 2025-07-17T09:05:59.4813570Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.4813674Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-07-17T09:05:59.4813753Z >>> from setuptools import setup 2025-07-17T09:05:59.4813884Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2025-07-17T09:05:59.4813945Z >>> setup( 2025-07-17T09:05:59.4814010Z ... name='extension', 2025-07-17T09:05:59.4814076Z ... ext_modules=[ 2025-07-17T09:05:59.4814143Z ... CppExtension( 2025-07-17T09:05:59.4814215Z ... name='extension', 2025-07-17T09:05:59.4814297Z ... sources=['extension.cpp'], 2025-07-17T09:05:59.4814376Z ... extra_compile_args=['-g'], 2025-07-17T09:05:59.4814477Z ... extra_link_args=['-Wl,--no-as-needed', '-lm']) 2025-07-17T09:05:59.4814539Z ... ], 2025-07-17T09:05:59.4814602Z ... cmdclass={ 2025-07-17T09:05:59.4814680Z ... 'build_ext': BuildExtension 2025-07-17T09:05:59.4814753Z ... }) 2025-07-17T09:05:59.4814808Z 2025-07-17T09:05:59.4814961Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4815014Z 2025-07-17T09:05:59.4815087Z warnings.warn(msg) 2025-07-17T09:05:59.4815142Z 2025-07-17T09:05:59.4815266Z --- Parse Warning: 36 / 136 --- 2025-07-17T09:05:59.4815762Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CUDAExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1217. 2025-07-17T09:05:59.4816039Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4816095Z 2025-07-17T09:05:59.4816200Z Create a :class:`setuptools.Extension` for CUDA/C++. 2025-07-17T09:05:59.4816255Z 2025-07-17T09:05:59.4816400Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-07-17T09:05:59.4816626Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2025-07-17T09:05:59.4816765Z extension. This includes the CUDA include path, library path and runtime 2025-07-17T09:05:59.4816820Z library. 2025-07-17T09:05:59.4816875Z 2025-07-17T09:05:59.4816998Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-07-17T09:05:59.4817089Z constructor. Full list arguments can be found at 2025-07-17T09:05:59.4817305Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2025-07-17T09:05:59.4817360Z 2025-07-17T09:05:59.4817423Z .. warning:: 2025-07-17T09:05:59.4817556Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-07-17T09:05:59.4817682Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-07-17T09:05:59.4817804Z the user's responsibility in their library to not use APIs from 2025-07-17T09:05:59.4817951Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-07-17T09:05:59.4818075Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-07-17T09:05:59.4818203Z example, to give access to custom ops from python, the library should 2025-07-17T09:05:59.4818285Z register the ops through the dispatcher. 2025-07-17T09:05:59.4818340Z 2025-07-17T09:05:59.4818467Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-07-17T09:05:59.4818587Z as a compile flag when py_limited_api is specified as an option for 2025-07-17T09:05:59.4818708Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-07-17T09:05:59.4818839Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-07-17T09:05:59.4818963Z safety, and sanity in order to encourage best practices. To target a 2025-07-17T09:05:59.4819092Z different version, set min_supported_cpython to the hexcode of the 2025-07-17T09:05:59.4819165Z CPython version of choice. 2025-07-17T09:05:59.4819226Z 2025-07-17T09:05:59.4819283Z Example: 2025-07-17T09:05:59.4819352Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.4819444Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-07-17T09:05:59.4819521Z >>> from setuptools import setup 2025-07-17T09:05:59.4819658Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2025-07-17T09:05:59.4819716Z >>> setup( 2025-07-17T09:05:59.4819788Z ... name='cuda_extension', 2025-07-17T09:05:59.4819853Z ... ext_modules=[ 2025-07-17T09:05:59.4819925Z ... CUDAExtension( 2025-07-17T09:05:59.4819997Z ... name='cuda_extension', 2025-07-17T09:05:59.4820104Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-07-17T09:05:59.4820190Z ... extra_compile_args={'cxx': ['-g'], 2025-07-17T09:05:59.4820286Z ... 'nvcc': ['-O2']}, 2025-07-17T09:05:59.4820378Z ... extra_link_args=['-Wl,--no-as-needed', '-lcuda']) 2025-07-17T09:05:59.4820437Z ... ], 2025-07-17T09:05:59.4820501Z ... cmdclass={ 2025-07-17T09:05:59.4820581Z ... 'build_ext': BuildExtension 2025-07-17T09:05:59.4820639Z ... }) 2025-07-17T09:05:59.4820692Z 2025-07-17T09:05:59.4820757Z Compute capabilities: 2025-07-17T09:05:59.4820814Z 2025-07-17T09:05:59.4820996Z By default the extension will be compiled to run on all archs of the cards visible during the 2025-07-17T09:05:59.4821283Z building process of the extension, plus PTX. If down the road a new card is installed the 2025-07-17T09:05:59.4821462Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2025-07-17T09:05:59.4821640Z newer than the newest version for which your nvcc can build fully-compiled binaries, PyTorch 2025-07-17T09:05:59.4821912Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2025-07-17T09:05:59.4821995Z support (see below for details on PTX). 2025-07-17T09:05:59.4822055Z 2025-07-17T09:05:59.4822235Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2025-07-17T09:05:59.4822322Z CCs you want the extension to support: 2025-07-17T09:05:59.4822376Z 2025-07-17T09:05:59.4822503Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2025-07-17T09:05:59.4822647Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2025-07-17T09:05:59.4822705Z 2025-07-17T09:05:59.4822885Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2025-07-17T09:05:59.4823075Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2025-07-17T09:05:59.4823251Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2025-07-17T09:05:59.4823423Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2025-07-17T09:05:59.4823607Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2025-07-17T09:05:59.4823776Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2025-07-17T09:05:59.4823961Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2025-07-17T09:05:59.4824142Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2025-07-17T09:05:59.4824215Z "8.0 8.6" would be better. 2025-07-17T09:05:59.4824275Z 2025-07-17T09:05:59.4824445Z Note that while it's possible to include all supported archs, the more archs get included the 2025-07-17T09:05:59.4824622Z slower the building process will be, as it will build a separate kernel image for each arch. 2025-07-17T09:05:59.4824678Z 2025-07-17T09:05:59.4824880Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2025-07-17T09:05:59.4825012Z To workaround the issue, move python binding logic to pure C++ file. 2025-07-17T09:05:59.4825072Z 2025-07-17T09:05:59.4825131Z Example use: 2025-07-17T09:05:59.4825198Z #include 2025-07-17T09:05:59.4825359Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2025-07-17T09:05:59.4825414Z 2025-07-17T09:05:59.4825482Z Instead of: 2025-07-17T09:05:59.4825555Z #include 2025-07-17T09:05:59.4825659Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2025-07-17T09:05:59.4825716Z 2025-07-17T09:05:59.4825890Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2025-07-17T09:05:59.4826187Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2025-07-17T09:05:59.4826247Z 2025-07-17T09:05:59.4826317Z Relocatable device code linking: 2025-07-17T09:05:59.4826376Z 2025-07-17T09:05:59.4826542Z If you want to reference device symbols across compilation units (across object files), 2025-07-17T09:05:59.4826701Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2025-07-17T09:05:59.4826911Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2025-07-17T09:05:59.4827187Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2025-07-17T09:05:59.4827444Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2025-07-17T09:05:59.4827562Z helps reduce the protentional perf degradation of `-rdc`. 2025-07-17T09:05:59.4827661Z Note that it needs to be used at both steps to be useful. 2025-07-17T09:05:59.4827722Z 2025-07-17T09:05:59.4828076Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2025-07-17T09:05:59.4828192Z There is also a case where `-dlink` is used without `-rdc`: 2025-07-17T09:05:59.4828345Z when an extension is linked against a static lib containing rdc-compiled objects 2025-07-17T09:05:59.4828485Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2025-07-17T09:05:59.4828540Z 2025-07-17T09:05:59.4828672Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2025-07-17T09:05:59.4828726Z 2025-07-17T09:05:59.4828782Z Example: 2025-07-17T09:05:59.4828855Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.4828951Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-07-17T09:05:59.4829017Z >>> CUDAExtension( 2025-07-17T09:05:59.4829092Z ... name='cuda_extension', 2025-07-17T09:05:59.4829203Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-07-17T09:05:59.4829267Z ... dlink=True, 2025-07-17T09:05:59.4829349Z ... dlink_libraries=["dlink_lib"], 2025-07-17T09:05:59.4829431Z ... extra_compile_args={'cxx': ['-g'], 2025-07-17T09:05:59.4829520Z ... 'nvcc': ['-O2', '-rdc=true']}) 2025-07-17T09:05:59.4829574Z 2025-07-17T09:05:59.4829727Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4829783Z 2025-07-17T09:05:59.4829847Z warnings.warn(msg) 2025-07-17T09:05:59.4829899Z 2025-07-17T09:05:59.4830026Z --- Parse Warning: 37 / 136 --- 2025-07-17T09:05:59.4830534Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SyclExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1408. 2025-07-17T09:05:59.4830698Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4830753Z 2025-07-17T09:05:59.4830856Z Creates a :class:`setuptools.Extension` for SYCL/C++. 2025-07-17T09:05:59.4830918Z 2025-07-17T09:05:59.4831063Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-07-17T09:05:59.4831190Z bare minimum (but often sufficient) arguments to build a SYCL/C++ 2025-07-17T09:05:59.4831251Z extension. 2025-07-17T09:05:59.4831309Z 2025-07-17T09:05:59.4831430Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-07-17T09:05:59.4831503Z constructor. 2025-07-17T09:05:59.4831557Z 2025-07-17T09:05:59.4831620Z .. warning:: 2025-07-17T09:05:59.4831753Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-07-17T09:05:59.4831883Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-07-17T09:05:59.4832002Z the user's responsibility in their library to not use APIs from 2025-07-17T09:05:59.4832141Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-07-17T09:05:59.4832268Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-07-17T09:05:59.4832401Z example, to give access to custom ops from python, the library should 2025-07-17T09:05:59.4832486Z register the ops through the dispatcher. 2025-07-17T09:05:59.4832544Z 2025-07-17T09:05:59.4832671Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-07-17T09:05:59.4832860Z as a compile flag when py_limited_api is specified as an option for 2025-07-17T09:05:59.4833037Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-07-17T09:05:59.4833162Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-07-17T09:05:59.4833289Z safety, and sanity in order to encourage best practices. To target a 2025-07-17T09:05:59.4833521Z different version, set min_supported_cpython to the hexcode of the 2025-07-17T09:05:59.4833596Z CPython version of choice. 2025-07-17T09:05:59.4833650Z 2025-07-17T09:05:59.4833710Z Example: 2025-07-17T09:05:59.4833774Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.4833874Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-07-17T09:05:59.4834003Z >>> from torch.utils.cpp_extension import BuildExtension, SyclExtension 2025-07-17T09:05:59.4834066Z >>> setup( 2025-07-17T09:05:59.4834133Z ... name='xpu_extension', 2025-07-17T09:05:59.4834202Z ... ext_modules=[ 2025-07-17T09:05:59.4834268Z ... SyclExtension( 2025-07-17T09:05:59.4834346Z ... name='xpu_extension', 2025-07-17T09:05:59.4834444Z ... sources=['extension.cpp', 'extension_kernel.cpp'], 2025-07-17T09:05:59.4834563Z ... extra_compile_args={'cxx': ['-g', '-std=c++20', '-fPIC']}) 2025-07-17T09:05:59.4834619Z ... ], 2025-07-17T09:05:59.4834692Z ... cmdclass={ 2025-07-17T09:05:59.4834770Z ... 'build_ext': BuildExtension 2025-07-17T09:05:59.4834828Z ... }) 2025-07-17T09:05:59.4834888Z 2025-07-17T09:05:59.4835067Z By default the extension will be compiled to run on all archs of the cards visible during the 2025-07-17T09:05:59.4835223Z building process of the extension. If down the road a new card is installed the 2025-07-17T09:05:59.4835372Z extension may need to be recompiled. You can override the default behavior using 2025-07-17T09:05:59.4835554Z `TORCH_XPU_ARCH_LIST` to explicitly specify which device architectures you want the extension 2025-07-17T09:05:59.4835615Z to support: 2025-07-17T09:05:59.4835675Z 2025-07-17T09:05:59.4835795Z ``TORCH_XPU_ARCH_LIST="pvc,xe-lpg" python build_my_extension.py`` 2025-07-17T09:05:59.4835850Z 2025-07-17T09:05:59.4836022Z Note that while it's possible to include all supported archs, the more archs get included the 2025-07-17T09:05:59.4836201Z slower the building process will be, as it will build a separate kernel image for each arch. 2025-07-17T09:05:59.4836255Z 2025-07-17T09:05:59.4836347Z Note: Ninja is required to build SyclExtension. 2025-07-17T09:05:59.4836400Z 2025-07-17T09:05:59.4836551Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4836606Z 2025-07-17T09:05:59.4836678Z warnings.warn(msg) 2025-07-17T09:05:59.4836731Z 2025-07-17T09:05:59.4836854Z --- Parse Warning: 38 / 136 --- 2025-07-17T09:05:59.4837332Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1585. 2025-07-17T09:05:59.4837488Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4837550Z 2025-07-17T09:05:59.4837646Z Load a PyTorch C++ extension just-in-time (JIT). 2025-07-17T09:05:59.4837708Z 2025-07-17T09:05:59.4837832Z To load an extension, a Ninja build file is emitted, which is used to 2025-07-17T09:05:59.4837960Z compile the given sources into a dynamic library. This library is 2025-07-17T09:05:59.4838090Z subsequently loaded into the current Python process as a module and 2025-07-17T09:05:59.4838172Z returned from this function, ready for use. 2025-07-17T09:05:59.4838227Z 2025-07-17T09:05:59.4838356Z By default, the directory to which the build file is emitted and the 2025-07-17T09:05:59.4838561Z resulting library compiled to is ``/torch_extensions/``, where 2025-07-17T09:05:59.4838736Z ```` is the temporary folder on the current platform and ```` 2025-07-17T09:05:59.4838862Z the name of the extension. This location can be overridden in two ways. 2025-07-17T09:05:59.4838991Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2025-07-17T09:05:59.4839346Z replaces ``/torch_extensions`` and all extensions will be compiled 2025-07-17T09:05:59.4839476Z into subfolders of this directory. Second, if the ``build_directory`` 2025-07-17T09:05:59.4839609Z argument to this function is supplied, it overrides the entire path, i.e. 2025-07-17T09:05:59.4839712Z the library will be compiled into that folder directly. 2025-07-17T09:05:59.4839763Z 2025-07-17T09:05:59.4839894Z To compile the sources, the default system compiler (``c++``) is used, 2025-07-17T09:05:59.4840036Z which can be overridden by setting the ``CXX`` environment variable. To pass 2025-07-17T09:05:59.4840177Z additional arguments to the compilation process, ``extra_cflags`` or 2025-07-17T09:05:59.4840310Z ``extra_ldflags`` can be provided. For example, to compile your extension 2025-07-17T09:05:59.4840440Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2025-07-17T09:05:59.4840541Z ``extra_cflags`` to pass further include directories. 2025-07-17T09:05:59.4840594Z 2025-07-17T09:05:59.4840733Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2025-07-17T09:05:59.4840841Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2025-07-17T09:05:59.4840985Z detected and compiled with nvcc rather than the C++ compiler. This includes 2025-07-17T09:05:59.4841106Z passing the CUDA lib64 directory as a library directory, and linking 2025-07-17T09:05:59.4841202Z ``cudart``. You can pass additional flags to nvcc via 2025-07-17T09:05:59.4841322Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2025-07-17T09:05:59.4841467Z heuristics for finding the CUDA install directory are used, which usually 2025-07-17T09:05:59.4841590Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2025-07-17T09:05:59.4841657Z safest option. 2025-07-17T09:05:59.4841710Z 2025-07-17T09:05:59.4841850Z SYCL support with mixed compilation is provided. Simply pass SYCL source 2025-07-17T09:05:59.4841966Z files (``.sycl``) along with other sources. Such files will be detected 2025-07-17T09:05:59.4842098Z and compiled with SYCL compiler (such as Intel DPC++ Compiler) rather 2025-07-17T09:05:59.4842220Z than the C++ compiler. You can pass additional flags to SYCL compiler 2025-07-17T09:05:59.4842334Z via ``extra_sycl_cflags``, just like with ``extra_cflags`` for C++. 2025-07-17T09:05:59.4842457Z SYCL compiler is expected to be found via system PATH environment 2025-07-17T09:05:59.4842524Z variable. 2025-07-17T09:05:59.4842577Z 2025-07-17T09:05:59.4842636Z Args: 2025-07-17T09:05:59.4842759Z name: The name of the extension to build. This MUST be the same as the 2025-07-17T09:05:59.4842838Z name of the pybind11 module! 2025-07-17T09:05:59.4842960Z sources: A list of relative or absolute paths to C++ source files. 2025-07-17T09:05:59.4843103Z extra_cflags: optional list of compiler flags to forward to the build. 2025-07-17T09:05:59.4843229Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2025-07-17T09:05:59.4843301Z when building CUDA sources. 2025-07-17T09:05:59.4843428Z extra_sycl_cflags: optional list of compiler flags to forward to SYCL 2025-07-17T09:05:59.4843505Z compiler when building SYCL sources. 2025-07-17T09:05:59.4843632Z extra_ldflags: optional list of linker flags to forward to the build. 2025-07-17T09:05:59.4843765Z extra_include_paths: optional list of include directories to forward 2025-07-17T09:05:59.4843945Z to the build. 2025-07-17T09:05:59.4844056Z build_directory: optional path to use as build workspace. 2025-07-17T09:05:59.4844168Z verbose: If ``True``, turns on verbose logging of load steps. 2025-07-17T09:05:59.4844294Z with_cuda: Determines whether CUDA headers and libraries are added to 2025-07-17T09:05:59.4844504Z the build. If set to ``None`` (default), this value is 2025-07-17T09:05:59.4844624Z automatically determined based on the existence of ``.cu`` or 2025-07-17T09:05:59.4844735Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2025-07-17T09:05:59.4844806Z and libraries to be included. 2025-07-17T09:05:59.4844939Z with_sycl: Determines whether SYCL headers and libraries are added to 2025-07-17T09:05:59.4845041Z the build. If set to ``None`` (default), this value is 2025-07-17T09:05:59.4845168Z automatically determined based on the existence of ``.sycl`` in 2025-07-17T09:05:59.4845274Z ``sources``. Set it to `True`` to force SYCL headers and 2025-07-17T09:05:59.4845344Z libraries to be included. 2025-07-17T09:05:59.4845464Z is_python_module: If ``True`` (default), imports the produced shared 2025-07-17T09:05:59.4845579Z library as a Python module. If ``False``, behavior depends on 2025-07-17T09:05:59.4845651Z ``is_standalone``. 2025-07-17T09:05:59.4845775Z is_standalone: If ``False`` (default) loads the constructed extension 2025-07-17T09:05:59.4845890Z into the process as a plain dynamic library. If ``True``, build a 2025-07-17T09:05:59.4845963Z standalone executable. 2025-07-17T09:05:59.4846015Z 2025-07-17T09:05:59.4846070Z Returns: 2025-07-17T09:05:59.4846148Z If ``is_python_module`` is ``True``: 2025-07-17T09:05:59.4846253Z Returns the loaded PyTorch extension as a Python module. 2025-07-17T09:05:59.4846313Z 2025-07-17T09:05:59.4846438Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2025-07-17T09:05:59.4846578Z Returns nothing. (The shared library is loaded into the process as 2025-07-17T09:05:59.4846644Z a side effect.) 2025-07-17T09:05:59.4846706Z 2025-07-17T09:05:59.4846776Z If ``is_standalone`` is ``True``. 2025-07-17T09:05:59.4846902Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2025-07-17T09:05:59.4847010Z added to the PATH environment variable as a side effect.) 2025-07-17T09:05:59.4847067Z 2025-07-17T09:05:59.4847123Z Example: 2025-07-17T09:05:59.4847189Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.4847277Z >>> from torch.utils.cpp_extension import load 2025-07-17T09:05:59.4847344Z >>> module = load( 2025-07-17T09:05:59.4847411Z ... name='extension', 2025-07-17T09:05:59.4847517Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-07-17T09:05:59.4847590Z ... extra_cflags=['-O2'], 2025-07-17T09:05:59.4847652Z ... verbose=True) 2025-07-17T09:05:59.4847710Z 2025-07-17T09:05:59.4847858Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4847913Z 2025-07-17T09:05:59.4847977Z warnings.warn(msg) 2025-07-17T09:05:59.4848031Z 2025-07-17T09:05:59.4848163Z --- Parse Warning: 39 / 136 --- 2025-07-17T09:05:59.4848667Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_inline in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1890. 2025-07-17T09:05:59.4848826Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4848883Z 2025-07-17T09:05:59.4849009Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2025-07-17T09:05:59.4849131Z 2025-07-17T09:05:59.4849266Z This function behaves exactly like :func:`load`, but takes its sources as 2025-07-17T09:05:59.4849475Z strings rather than filenames. These strings are stored to files in the 2025-07-17T09:05:59.4849603Z build directory, after which the behavior of :func:`load_inline` is 2025-07-17T09:05:59.4849678Z identical to :func:`load`. 2025-07-17T09:05:59.4849735Z 2025-07-17T09:05:59.4849800Z See `the 2025-07-17T09:05:59.4850100Z tests `_ 2025-07-17T09:05:59.4850187Z for good examples of using this function. 2025-07-17T09:05:59.4850245Z 2025-07-17T09:05:59.4850384Z Sources may omit two required parts of a typical non-inline C++ extension: 2025-07-17T09:05:59.4850529Z the necessary header includes, as well as the (pybind11) binding code. More 2025-07-17T09:05:59.4850665Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2025-07-17T09:05:59.4850789Z single ``.cpp`` file. This file is then prepended with ``#include 2025-07-17T09:05:59.4850854Z `` 2025-07-17T09:05:59.4850914Z 2025-07-17T09:05:59.4851050Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2025-07-17T09:05:59.4851198Z automatically generated for each function specified. ``functions`` can 2025-07-17T09:05:59.4851330Z either be a list of function names, or a dictionary mapping from function 2025-07-17T09:05:59.4851469Z names to docstrings. If a list is given, the name of each function is used 2025-07-17T09:05:59.4851531Z as its docstring. 2025-07-17T09:05:59.4851591Z 2025-07-17T09:05:59.4851719Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2025-07-17T09:05:59.4851837Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2025-07-17T09:05:59.4851960Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2025-07-17T09:05:59.4852097Z separately, but ultimately linked into a single library. Note that no 2025-07-17T09:05:59.4852232Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2025-07-17T09:05:59.4852368Z to a CUDA kernel, you must create a C++ function that calls it, and either 2025-07-17T09:05:59.4852493Z declare or define this C++ function in one of the ``cpp_sources`` (and 2025-07-17T09:05:59.4852576Z include its name in ``functions``). 2025-07-17T09:05:59.4852632Z 2025-07-17T09:05:59.4852758Z The sources in ``sycl_sources`` are concatenated into a separate ``.sycl`` 2025-07-17T09:05:59.4852892Z file and prepended with ``torch/types.h``, ``sycl/sycl.hpp`` includes. 2025-07-17T09:05:59.4853006Z The ``.cpp`` and ``.sycl`` files are compiled separately, but ultimately 2025-07-17T09:05:59.4853133Z linked into a single library. Note that no bindings are generated for 2025-07-17T09:05:59.4853260Z functions in ``sycl_sources`` per se. To bind to a SYCL kernel, you must 2025-07-17T09:05:59.4853393Z create a C++ function that calls it, and either declare or define this 2025-07-17T09:05:59.4853512Z C++ function in one of the ``cpp_sources`` (and include its name 2025-07-17T09:05:59.4853577Z in ``functions``). 2025-07-17T09:05:59.4853629Z 2025-07-17T09:05:59.4853684Z 2025-07-17T09:05:59.4853738Z 2025-07-17T09:05:59.4853865Z See :func:`load` for a description of arguments omitted below. 2025-07-17T09:05:59.4853922Z 2025-07-17T09:05:59.4853985Z Args: 2025-07-17T09:05:59.4854116Z cpp_sources: A string, or list of strings, containing C++ source code. 2025-07-17T09:05:59.4854258Z cuda_sources: A string, or list of strings, containing CUDA source code. 2025-07-17T09:05:59.4854384Z sycl_sources: A string, or list of strings, containing SYCL source code. 2025-07-17T09:05:59.4854514Z functions: A list of function names for which to generate function 2025-07-17T09:05:59.4854639Z bindings. If a dictionary is given, it should map function names to 2025-07-17T09:05:59.4854866Z docstrings (which are otherwise just the function names). 2025-07-17T09:05:59.4854998Z with_cuda: Determines whether CUDA headers and libraries are added to 2025-07-17T09:05:59.4855105Z the build. If set to ``None`` (default), this value is 2025-07-17T09:05:59.4855226Z automatically determined based on whether ``cuda_sources`` is 2025-07-17T09:05:59.4855427Z provided. Set it to ``True`` to force CUDA headers 2025-07-17T09:05:59.4855503Z and libraries to be included. 2025-07-17T09:05:59.4855628Z with_sycl: Determines whether SYCL headers and libraries are added to 2025-07-17T09:05:59.4855728Z the build. If set to ``None`` (default), this value is 2025-07-17T09:05:59.4855845Z automatically determined based on whether ``sycl_sources`` is 2025-07-17T09:05:59.4855940Z provided. Set it to ``True`` to force SYCL headers 2025-07-17T09:05:59.4856010Z and libraries to be included. 2025-07-17T09:05:59.4856139Z with_pytorch_error_handling: Determines whether pytorch error and 2025-07-17T09:05:59.4856257Z warning macros are handled by pytorch instead of pybind. To do 2025-07-17T09:05:59.4856396Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2025-07-17T09:05:59.4856519Z function. This redirection might cause issues in obscure cases 2025-07-17T09:05:59.4856632Z of cpp. This flag should be set to ``False`` when this redirect 2025-07-17T09:05:59.4856695Z causes issues. 2025-07-17T09:05:59.4856850Z no_implicit_headers: If ``True``, skips automatically adding headers, most notably 2025-07-17T09:05:59.4856990Z ``#include `` and ``#include `` lines. 2025-07-17T09:05:59.4857094Z Use this option to improve cold start times when you 2025-07-17T09:05:59.4857243Z already include the necessary headers in your source code. Default: ``False``. 2025-07-17T09:05:59.4857304Z 2025-07-17T09:05:59.4857364Z Example: 2025-07-17T09:05:59.4857465Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-07-17T09:05:59.4857566Z >>> from torch.utils.cpp_extension import load_inline 2025-07-17T09:05:59.4857636Z >>> source = """ 2025-07-17T09:05:59.4857736Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2025-07-17T09:05:59.4857819Z return x.sin() + y.sin(); 2025-07-17T09:05:59.4857877Z } 2025-07-17T09:05:59.4857937Z """ 2025-07-17T09:05:59.4858032Z >>> module = load_inline(name='inline_extension', 2025-07-17T09:05:59.4858113Z ... cpp_sources=[source], 2025-07-17T09:05:59.4858198Z ... functions=['sin_add']) 2025-07-17T09:05:59.4858255Z 2025-07-17T09:05:59.4858320Z .. note:: 2025-07-17T09:05:59.4858464Z Since load_inline will just-in-time compile the source code, please ensure 2025-07-17T09:05:59.4858612Z that you have the right toolchains installed in the runtime. For example, 2025-07-17T09:05:59.4858745Z when loading C++, make sure a C++ compiler is available. If you're loading 2025-07-17T09:05:59.4858896Z a CUDA extension, you will need to additionally install the corresponding CUDA 2025-07-17T09:05:59.4859044Z toolkit (nvcc and any other dependencies your code has). Compiling toolchains 2025-07-17T09:05:59.4859192Z are not included when you install torch and must be additionally installed. 2025-07-17T09:05:59.4859248Z 2025-07-17T09:05:59.4859403Z During compiling, by default, the Ninja backend uses #CPUS + 2 workers to build 2025-07-17T09:05:59.4859534Z the extension. This may use up too many resources on some systems. One 2025-07-17T09:05:59.4859673Z can control the number of workers by setting the `MAX_JOBS` environment 2025-07-17T09:05:59.4859752Z variable to a non-negative number. 2025-07-17T09:05:59.4859872Z 2025-07-17T09:05:59.4860071Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4860128Z 2025-07-17T09:05:59.4860193Z warnings.warn(msg) 2025-07-17T09:05:59.4860256Z 2025-07-17T09:05:59.4860376Z --- Parse Warning: 40 / 136 --- 2025-07-17T09:05:59.4861012Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1218. 2025-07-17T09:05:59.4861181Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4861244Z 2025-07-17T09:05:59.4861375Z Context passed to policy function during selective checkpointing. 2025-07-17T09:05:59.4861429Z 2025-07-17T09:05:59.4861574Z This class is used to pass relevant metadata to the policy function during 2025-07-17T09:05:59.4861729Z selective checkpointing. The metadata includes whether the current invocation 2025-07-17T09:05:59.4861845Z of the policy function is during recomputation or not. 2025-07-17T09:05:59.4861902Z 2025-07-17T09:05:59.4861970Z Example: 2025-07-17T09:05:59.4862050Z >>> # xdoctest: +SKIP(stub) 2025-07-17T09:05:59.4862112Z >>> 2025-07-17T09:05:59.4862201Z >>> def policy_fn(ctx, op, *args, **kwargs): 2025-07-17T09:05:59.4862280Z >>> print(ctx.is_recompute) 2025-07-17T09:05:59.4862336Z >>> 2025-07-17T09:05:59.4862503Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2025-07-17T09:05:59.4862557Z >>> 2025-07-17T09:05:59.4862651Z >>> out = torch.utils.checkpoint.checkpoint( 2025-07-17T09:05:59.4862715Z >>> fn, x, y, 2025-07-17T09:05:59.4862784Z >>> use_reentrant=False, 2025-07-17T09:05:59.4862868Z >>> context_fn=context_fn, 2025-07-17T09:05:59.4862929Z >>> ) 2025-07-17T09:05:59.4862990Z 2025-07-17T09:05:59.4863144Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4863199Z 2025-07-17T09:05:59.4863265Z warnings.warn(msg) 2025-07-17T09:05:59.4863326Z 2025-07-17T09:05:59.4863446Z --- Parse Warning: 41 / 136 --- 2025-07-17T09:05:59.4863990Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=create_selective_checkpoint_contexts in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1358. 2025-07-17T09:05:59.4864148Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4864211Z 2025-07-17T09:05:59.4864352Z Helper to avoid recomputing certain ops during activation checkpointing. 2025-07-17T09:05:59.4864407Z 2025-07-17T09:05:59.4864538Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2025-07-17T09:05:59.4864649Z operations are recomputed during the backward pass. 2025-07-17T09:05:59.4864703Z 2025-07-17T09:05:59.4864759Z Args: 2025-07-17T09:05:59.4864837Z policy_fn_or_list (Callable or List): 2025-07-17T09:05:59.4864942Z - If a policy function is provided, it should accept a 2025-07-17T09:05:59.4865093Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2025-07-17T09:05:59.4865239Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2025-07-17T09:05:59.4865488Z indicating whether the execution of the op should be recomputed or not. 2025-07-17T09:05:59.4865610Z - If a list of operations is provided, it is equivalent to a policy 2025-07-17T09:05:59.4865733Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2025-07-17T09:05:59.4865863Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2025-07-17T09:05:59.4865929Z operations. 2025-07-17T09:05:59.4866129Z allow_cache_entry_mutation (bool, optional): By default, an error is 2025-07-17T09:05:59.4866315Z raised if any tensors cached by selective activation checkpoint are 2025-07-17T09:05:59.4866439Z mutated in order to ensure correctness. If set to `True`, this check 2025-07-17T09:05:59.4866510Z is disabled. 2025-07-17T09:05:59.4866570Z Returns: 2025-07-17T09:05:59.4866780Z A tuple of two context managers. 2025-07-17T09:05:59.4866838Z 2025-07-17T09:05:59.4866903Z Example: 2025-07-17T09:05:59.4866974Z >>> # xdoctest: +REQUIRES(LINUX) 2025-07-17T09:05:59.4867051Z >>> import functools 2025-07-17T09:05:59.4867106Z >>> 2025-07-17T09:05:59.4867189Z >>> x = torch.rand(10, 10, requires_grad=True) 2025-07-17T09:05:59.4867263Z >>> y = torch.rand(10, 10, requires_grad=True) 2025-07-17T09:05:59.4867316Z >>> 2025-07-17T09:05:59.4867381Z >>> ops_to_save = [ 2025-07-17T09:05:59.4867455Z >>> torch.ops.aten.mm.default, 2025-07-17T09:05:59.4867518Z >>> ] 2025-07-17T09:05:59.4867572Z >>> 2025-07-17T09:05:59.4867659Z >>> def policy_fn(ctx, op, *args, **kwargs): 2025-07-17T09:05:59.4867727Z >>> if op in ops_to_save: 2025-07-17T09:05:59.4867806Z >>> return CheckpointPolicy.MUST_SAVE 2025-07-17T09:05:59.4867863Z >>> else: 2025-07-17T09:05:59.4867963Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2025-07-17T09:05:59.4868017Z >>> 2025-07-17T09:05:59.4868187Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2025-07-17T09:05:59.4868264Z >>> 2025-07-17T09:05:59.4868335Z >>> # or equivalently 2025-07-17T09:05:59.4868494Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2025-07-17T09:05:59.4868553Z >>> 2025-07-17T09:05:59.4868617Z >>> def fn(x, y): 2025-07-17T09:05:59.4868738Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2025-07-17T09:05:59.4868801Z >>> 2025-07-17T09:05:59.4868887Z >>> out = torch.utils.checkpoint.checkpoint( 2025-07-17T09:05:59.4868947Z >>> fn, x, y, 2025-07-17T09:05:59.4869012Z >>> use_reentrant=False, 2025-07-17T09:05:59.4869089Z >>> context_fn=context_fn, 2025-07-17T09:05:59.4869146Z >>> ) 2025-07-17T09:05:59.4869204Z 2025-07-17T09:05:59.4869350Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4869407Z 2025-07-17T09:05:59.4869469Z warnings.warn(msg) 2025-07-17T09:05:59.4869528Z 2025-07-17T09:05:59.4869654Z --- Parse Warning: 42 / 136 --- 2025-07-17T09:05:59.4870154Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_pytree_node in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py line=134. 2025-07-17T09:05:59.4870307Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4870402Z Register a container-like type as pytree node. 2025-07-17T09:05:59.4870454Z 2025-07-17T09:05:59.4870509Z Args: 2025-07-17T09:05:59.4870622Z cls (type): A Python type to treat as an internal pytree node. 2025-07-17T09:05:59.4870788Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2025-07-17T09:05:59.4870942Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2025-07-17T09:05:59.4871111Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2025-07-17T09:05:59.4871191Z passed to the ``unflatten_fn``. 2025-07-17T09:05:59.4871348Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2025-07-17T09:05:59.4871508Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2025-07-17T09:05:59.4871741Z The function should return an instance of ``cls``. 2025-07-17T09:05:59.4871904Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2025-07-17T09:05:59.4872009Z qualified name used when serializing the tree spec. 2025-07-17T09:05:59.4872300Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2025-07-17T09:05:59.4872471Z to convert the context of the pytree to a custom json dumpable representation. This is 2025-07-17T09:05:59.4872659Z used for json serialization, which is being used in :mod:`torch.export` right now. 2025-07-17T09:05:59.4872836Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2025-07-17T09:05:59.4872996Z how to convert the custom json dumpable representation of the context back to the 2025-07-17T09:05:59.4873150Z original context. This is used for json deserialization, which is being used in 2025-07-17T09:05:59.4873230Z :mod:`torch.export` right now. 2025-07-17T09:05:59.4873287Z 2025-07-17T09:05:59.4873358Z Example:: 2025-07-17T09:05:59.4873417Z 2025-07-17T09:05:59.4873494Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.4873592Z >>> # Registry a Python type with lambda functions 2025-07-17T09:05:59.4873674Z >>> register_pytree_node( 2025-07-17T09:05:59.4873739Z ... set, 2025-07-17T09:05:59.4873824Z ... lambda s: (sorted(s), None, None), 2025-07-17T09:05:59.4873905Z ... lambda children, _: set(children), 2025-07-17T09:05:59.4873969Z ... ) 2025-07-17T09:05:59.4874027Z 2025-07-17T09:05:59.4874173Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4874239Z 2025-07-17T09:05:59.4874306Z warnings.warn(msg) 2025-07-17T09:05:59.4874371Z 2025-07-17T09:05:59.4874499Z --- Parse Warning: 43 / 136 --- 2025-07-17T09:05:59.4875021Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedSampler in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/distributed.py line=18. 2025-07-17T09:05:59.4875179Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4875304Z Sampler that restricts data loading to a subset of the dataset. 2025-07-17T09:05:59.4875356Z 2025-07-17T09:05:59.4875450Z It is especially useful in conjunction with 2025-07-17T09:05:59.4875600Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2025-07-17T09:05:59.4875778Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2025-07-17T09:05:59.4875912Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2025-07-17T09:05:59.4876009Z original dataset that is exclusive to it. 2025-07-17T09:05:59.4876064Z 2025-07-17T09:05:59.4876132Z .. note:: 2025-07-17T09:05:59.4876272Z Dataset is assumed to be of constant size and that any instance of it always 2025-07-17T09:05:59.4876371Z returns the same elements in the same order. 2025-07-17T09:05:59.4876427Z 2025-07-17T09:05:59.4876487Z Args: 2025-07-17T09:05:59.4876561Z dataset: Dataset used for sampling. 2025-07-17T09:05:59.4876696Z num_replicas (int, optional): Number of processes participating in 2025-07-17T09:05:59.4876844Z distributed training. By default, :attr:`world_size` is retrieved from the 2025-07-17T09:05:59.4876923Z current distributed group. 2025-07-17T09:05:59.4877069Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2025-07-17T09:05:59.4877254Z By default, :attr:`rank` is retrieved from the current distributed 2025-07-17T09:05:59.4877380Z group. 2025-07-17T09:05:59.4877517Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2025-07-17T09:05:59.4877580Z indices. 2025-07-17T09:05:59.4877697Z seed (int, optional): random seed used to shuffle the sampler if 2025-07-17T09:05:59.4877923Z :attr:`shuffle=True`. This number should be identical across all 2025-07-17T09:05:59.4878025Z processes in the distributed group. Default: ``0``. 2025-07-17T09:05:59.4878157Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2025-07-17T09:05:59.4878273Z tail of the data to make it evenly divisible across the number of 2025-07-17T09:05:59.4878391Z replicas. If ``False``, the sampler will add extra indices to make 2025-07-17T09:05:59.4878512Z the data evenly divisible across the replicas. Default: ``False``. 2025-07-17T09:05:59.4878576Z 2025-07-17T09:05:59.4878641Z .. warning:: 2025-07-17T09:05:59.4878768Z In distributed mode, calling the :meth:`set_epoch` method at 2025-07-17T09:05:59.4878919Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2025-07-17T09:05:59.4879082Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2025-07-17T09:05:59.4879165Z the same ordering will be always used. 2025-07-17T09:05:59.4879221Z 2025-07-17T09:05:59.4879278Z Example:: 2025-07-17T09:05:59.4879341Z 2025-07-17T09:05:59.4879411Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.4879541Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2025-07-17T09:05:59.4879663Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2025-07-17T09:05:59.4879746Z ... sampler=sampler) 2025-07-17T09:05:59.4879844Z >>> for epoch in range(start_epoch, n_epochs): 2025-07-17T09:05:59.4879915Z ... if is_distributed: 2025-07-17T09:05:59.4879998Z ... sampler.set_epoch(epoch) 2025-07-17T09:05:59.4880059Z ... train(loader) 2025-07-17T09:05:59.4880120Z 2025-07-17T09:05:59.4880265Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4880335Z 2025-07-17T09:05:59.4880399Z warnings.warn(msg) 2025-07-17T09:05:59.4880457Z 2025-07-17T09:05:59.4880574Z --- Parse Warning: 44 / 136 --- 2025-07-17T09:05:59.4881036Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=calculate_gain in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py line=142. 2025-07-17T09:05:59.4881186Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4881325Z Return the recommended gain value for the given nonlinearity function. 2025-07-17T09:05:59.4881383Z 2025-07-17T09:05:59.4881456Z The values are as follows: 2025-07-17T09:05:59.4881508Z 2025-07-17T09:05:59.4881590Z ================= ==================================================== 2025-07-17T09:05:59.4881660Z nonlinearity gain 2025-07-17T09:05:59.4881739Z ================= ==================================================== 2025-07-17T09:05:59.4881807Z Linear / Identity :math:`1` 2025-07-17T09:05:59.4881869Z Conv{1,2,3}D :math:`1` 2025-07-17T09:05:59.4881941Z Sigmoid :math:`1` 2025-07-17T09:05:59.4895055Z Tanh :math:`\frac{5}{3}` 2025-07-17T09:05:59.4895176Z ReLU :math:`\sqrt{2}` 2025-07-17T09:05:59.4895307Z Leaky Relu :math:`\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}` 2025-07-17T09:05:59.4895389Z SELU :math:`\frac{3}{4}` 2025-07-17T09:05:59.4895584Z ================= ==================================================== 2025-07-17T09:05:59.4895701Z 2025-07-17T09:05:59.4895768Z .. warning:: 2025-07-17T09:05:59.4895900Z In order to implement `Self-Normalizing Neural Networks`_ , 2025-07-17T09:05:59.4896055Z you should use ``nonlinearity='linear'`` instead of ``nonlinearity='selu'``. 2025-07-17T09:05:59.4896288Z This gives the initial weights a variance of ``1 / N``, 2025-07-17T09:05:59.4896429Z which is necessary to induce a stable fixed point in the forward pass. 2025-07-17T09:05:59.4896575Z In contrast, the default gain for ``SELU`` sacrifices the normalization 2025-07-17T09:05:59.4896692Z effect for more stable gradient flow in rectangular layers. 2025-07-17T09:05:59.4896755Z 2025-07-17T09:05:59.4896814Z Args: 2025-07-17T09:05:59.4896940Z nonlinearity: the non-linear function (`nn.functional` name) 2025-07-17T09:05:59.4897057Z param: optional parameter for the non-linear function 2025-07-17T09:05:59.4897115Z 2025-07-17T09:05:59.4897179Z Examples: 2025-07-17T09:05:59.4897259Z >>> gain = nn.init.calculate_gain( 2025-07-17T09:05:59.4897332Z ... "leaky_relu", 0.2 2025-07-17T09:05:59.4897419Z ... ) # leaky_relu with negative_slope=0.2 2025-07-17T09:05:59.4897480Z 2025-07-17T09:05:59.4897774Z .. _Self-Normalizing Neural Networks: https://papers.nips.cc/paper/2017/hash/5d44ee6f2c3f71b73125876103c8f6c4-Abstract.html 2025-07-17T09:05:59.4897837Z 2025-07-17T09:05:59.4897990Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4898050Z 2025-07-17T09:05:59.4898120Z warnings.warn(msg) 2025-07-17T09:05:59.4898176Z 2025-07-17T09:05:59.4898329Z --- Parse Warning: 45 / 136 --- 2025-07-17T09:05:59.4898897Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=convert_conv2d_weight_memory_format in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/memory_format.py line=14. 2025-07-17T09:05:59.4899063Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4899202Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2025-07-17T09:05:59.4899259Z 2025-07-17T09:05:59.4899434Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2025-07-17T09:05:59.4899600Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2025-07-17T09:05:59.4899750Z This function is used to facilitate the computation to adopt NHWC kernels, which 2025-07-17T09:05:59.4899939Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2025-07-17T09:05:59.4899993Z 2025-07-17T09:05:59.4900061Z .. note:: 2025-07-17T09:05:59.4900205Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2025-07-17T09:05:59.4900346Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2025-07-17T09:05:59.4900478Z layer with 4d weight will be affected by ``model.to``, which does not 2025-07-17T09:05:59.4900632Z necessarily benefit from conversion to specified ``memory_format``. 2025-07-17T09:05:59.4900770Z One place we are confident in is that NHWC(channels_last) conversion for 2025-07-17T09:05:59.4900905Z convolution in cuDNN, as it is beneficial to run convolution in NHWC, 2025-07-17T09:05:59.4901028Z even in cases where we have to apply permutation to input tensors. 2025-07-17T09:05:59.4901089Z 2025-07-17T09:05:59.4901227Z Hence our strategy here is to convert only the weight of convolution to 2025-07-17T09:05:59.4901317Z channels_last. This ensures that; 2025-07-17T09:05:59.4901524Z 1. Fast convolution kernels will be used, the benefit of which could 2025-07-17T09:05:59.4901715Z outweigh overhead of permutation (if input is not in the same format). 2025-07-17T09:05:59.4901854Z 2. No unnecessary permutations are applied on layers that do not benefit 2025-07-17T09:05:59.4901936Z from memory_format conversion. 2025-07-17T09:05:59.4901988Z 2025-07-17T09:05:59.4902242Z The optimal case is that, layers between convolution layers are channels 2025-07-17T09:05:59.4902381Z last compatible. Input tensor would be permuted to channels last when it 2025-07-17T09:05:59.4902527Z encounters the first convolution layer and stay in that memory format. 2025-07-17T09:05:59.4902666Z Hence following convolutions will not need to permute its input tensor. 2025-07-17T09:05:59.4902723Z 2025-07-17T09:05:59.4902853Z In case where a channels last incompatible layer is between convolution 2025-07-17T09:05:59.4902983Z layers, we need to permute the input tensor back to contiguous format 2025-07-17T09:05:59.4903109Z for that layer. The input tensor will go through the remaining layers in 2025-07-17T09:05:59.4903241Z contiguous format and be permuted to channels last when it encounters 2025-07-17T09:05:59.4903364Z another convolution layer. There's no point in propagating that 2025-07-17T09:05:59.4903497Z permutation to an earlier layer, as most layers are quite agnostic to 2025-07-17T09:05:59.4903564Z ``memory_format``. 2025-07-17T09:05:59.4903617Z 2025-07-17T09:05:59.4903756Z This claim might change when PyTorch supports fusion of permutation, as 2025-07-17T09:05:59.4903886Z there might have been a better spot to fuse the permutation other than 2025-07-17T09:05:59.4903967Z immediately before a convolution. 2025-07-17T09:05:59.4904018Z 2025-07-17T09:05:59.4904083Z Args: 2025-07-17T09:05:59.4904207Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2025-07-17T09:05:59.4904285Z ``nn.Module`` 2025-07-17T09:05:59.4904384Z memory_format: user specified ``memory_format``, 2025-07-17T09:05:59.4904493Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2025-07-17T09:05:59.4904545Z 2025-07-17T09:05:59.4904609Z Returns: 2025-07-17T09:05:59.4904707Z The original module with updated ``nn.Conv2d`` 2025-07-17T09:05:59.4904769Z 2025-07-17T09:05:59.4904824Z Example: 2025-07-17T09:05:59.4904916Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-07-17T09:05:59.4905013Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2025-07-17T09:05:59.4905092Z >>> input = torch.randint( 2025-07-17T09:05:59.4905187Z ... 1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda" 2025-07-17T09:05:59.4905241Z ... ) 2025-07-17T09:05:59.4905389Z >>> model = nn.Sequential( 2025-07-17T09:05:59.4905469Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2025-07-17T09:05:59.4905545Z >>> # This is identical to: 2025-07-17T09:05:59.4905692Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2025-07-17T09:05:59.4905805Z >>> model = nn.utils.convert_conv2d_weight_memory_format( 2025-07-17T09:05:59.4905881Z ... model, torch.channels_last 2025-07-17T09:05:59.4905944Z ... ) 2025-07-17T09:05:59.4906011Z >>> out = model(input) 2025-07-17T09:05:59.4906069Z 2025-07-17T09:05:59.4906218Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4906275Z 2025-07-17T09:05:59.4906342Z warnings.warn(msg) 2025-07-17T09:05:59.4906400Z 2025-07-17T09:05:59.4906529Z --- Parse Warning: 46 / 136 --- 2025-07-17T09:05:59.4907090Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=convert_conv3d_weight_memory_format in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/memory_format.py line=93. 2025-07-17T09:05:59.4907388Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4907525Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2025-07-17T09:05:59.4907806Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2025-07-17T09:05:59.4907979Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2025-07-17T09:05:59.4908129Z This function is used to facilitate the computation to adopt NHWC kernels, which 2025-07-17T09:05:59.4908319Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2025-07-17T09:05:59.4908373Z 2025-07-17T09:05:59.4908441Z .. note:: 2025-07-17T09:05:59.4908584Z Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive 2025-07-17T09:05:59.4908718Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2025-07-17T09:05:59.4908849Z layer with 4d weight will be affected by ``model.to``, which does not 2025-07-17T09:05:59.4908988Z necessarily benefit from conversion to specified ``memory_format``. 2025-07-17T09:05:59.4909134Z One place we are confident in is that NDHWC(channels_last_3d) conversion for 2025-07-17T09:05:59.4909259Z convolution in cuDNN, as it is beneficial to run convolution in NDHWC, 2025-07-17T09:05:59.4909382Z even in cases where we have to apply permutation to input tensors. 2025-07-17T09:05:59.4909436Z 2025-07-17T09:05:59.4909569Z Hence our strategy here is to convert only the weight of convolution to 2025-07-17T09:05:59.4909646Z channels_last_3d. This ensures that; 2025-07-17T09:05:59.4909783Z 1. Fast convolution kernels will be used, the benefit of which could 2025-07-17T09:05:59.4909923Z outweigh overhead of permutation (if input is not in the same format). 2025-07-17T09:05:59.4910060Z 2. No unnecessary permutations are applied on layers that do not benefit 2025-07-17T09:05:59.4910141Z from memory_format conversion. 2025-07-17T09:05:59.4910203Z 2025-07-17T09:05:59.4910333Z The optimal case is that, layers between convolution layers are channels 2025-07-17T09:05:59.4910474Z last compatible. Input tensor would be permuted to channels last when it 2025-07-17T09:05:59.4910609Z encounters the first convolution layer and stay in that memory format. 2025-07-17T09:05:59.4910756Z Hence following convolutions will not need to permute its input tensor. 2025-07-17T09:05:59.4910809Z 2025-07-17T09:05:59.4910940Z In case where a channels last incompatible layer is between convolution 2025-07-17T09:05:59.4911061Z layers, we need to permute the input tensor back to contiguous format 2025-07-17T09:05:59.4911201Z for that layer. The input tensor will go through the remaining layers in 2025-07-17T09:05:59.4911328Z contiguous format and be permuted to channels last when it encounters 2025-07-17T09:05:59.4911455Z another convolution layer. There's no point in propagating that 2025-07-17T09:05:59.4911582Z permutation to an earlier layer, as most layers are quite agnostic to 2025-07-17T09:05:59.4911654Z ``memory_format``. 2025-07-17T09:05:59.4911707Z 2025-07-17T09:05:59.4911851Z This claim might change when PyTorch supports fusion of permutation, as 2025-07-17T09:05:59.4911980Z there might have been a better spot to fuse the permutation other than 2025-07-17T09:05:59.4912059Z immediately before a convolution. 2025-07-17T09:05:59.4912114Z 2025-07-17T09:05:59.4912172Z Args: 2025-07-17T09:05:59.4912376Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2025-07-17T09:05:59.4912498Z ``nn.Module`` 2025-07-17T09:05:59.4912595Z memory_format: user specified ``memory_format``, 2025-07-17T09:05:59.4912705Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2025-07-17T09:05:59.4912769Z 2025-07-17T09:05:59.4912829Z Returns: 2025-07-17T09:05:59.4913027Z The original module with updated ``nn.Conv3d`` 2025-07-17T09:05:59.4913085Z 2025-07-17T09:05:59.4913149Z Example: 2025-07-17T09:05:59.4913238Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-07-17T09:05:59.4913344Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2025-07-17T09:05:59.4913415Z >>> input = torch.randint( 2025-07-17T09:05:59.4913517Z ... 1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda" 2025-07-17T09:05:59.4913574Z ... ) 2025-07-17T09:05:59.4913649Z >>> model = nn.Sequential( 2025-07-17T09:05:59.4913727Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2025-07-17T09:05:59.4913795Z >>> # This is identical to: 2025-07-17T09:05:59.4913955Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2025-07-17T09:05:59.4914059Z >>> model = nn.utils.convert_conv3d_weight_memory_format( 2025-07-17T09:05:59.4914146Z ... model, torch.channels_last_3d 2025-07-17T09:05:59.4914200Z ... ) 2025-07-17T09:05:59.4914274Z >>> out = model(input) 2025-07-17T09:05:59.4914330Z 2025-07-17T09:05:59.4914481Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4914535Z 2025-07-17T09:05:59.4914612Z warnings.warn(msg) 2025-07-17T09:05:59.4914664Z 2025-07-17T09:05:59.4914789Z --- Parse Warning: 47 / 136 --- 2025-07-17T09:05:59.4915283Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=pad_packed_sequence in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py line=342. 2025-07-17T09:05:59.4915442Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4915537Z Pad a packed batch of variable length sequences. 2025-07-17T09:05:59.4915593Z 2025-07-17T09:05:59.4915709Z It is an inverse operation to :func:`pack_padded_sequence`. 2025-07-17T09:05:59.4915768Z 2025-07-17T09:05:59.4915937Z The returned Tensor's data will be of size ``T x B x *`` (if :attr:`batch_first` is ``False``) 2025-07-17T09:05:59.4916088Z or ``B x T x *`` (if :attr:`batch_first` is ``True``) , where ``T`` is the length of the longest 2025-07-17T09:05:59.4916160Z sequence and ``B`` is the batch size. 2025-07-17T09:05:59.4916211Z 2025-07-17T09:05:59.4916269Z Example: 2025-07-17T09:05:59.4916411Z >>> from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence 2025-07-17T09:05:59.4916512Z >>> seq = torch.tensor([[1, 2, 0], [3, 0, 0], [4, 5, 6]]) 2025-07-17T09:05:59.4916579Z >>> lens = [2, 1, 3] 2025-07-17T09:05:59.4916663Z >>> packed = pack_padded_sequence( 2025-07-17T09:05:59.4916753Z ... seq, lens, batch_first=True, enforce_sorted=False 2025-07-17T09:05:59.4916819Z ... ) 2025-07-17T09:05:59.4916885Z >>> packed 2025-07-17T09:05:59.4917033Z PackedSequence(data=tensor([4, 1, 3, 5, 2, 6]), batch_sizes=tensor([3, 2, 1]), 2025-07-17T09:05:59.4917168Z sorted_indices=tensor([2, 0, 1]), unsorted_indices=tensor([1, 2, 0])) 2025-07-17T09:05:59.4917323Z >>> seq_unpacked, lens_unpacked = pad_packed_sequence(packed, batch_first=True) 2025-07-17T09:05:59.4917391Z >>> seq_unpacked 2025-07-17T09:05:59.4917466Z tensor([[1, 2, 0], 2025-07-17T09:05:59.4917602Z [3, 0, 0], 2025-07-17T09:05:59.4917715Z [4, 5, 6]]) 2025-07-17T09:05:59.4917777Z >>> lens_unpacked 2025-07-17T09:05:59.4917848Z tensor([2, 1, 3]) 2025-07-17T09:05:59.4917901Z 2025-07-17T09:05:59.4917966Z .. note:: 2025-07-17T09:05:59.4918071Z :attr:`total_length` is useful to implement the 2025-07-17T09:05:59.4918306Z ``pack sequence -> recurrent network -> unpack sequence`` pattern in a 2025-07-17T09:05:59.4918450Z :class:`~torch.nn.Module` wrapped in :class:`~torch.nn.DataParallel`. 2025-07-17T09:05:59.4918591Z See :ref:`this FAQ section ` for 2025-07-17T09:05:59.4918659Z details. 2025-07-17T09:05:59.4918711Z 2025-07-17T09:05:59.4918778Z Args: 2025-07-17T09:05:59.4918860Z sequence (PackedSequence): batch to pad 2025-07-17T09:05:59.4919002Z batch_first (bool, optional): if ``True``, the output will be in ``B x T x *`` 2025-07-17T09:05:59.4919081Z format, ``T x B x *`` otherwise. 2025-07-17T09:05:59.4919206Z padding_value (float, optional): values for padded elements. 2025-07-17T09:05:59.4919345Z total_length (int, optional): if not ``None``, the output will be padded to 2025-07-17T09:05:59.4919489Z have length :attr:`total_length`. This method will throw :class:`ValueError` 2025-07-17T09:05:59.4919607Z if :attr:`total_length` is less than the max sequence length in 2025-07-17T09:05:59.4919674Z :attr:`sequence`. 2025-07-17T09:05:59.4919731Z 2025-07-17T09:05:59.4919795Z Returns: 2025-07-17T09:05:59.4919907Z Tuple of Tensor containing the padded sequence, and a Tensor 2025-07-17T09:05:59.4920035Z containing the list of lengths of each sequence in the batch. 2025-07-17T09:05:59.4920180Z Batch elements will be re-ordered as they were ordered originally when 2025-07-17T09:05:59.4920314Z the batch was passed to ``pack_padded_sequence`` or ``pack_sequence``. 2025-07-17T09:05:59.4920369Z 2025-07-17T09:05:59.4920515Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4920574Z 2025-07-17T09:05:59.4920639Z warnings.warn(msg) 2025-07-17T09:05:59.4920695Z 2025-07-17T09:05:59.4920825Z --- Parse Warning: 48 / 136 --- 2025-07-17T09:05:59.4921315Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ln_structured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=979. 2025-07-17T09:05:59.4921463Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4921651Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2025-07-17T09:05:59.4921705Z 2025-07-17T09:05:59.4921851Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2025-07-17T09:05:59.4921980Z by removing the specified ``amount`` of (currently unpruned) channels 2025-07-17T09:05:59.4922091Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2025-07-17T09:05:59.4922208Z Modifies module in place (and also return the modified module) 2025-07-17T09:05:59.4922271Z by: 2025-07-17T09:05:59.4922325Z 2025-07-17T09:05:59.4922452Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-07-17T09:05:59.4922583Z binary mask applied to the parameter ``name`` by the pruning method. 2025-07-17T09:05:59.4922715Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-07-17T09:05:59.4922840Z original (unpruned) parameter is stored in a new parameter named 2025-07-17T09:05:59.4922913Z ``name+'_orig'``. 2025-07-17T09:05:59.4922968Z 2025-07-17T09:05:59.4923097Z Args: 2025-07-17T09:05:59.4923209Z module (nn.Module): module containing the tensor to prune 2025-07-17T09:05:59.4923370Z name (str): parameter name within ``module`` on which pruning 2025-07-17T09:05:59.4923440Z will act. 2025-07-17T09:05:59.4923549Z amount (int or float): quantity of parameters to prune. 2025-07-17T09:05:59.4923660Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-07-17T09:05:59.4923898Z fraction of parameters to prune. If ``int``, it represents the 2025-07-17T09:05:59.4923995Z absolute number of parameters to prune. 2025-07-17T09:05:59.4924108Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2025-07-17T09:05:59.4924210Z entries for argument ``p`` in :func:`torch.norm`. 2025-07-17T09:05:59.4924342Z dim (int): index of the dim along which we define channels to prune. 2025-07-17T09:05:59.4924481Z importance_scores (torch.Tensor): tensor of importance scores (of same 2025-07-17T09:05:59.4924604Z shape as module parameter) used to compute mask for pruning. 2025-07-17T09:05:59.4924737Z The values in this tensor indicate the importance of the corresponding 2025-07-17T09:05:59.4924814Z elements in the parameter being pruned. 2025-07-17T09:05:59.4924955Z If unspecified or None, the module parameter will be used in its place. 2025-07-17T09:05:59.4925010Z 2025-07-17T09:05:59.4925070Z Returns: 2025-07-17T09:05:59.4925194Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-07-17T09:05:59.4925248Z 2025-07-17T09:05:59.4925306Z Examples: 2025-07-17T09:05:59.4925388Z >>> from torch.nn.utils import prune 2025-07-17T09:05:59.4925460Z >>> m = prune.ln_structured( 2025-07-17T09:05:59.4925582Z ... nn.Conv2d(5, 3, 2), "weight", amount=0.3, dim=1, n=float("-inf") 2025-07-17T09:05:59.4925641Z ... ) 2025-07-17T09:05:59.4925701Z 2025-07-17T09:05:59.4925850Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4925904Z 2025-07-17T09:05:59.4925974Z warnings.warn(msg) 2025-07-17T09:05:59.4926031Z 2025-07-17T09:05:59.4926162Z --- Parse Warning: 49 / 136 --- 2025-07-17T09:05:59.4926664Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=global_unstructured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=1026. 2025-07-17T09:05:59.4926818Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4926875Z 2025-07-17T09:05:59.4927131Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2025-07-17T09:05:59.4927186Z 2025-07-17T09:05:59.4927266Z Modifies modules in place by: 2025-07-17T09:05:59.4927319Z 2025-07-17T09:05:59.4927458Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-07-17T09:05:59.4927583Z binary mask applied to the parameter ``name`` by the pruning method. 2025-07-17T09:05:59.4927707Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-07-17T09:05:59.4927848Z original (unpruned) parameter is stored in a new parameter named 2025-07-17T09:05:59.4927909Z ``name+'_orig'``. 2025-07-17T09:05:59.4927967Z 2025-07-17T09:05:59.4928027Z Args: 2025-07-17T09:05:59.4928152Z parameters (Iterable of (module, name) tuples): parameters of 2025-07-17T09:05:59.4928267Z the model to prune in a global fashion, i.e. by aggregating all 2025-07-17T09:05:59.4928395Z weights prior to deciding which ones to prune. module must be of 2025-07-17T09:05:59.4928492Z type :class:`nn.Module`, and name must be a string. 2025-07-17T09:05:59.4928688Z pruning_method (function): a valid pruning function from this module, 2025-07-17T09:05:59.4928850Z or a custom one implemented by the user that satisfies the 2025-07-17T09:05:59.4928990Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2025-07-17T09:05:59.4929128Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2025-07-17T09:05:59.4929365Z the corresponding parameter's importance scores tensor. The tensor 2025-07-17T09:05:59.4929491Z should be the same shape as the parameter, and is used for computing 2025-07-17T09:05:59.4929558Z mask for pruning. 2025-07-17T09:05:59.4929684Z If unspecified or None, the parameter will be used in place of its 2025-07-17T09:05:59.4929750Z importance scores. 2025-07-17T09:05:59.4929836Z kwargs: other keyword arguments such as: 2025-07-17T09:05:59.4929949Z amount (int or float): quantity of parameters to prune across the 2025-07-17T09:05:59.4930022Z specified parameters. 2025-07-17T09:05:59.4930127Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-07-17T09:05:59.4930247Z fraction of parameters to prune. If ``int``, it represents the 2025-07-17T09:05:59.4930329Z absolute number of parameters to prune. 2025-07-17T09:05:59.4930386Z 2025-07-17T09:05:59.4930442Z Raises: 2025-07-17T09:05:59.4930550Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2025-07-17T09:05:59.4930604Z 2025-07-17T09:05:59.4930666Z Note: 2025-07-17T09:05:59.4930793Z Since global structured pruning doesn't make much sense unless the 2025-07-17T09:05:59.4930915Z norm is normalized by the size of the parameter, we now limit the 2025-07-17T09:05:59.4931008Z scope of global pruning to unstructured methods. 2025-07-17T09:05:59.4931066Z 2025-07-17T09:05:59.4931122Z Examples: 2025-07-17T09:05:59.4931202Z >>> from torch.nn.utils import prune 2025-07-17T09:05:59.4931289Z >>> from collections import OrderedDict 2025-07-17T09:05:59.4931358Z >>> net = nn.Sequential( 2025-07-17T09:05:59.4931422Z ... OrderedDict( 2025-07-17T09:05:59.4931479Z ... [ 2025-07-17T09:05:59.4931564Z ... ("first", nn.Linear(10, 4)), 2025-07-17T09:05:59.4931637Z ... ("second", nn.Linear(4, 1)), 2025-07-17T09:05:59.4931701Z ... ] 2025-07-17T09:05:59.4931758Z ... ) 2025-07-17T09:05:59.4931818Z ... ) 2025-07-17T09:05:59.4931889Z >>> parameters_to_prune = ( 2025-07-17T09:05:59.4931963Z ... (net.first, "weight"), 2025-07-17T09:05:59.4932030Z ... (net.second, "weight"), 2025-07-17T09:05:59.4932091Z ... ) 2025-07-17T09:05:59.4932164Z >>> prune.global_unstructured( 2025-07-17T09:05:59.4932234Z ... parameters_to_prune, 2025-07-17T09:05:59.4932318Z ... pruning_method=prune.L1Unstructured, 2025-07-17T09:05:59.4932381Z ... amount=10, 2025-07-17T09:05:59.4932444Z ... ) 2025-07-17T09:05:59.4932578Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2025-07-17T09:05:59.4932643Z tensor(10) 2025-07-17T09:05:59.4932695Z 2025-07-17T09:05:59.4932757Z 2025-07-17T09:05:59.4932902Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4932960Z 2025-07-17T09:05:59.4933026Z warnings.warn(msg) 2025-07-17T09:05:59.4933080Z 2025-07-17T09:05:59.4933201Z --- Parse Warning: 50 / 136 --- 2025-07-17T09:05:59.4933695Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=custom_from_mask in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=1149. 2025-07-17T09:05:59.4933847Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4934144Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2025-07-17T09:05:59.4934251Z 2025-07-17T09:05:59.4934377Z Modifies module in place (and also return the modified module) by: 2025-07-17T09:05:59.4934429Z 2025-07-17T09:05:59.4934552Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-07-17T09:05:59.4934791Z binary mask applied to the parameter ``name`` by the pruning method. 2025-07-17T09:05:59.4934917Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-07-17T09:05:59.4935038Z original (unpruned) parameter is stored in a new parameter named 2025-07-17T09:05:59.4935101Z ``name+'_orig'``. 2025-07-17T09:05:59.4935157Z 2025-07-17T09:05:59.4935220Z Args: 2025-07-17T09:05:59.4935326Z module (nn.Module): module containing the tensor to prune 2025-07-17T09:05:59.4935444Z name (str): parameter name within ``module`` on which pruning 2025-07-17T09:05:59.4935515Z will act. 2025-07-17T09:05:59.4935622Z mask (Tensor): binary mask to be applied to the parameter. 2025-07-17T09:05:59.4935673Z 2025-07-17T09:05:59.4935729Z Returns: 2025-07-17T09:05:59.4935855Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-07-17T09:05:59.4935907Z 2025-07-17T09:05:59.4935967Z Examples: 2025-07-17T09:05:59.4936042Z >>> from torch.nn.utils import prune 2025-07-17T09:05:59.4936118Z >>> m = prune.custom_from_mask( 2025-07-17T09:05:59.4936221Z ... nn.Linear(5, 3), name="bias", mask=torch.tensor([0, 1, 0]) 2025-07-17T09:05:59.4936285Z ... ) 2025-07-17T09:05:59.4936353Z >>> print(m.bias_mask) 2025-07-17T09:05:59.4936420Z tensor([0., 1., 0.]) 2025-07-17T09:05:59.4936471Z 2025-07-17T09:05:59.4936525Z 2025-07-17T09:05:59.4936668Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4936728Z 2025-07-17T09:05:59.4936789Z warnings.warn(msg) 2025-07-17T09:05:59.4936843Z 2025-07-17T09:05:59.4936961Z --- Parse Warning: 51 / 136 --- 2025-07-17T09:05:59.4937484Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_parametrization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrize.py line=424. 2025-07-17T09:05:59.4937633Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4937735Z Register a parametrization to a tensor in a module. 2025-07-17T09:05:59.4937787Z 2025-07-17T09:05:59.4937950Z Assume that ``tensor_name="weight"`` for simplicity. When accessing ``module.weight``, 2025-07-17T09:05:59.4938116Z the module will return the parametrized version ``parametrization(module.weight)``. 2025-07-17T09:05:59.4938276Z If the original tensor requires a gradient, the backward pass will differentiate 2025-07-17T09:05:59.4938443Z through :attr:`parametrization`, and the optimizer will update the tensor accordingly. 2025-07-17T09:05:59.4938496Z 2025-07-17T09:05:59.4938675Z The first time that a module registers a parametrization, this function will add an attribute 2025-07-17T09:05:59.4938820Z ``parametrizations`` to the module of type :class:`~ParametrizationList`. 2025-07-17T09:05:59.4938877Z 2025-07-17T09:05:59.4939028Z The list of parametrizations on the tensor ``weight`` will be accessible under 2025-07-17T09:05:59.4939112Z ``module.parametrizations.weight``. 2025-07-17T09:05:59.4939164Z 2025-07-17T09:05:59.4939251Z The original tensor will be accessible under 2025-07-17T09:05:59.4939340Z ``module.parametrizations.weight.original``. 2025-07-17T09:05:59.4939396Z 2025-07-17T09:05:59.4939550Z Parametrizations may be concatenated by registering several parametrizations 2025-07-17T09:05:59.4939858Z on the same attribute. 2025-07-17T09:05:59.4939913Z 2025-07-17T09:05:59.4940060Z The training mode of a registered parametrization is updated on registration 2025-07-17T09:05:59.4940149Z to match the training mode of the host module 2025-07-17T09:05:59.4940207Z 2025-07-17T09:05:59.4940493Z Parametrized parameters and buffers have an inbuilt caching system that can be activated 2025-07-17T09:05:59.4940578Z using the context manager :func:`cached`. 2025-07-17T09:05:59.4940630Z 2025-07-17T09:05:59.4940771Z A :attr:`parametrization` may optionally implement a method with signature 2025-07-17T09:05:59.4940825Z 2025-07-17T09:05:59.4940897Z .. code-block:: python 2025-07-17T09:05:59.4940957Z 2025-07-17T09:05:59.4941086Z def right_inverse(self, X: Tensor) -> Union[Tensor, Sequence[Tensor]] 2025-07-17T09:05:59.4941148Z 2025-07-17T09:05:59.4941304Z This method is called on the unparametrized tensor when the first parametrization 2025-07-17T09:05:59.4941430Z is registered to compute the initial value of the original tensor. 2025-07-17T09:05:59.4941607Z If this method is not implemented, the original tensor will be just the unparametrized tensor. 2025-07-17T09:05:59.4941664Z 2025-07-17T09:05:59.4941847Z If all the parametrizations registered on a tensor implement `right_inverse` it is possible 2025-07-17T09:05:59.4942020Z to initialize a parametrized tensor by assigning to it, as shown in the example below. 2025-07-17T09:05:59.4942075Z 2025-07-17T09:05:59.4942205Z It is possible for the first parametrization to depend on several inputs. 2025-07-17T09:05:59.4942348Z This may be implemented returning a tuple of tensors from ``right_inverse`` 2025-07-17T09:05:59.4942490Z (see the example implementation of a ``RankOne`` parametrization below). 2025-07-17T09:05:59.4942547Z 2025-07-17T09:05:59.4942751Z In this case, the unconstrained tensors are also located under ``module.parametrizations.weight`` 2025-07-17T09:05:59.4942836Z with names ``original0``, ``original1``,... 2025-07-17T09:05:59.4942890Z 2025-07-17T09:05:59.4942948Z .. note:: 2025-07-17T09:05:59.4943001Z 2025-07-17T09:05:59.4943159Z If unsafe=False (default) both the forward and right_inverse methods will be called 2025-07-17T09:05:59.4943257Z once to perform a number of consistency checks. 2025-07-17T09:05:59.4943416Z If unsafe=True, then right_inverse will be called if the tensor is not parametrized, 2025-07-17T09:05:59.4943494Z and nothing will be called otherwise. 2025-07-17T09:05:59.4943553Z 2025-07-17T09:05:59.4943612Z .. note:: 2025-07-17T09:05:59.4943667Z 2025-07-17T09:05:59.4943790Z In most situations, ``right_inverse`` will be a function such that 2025-07-17T09:05:59.4943873Z ``forward(right_inverse(X)) == X`` (see 2025-07-17T09:05:59.4944045Z `right inverse `_). 2025-07-17T09:05:59.4944204Z Sometimes, when the parametrization is not surjective, it may be reasonable 2025-07-17T09:05:59.4944268Z to relax this. 2025-07-17T09:05:59.4944323Z 2025-07-17T09:05:59.4944380Z .. warning:: 2025-07-17T09:05:59.4944437Z 2025-07-17T09:05:59.4944597Z If a parametrization depends on several inputs, :func:`~register_parametrization` 2025-07-17T09:05:59.4944749Z will register a number of new parameters. If such parametrization is registered 2025-07-17T09:05:59.4944905Z after the optimizer is created, these new parameters will need to be added manually 2025-07-17T09:05:59.4945027Z to the optimizer. See :meth:`torch.Optimizer.add_param_group`. 2025-07-17T09:05:59.4945082Z 2025-07-17T09:05:59.4945146Z Args: 2025-07-17T09:05:59.4945444Z module (nn.Module): module on which to register the parametrization 2025-07-17T09:05:59.4945668Z tensor_name (str): name of the parameter or buffer on which to register 2025-07-17T09:05:59.4945747Z the parametrization 2025-07-17T09:05:59.4945872Z parametrization (nn.Module): the parametrization to register 2025-07-17T09:05:59.4945941Z Keyword args: 2025-07-17T09:05:59.4946219Z unsafe (bool): a boolean flag that denotes whether the parametrization 2025-07-17T09:05:59.4946337Z may change the dtype and shape of the tensor. Default: `False` 2025-07-17T09:05:59.4946490Z Warning: the parametrization is not checked for consistency upon registration. 2025-07-17T09:05:59.4946570Z Enable this flag at your own risk. 2025-07-17T09:05:59.4946624Z 2025-07-17T09:05:59.4946684Z Raises: 2025-07-17T09:05:59.4946849Z ValueError: if the module does not have a parameter or a buffer named :attr:`tensor_name` 2025-07-17T09:05:59.4946906Z 2025-07-17T09:05:59.4946963Z Examples: 2025-07-17T09:05:59.4947059Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK) 2025-07-17T09:05:59.4947121Z >>> import torch 2025-07-17T09:05:59.4947192Z >>> import torch.nn as nn 2025-07-17T09:05:59.4947280Z >>> import torch.nn.utils.parametrize as P 2025-07-17T09:05:59.4947340Z >>> 2025-07-17T09:05:59.4947419Z >>> class Symmetric(nn.Module): 2025-07-17T09:05:59.4947491Z >>> def forward(self, X): 2025-07-17T09:05:59.4947601Z >>> return X.triu() + X.triu(1).T # Return a symmetric matrix 2025-07-17T09:05:59.4947657Z >>> 2025-07-17T09:05:59.4947735Z >>> def right_inverse(self, A): 2025-07-17T09:05:59.4947801Z >>> return A.triu() 2025-07-17T09:05:59.4947862Z >>> 2025-07-17T09:05:59.4947927Z >>> m = nn.Linear(5, 5) 2025-07-17T09:05:59.4948040Z >>> P.register_parametrization(m, "weight", Symmetric()) 2025-07-17T09:05:59.4948183Z >>> print(torch.allclose(m.weight, m.weight.T)) # m.weight is now symmetric 2025-07-17T09:05:59.4948242Z True 2025-07-17T09:05:59.4948306Z >>> A = torch.rand(5, 5) 2025-07-17T09:05:59.4948377Z >>> A = A + A.T # A is now symmetric 2025-07-17T09:05:59.4948499Z >>> m.weight = A # Initialize the weight to be the symmetric matrix A 2025-07-17T09:05:59.4948581Z >>> print(torch.allclose(m.weight, A)) 2025-07-17T09:05:59.4948636Z True 2025-07-17T09:05:59.4948690Z 2025-07-17T09:05:59.4948760Z >>> class RankOne(nn.Module): 2025-07-17T09:05:59.4948839Z >>> def forward(self, x, y): 2025-07-17T09:05:59.4948930Z >>> # Form a rank 1 matrix multiplying two vectors 2025-07-17T09:05:59.4949015Z >>> return x.unsqueeze(-1) @ y.unsqueeze(-2) 2025-07-17T09:05:59.4949073Z >>> 2025-07-17T09:05:59.4949143Z >>> def right_inverse(self, Z): 2025-07-17T09:05:59.4949225Z >>> # Project Z onto the rank 1 matrices 2025-07-17T09:05:59.4949318Z >>> U, S, Vh = torch.linalg.svd(Z, full_matrices=False) 2025-07-17T09:05:59.4949397Z >>> # Return rescaled singular vectors 2025-07-17T09:05:59.4949476Z >>> s0_sqrt = S[0].sqrt().unsqueeze(-1) 2025-07-17T09:05:59.4949574Z >>> return U[..., :, 0] * s0_sqrt, Vh[..., 0, :] * s0_sqrt 2025-07-17T09:05:59.4949629Z >>> 2025-07-17T09:05:59.4949731Z >>> linear_rank_one = P.register_parametrization( 2025-07-17T09:05:59.4949815Z ... nn.Linear(4, 4), "weight", RankOne() 2025-07-17T09:05:59.4949871Z ... ) 2025-07-17T09:05:59.4949989Z >>> print(torch.linalg.matrix_rank(linear_rank_one.weight).item()) 2025-07-17T09:05:59.4950044Z 1 2025-07-17T09:05:59.4950099Z 2025-07-17T09:05:59.4950291Z 2025-07-17T09:05:59.4950440Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4950559Z 2025-07-17T09:05:59.4950631Z warnings.warn(msg) 2025-07-17T09:05:59.4950683Z 2025-07-17T09:05:59.4950808Z --- Parse Warning: 52 / 136 --- 2025-07-17T09:05:59.4951484Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedDataParallel.join in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py line=1766. 2025-07-17T09:05:59.4951641Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4951694Z 2025-07-17T09:05:59.4951834Z Context manager for training with uneven inputs across processes in DDP. 2025-07-17T09:05:59.4951887Z 2025-07-17T09:05:59.4952022Z This context manager will keep track of already-joined DDP processes, 2025-07-17T09:05:59.4952147Z and "shadow" the forward and backward passes by inserting collective 2025-07-17T09:05:59.4952294Z communication operations to match with the ones created by non-joined 2025-07-17T09:05:59.4952428Z DDP processes. This will ensure each collective call has a corresponding 2025-07-17T09:05:59.4952559Z call by already-joined DDP processes, preventing hangs or errors that 2025-07-17T09:05:59.4952685Z would otherwise happen when training with uneven inputs across 2025-07-17T09:05:59.4952826Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2025-07-17T09:05:59.4952947Z specified to be ``True``, all trainers will throw an error once one rank 2025-07-17T09:05:59.4953069Z runs out of inputs, allowing these errors to be caught and handled 2025-07-17T09:05:59.4953140Z according to application logic. 2025-07-17T09:05:59.4953194Z 2025-07-17T09:05:59.4953322Z Once all DDP processes have joined, the context manager will broadcast 2025-07-17T09:05:59.4953464Z the model corresponding to the last joined process to all processes to 2025-07-17T09:05:59.4953565Z ensure the model is the same across all processes 2025-07-17T09:05:59.4953645Z (which is guaranteed by DDP). 2025-07-17T09:05:59.4953700Z 2025-07-17T09:05:59.4953819Z To use this to enable training with uneven inputs across processes, 2025-07-17T09:05:59.4953952Z simply wrap this context manager around your training loop. No further 2025-07-17T09:05:59.4954059Z modifications to the model or data loading is required. 2025-07-17T09:05:59.4954113Z 2025-07-17T09:05:59.4954178Z .. warning:: 2025-07-17T09:05:59.4954298Z If the model or training loop this context manager is wrapped around 2025-07-17T09:05:59.4954411Z has additional distributed collective operations, such as 2025-07-17T09:05:59.4954532Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2025-07-17T09:05:59.4954654Z ``throw_on_early_termination`` must be enabled. This is because this 2025-07-17T09:05:59.4954784Z context manager is not aware of non-DDP collective communication. 2025-07-17T09:05:59.4954890Z This flag will cause all ranks to throw when any one rank 2025-07-17T09:05:59.4955019Z exhausts inputs, allowing these errors to be caught and recovered 2025-07-17T09:05:59.4955090Z from across all ranks. 2025-07-17T09:05:59.4955150Z 2025-07-17T09:05:59.4955204Z Args: 2025-07-17T09:05:59.4955326Z divide_by_initial_world_size (bool): If ``True``, will divide 2025-07-17T09:05:59.4955448Z gradients by the initial ``world_size`` DDP training was launched 2025-07-17T09:05:59.4955551Z with. If ``False``, will compute the effective world size 2025-07-17T09:05:59.4955663Z (number of ranks that have not depleted their inputs yet) and 2025-07-17T09:05:59.4955755Z divide gradients by that during allreduce. Set 2025-07-17T09:05:59.4955862Z ``divide_by_initial_world_size=True`` to ensure every input 2025-07-17T09:05:59.4956083Z sample including the uneven inputs have equal weight in terms of 2025-07-17T09:05:59.4956237Z how much they contribute to the global gradient. This is 2025-07-17T09:05:59.4956353Z achieved by always dividing the gradient by the initial 2025-07-17T09:05:59.4956463Z ``world_size`` even when we encounter uneven inputs. If you set 2025-07-17T09:05:59.4956669Z this to ``False``, we divide the gradient by the remaining 2025-07-17T09:05:59.4956783Z number of nodes. This ensures parity with training on a smaller 2025-07-17T09:05:59.4956887Z ``world_size`` although it also means the uneven inputs would 2025-07-17T09:05:59.4957004Z contribute more towards the global gradient. Typically, you 2025-07-17T09:05:59.4957112Z would want to set this to ``True`` for cases where the last few 2025-07-17T09:05:59.4957238Z inputs of your training job are uneven. In extreme cases, where 2025-07-17T09:05:59.4957349Z there is a large discrepancy in the number of inputs, setting 2025-07-17T09:05:59.4957446Z this to ``False`` might provide better results. 2025-07-17T09:05:59.4957570Z enable (bool): Whether to enable uneven input detection or not. Pass 2025-07-17T09:05:59.4957678Z in ``enable=False`` to disable in cases where you know that 2025-07-17T09:05:59.4957793Z inputs are even across participating processes. Default is 2025-07-17T09:05:59.4957857Z ``True``. 2025-07-17T09:05:59.4957962Z throw_on_early_termination (bool): Whether to throw an error 2025-07-17T09:05:59.4958071Z or continue training when at least one rank has exhausted 2025-07-17T09:05:59.4958176Z inputs. If ``True``, will throw upon the first rank reaching end 2025-07-17T09:05:59.4958286Z of data. If ``False``, will continue training with a smaller 2025-07-17T09:05:59.4958398Z effective world size until all ranks are joined. Note that if 2025-07-17T09:05:59.4958482Z this flag is specified, then the flag 2025-07-17T09:05:59.4958581Z ``divide_by_initial_world_size`` would be ignored. Default 2025-07-17T09:05:59.4958644Z is ``False``. 2025-07-17T09:05:59.4958696Z 2025-07-17T09:05:59.4958755Z 2025-07-17T09:05:59.4958819Z Example:: 2025-07-17T09:05:59.4958882Z 2025-07-17T09:05:59.4958961Z >>> # xdoctest: +SKIP("Distributed") 2025-07-17T09:05:59.4959024Z >>> import torch 2025-07-17T09:05:59.4959102Z >>> import torch.distributed as dist 2025-07-17T09:05:59.4959162Z >>> import os 2025-07-17T09:05:59.4959244Z >>> import torch.multiprocessing as mp 2025-07-17T09:05:59.4959310Z >>> import torch.nn as nn 2025-07-17T09:05:59.4959382Z >>> # On each spawned worker 2025-07-17T09:05:59.4959446Z >>> def worker(rank): 2025-07-17T09:05:59.4959554Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2025-07-17T09:05:59.4959630Z >>> torch.cuda.set_device(rank) 2025-07-17T09:05:59.4959722Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2025-07-17T09:05:59.4959833Z >>> model = torch.nn.parallel.DistributedDataParallel( 2025-07-17T09:05:59.4959931Z >>> model, device_ids=[rank], output_device=rank 2025-07-17T09:05:59.4959986Z >>> ) 2025-07-17T09:05:59.4960074Z >>> # Rank 1 gets one more input than rank 0. 2025-07-17T09:05:59.4960184Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2025-07-17T09:05:59.4960252Z >>> with model.join(): 2025-07-17T09:05:59.4960318Z >>> for _ in range(5): 2025-07-17T09:05:59.4960391Z >>> for inp in inputs: 2025-07-17T09:05:59.4960464Z >>> loss = model(inp).sum() 2025-07-17T09:05:59.4960531Z >>> loss.backward() 2025-07-17T09:05:59.4960651Z >>> # Without the join() API, the below synchronization will hang 2025-07-17T09:05:59.4960867Z >>> # blocking for rank 1's allreduce to complete. 2025-07-17T09:05:59.4960953Z >>> torch.cuda.synchronize(device=rank) 2025-07-17T09:05:59.4961006Z 2025-07-17T09:05:59.4961154Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4961207Z 2025-07-17T09:05:59.4961275Z warnings.warn(msg) 2025-07-17T09:05:59.4961433Z 2025-07-17T09:05:59.4961561Z --- Parse Warning: 53 / 136 --- 2025-07-17T09:05:59.4962155Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedDataParallel._register_fused_optim in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py line=2057. 2025-07-17T09:05:59.4962312Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4962369Z 2025-07-17T09:05:59.4962557Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2025-07-17T09:05:59.4962610Z 2025-07-17T09:05:59.4962739Z Registers an optimizer with DDP such that the optimization for a 2025-07-17T09:05:59.4962858Z parameter will run immediately when that parameter's gradient is 2025-07-17T09:05:59.4962984Z finished with reduction, instead of waiting for all parameters' 2025-07-17T09:05:59.4963111Z gradients to finish reduction. This can result in a training speedup 2025-07-17T09:05:59.4963239Z depending on your workload since the optimizer can run while gradient 2025-07-17T09:05:59.4963369Z reduction for other parameters are still ongoing. In addition, this has 2025-07-17T09:05:59.4963505Z the potential to reduce peak memory consumption during training, as it 2025-07-17T09:05:59.4963624Z only needs to load the per-parameter optimizer states of a single 2025-07-17T09:05:59.4963756Z parameter at a time, instead of loading all per-parameter optimizer 2025-07-17T09:05:59.4963820Z states at once. 2025-07-17T09:05:59.4963877Z 2025-07-17T09:05:59.4963934Z Args: 2025-07-17T09:05:59.4964048Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2025-07-17T09:05:59.4964120Z as a fused optimizer. 2025-07-17T09:05:59.4964224Z *args (Sequence[Any]): Arguments to forward to `optim`. 2025-07-17T09:05:59.4964363Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2025-07-17T09:05:59.4964492Z to optimize, similar to `params` argument of traditional `torch.optim` 2025-07-17T09:05:59.4964611Z Optimizers. If this is omitted, all DDP model parameters will be 2025-07-17T09:05:59.4964668Z optimized. 2025-07-17T09:05:59.4964788Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2025-07-17T09:05:59.4964840Z 2025-07-17T09:05:59.4964908Z .. warning :: 2025-07-17T09:05:59.4965034Z _register_fused_optim should only be called once on a DDP instance, 2025-07-17T09:05:59.4965165Z and registering multiple fused optimizers for the same DDP model 2025-07-17T09:05:59.4965246Z is not currently supported. Please ping 2025-07-17T09:05:59.4965386Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-07-17T09:05:59.4965448Z for your use case. 2025-07-17T09:05:59.4965508Z 2025-07-17T09:05:59.4965568Z .. warning :: 2025-07-17T09:05:59.4965687Z _register_fused_optim and register_comm_hook currently do not 2025-07-17T09:05:59.4965820Z compose together, meaning that custom DDP communication hooks are 2025-07-17T09:05:59.4965926Z not supported with overlapped optimizers. Please ping 2025-07-17T09:05:59.4966060Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-07-17T09:05:59.4966122Z for your use case. 2025-07-17T09:05:59.4966185Z 2025-07-17T09:05:59.4966241Z .. warning :: 2025-07-17T09:05:59.4966461Z Gradient accumulation and DDP `no_sync` are currently not supported 2025-07-17T09:05:59.4966597Z with overlapped optimizer. Please ping 2025-07-17T09:05:59.4966723Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-07-17T09:05:59.4966782Z for your use case. 2025-07-17T09:05:59.4966838Z 2025-07-17T09:05:59.4966899Z Example:: 2025-07-17T09:05:59.4966954Z 2025-07-17T09:05:59.4967139Z >>> # xdoctest: +SKIP("No rendezvous handler") 2025-07-17T09:05:59.4967324Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2025-07-17T09:05:59.4967445Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2025-07-17T09:05:59.4967505Z >>> lr = 1e-2 2025-07-17T09:05:59.4967567Z >>> betas = (0.9, 0.99) 2025-07-17T09:05:59.4967628Z >>> eps = 1e-6 2025-07-17T09:05:59.4967766Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2025-07-17T09:05:59.4967847Z >>> # Example with subset of parameters 2025-07-17T09:05:59.4967940Z >>> params_to_opt = [list(net.parameters())[0]] 2025-07-17T09:05:59.4968012Z >>> net._register_fused_optim( 2025-07-17T09:05:59.4968157Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2025-07-17T09:05:59.4968211Z ... ) 2025-07-17T09:05:59.4968266Z 2025-07-17T09:05:59.4968418Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4968476Z 2025-07-17T09:05:59.4968540Z warnings.warn(msg) 2025-07-17T09:05:59.4968594Z 2025-07-17T09:05:59.4968714Z --- Parse Warning: 54 / 136 --- 2025-07-17T09:05:59.4969212Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SyncBatchNorm in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py line=601. 2025-07-17T09:05:59.4969364Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4969477Z Applies Batch Normalization over a N-Dimensional input. 2025-07-17T09:05:59.4969530Z 2025-07-17T09:05:59.4969739Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2025-07-17T09:05:59.4969870Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2025-07-17T09:05:59.4970002Z Internal Covariate Shift `__ . 2025-07-17T09:05:59.4970055Z 2025-07-17T09:05:59.4970114Z .. math:: 2025-07-17T09:05:59.4970170Z 2025-07-17T09:05:59.4970305Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2025-07-17T09:05:59.4970361Z 2025-07-17T09:05:59.4970491Z The mean and standard-deviation are calculated per-dimension over all 2025-07-17T09:05:59.4970635Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2025-07-17T09:05:59.4970782Z are learnable parameter vectors of size `C` (where `C` is the input size). 2025-07-17T09:05:59.4970894Z By default, the elements of :math:`\gamma` are sampled from 2025-07-17T09:05:59.4971014Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2025-07-17T09:05:59.4971177Z The standard-deviation is calculated via the biased estimator, equivalent to 2025-07-17T09:05:59.4971258Z `torch.var(input, unbiased=False)`. 2025-07-17T09:05:59.4971319Z 2025-07-17T09:05:59.4971458Z Also by default, during training this layer keeps running estimates of its 2025-07-17T09:05:59.4971595Z computed mean and variance, which are then used for normalization during 2025-07-17T09:05:59.4971736Z evaluation. The running estimates are kept with a default :attr:`momentum` 2025-07-17T09:05:59.4971799Z of 0.1. 2025-07-17T09:05:59.4971852Z 2025-07-17T09:05:59.4971994Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2025-07-17T09:05:59.4972263Z keep running estimates, and batch statistics are instead used during 2025-07-17T09:05:59.4972334Z evaluation time as well. 2025-07-17T09:05:59.4972387Z 2025-07-17T09:05:59.4972450Z .. note:: 2025-07-17T09:05:59.4972579Z This :attr:`momentum` argument is different from one used in optimizer 2025-07-17T09:05:59.4972813Z classes and the conventional notion of momentum. Mathematically, the 2025-07-17T09:05:59.4972911Z update rule for running statistics here is 2025-07-17T09:05:59.4973069Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2025-07-17T09:05:59.4973196Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2025-07-17T09:05:59.4973264Z new observed value. 2025-07-17T09:05:59.4973327Z 2025-07-17T09:05:59.4973504Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2025-07-17T09:05:59.4973664Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2025-07-17T09:05:59.4973774Z Normalization or Spatio-temporal Batch Normalization. 2025-07-17T09:05:59.4973829Z 2025-07-17T09:05:59.4973917Z Currently :class:`SyncBatchNorm` only supports 2025-07-17T09:05:59.4974091Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2025-07-17T09:05:59.4974219Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2025-07-17T09:05:59.4974349Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2025-07-17T09:05:59.4974413Z Network with DDP. 2025-07-17T09:05:59.4974476Z 2025-07-17T09:05:59.4974533Z Args: 2025-07-17T09:05:59.4974640Z num_features: :math:`C` from an expected input of size 2025-07-17T09:05:59.4974704Z :math:`(N, C, +)` 2025-07-17T09:05:59.4974826Z eps: a value added to the denominator for numerical stability. 2025-07-17T09:05:59.4974893Z Default: ``1e-5`` 2025-07-17T09:05:59.4975012Z momentum: the value used for the running_mean and running_var 2025-07-17T09:05:59.4975133Z computation. Can be set to ``None`` for cumulative moving average 2025-07-17T09:05:59.4975223Z (i.e. simple average). Default: 0.1 2025-07-17T09:05:59.4975354Z affine: a boolean value that when set to ``True``, this module has 2025-07-17T09:05:59.4975447Z learnable affine parameters. Default: ``True`` 2025-07-17T09:05:59.4975578Z track_running_stats: a boolean value that when set to ``True``, this 2025-07-17T09:05:59.4975716Z module tracks the running mean and variance, and when set to ``False``, 2025-07-17T09:05:59.4975855Z this module does not track such statistics, and initializes statistics 2025-07-17T09:05:59.4975976Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2025-07-17T09:05:59.4976116Z When these buffers are ``None``, this module always uses batch statistics. 2025-07-17T09:05:59.4976207Z in both training and eval modes. Default: ``True`` 2025-07-17T09:05:59.4976355Z process_group: synchronization of stats happen within each process group 2025-07-17T09:05:59.4976487Z individually. Default behavior is synchronization across the whole 2025-07-17T09:05:59.4976557Z world 2025-07-17T09:05:59.4976610Z 2025-07-17T09:05:59.4976672Z Shape: 2025-07-17T09:05:59.4976745Z - Input: :math:`(N, C, +)` 2025-07-17T09:05:59.4976845Z - Output: :math:`(N, C, +)` (same shape as input) 2025-07-17T09:05:59.4976902Z 2025-07-17T09:05:59.4976966Z .. note:: 2025-07-17T09:05:59.4977114Z Synchronization of batchnorm statistics occurs only while training, i.e. 2025-07-17T09:05:59.4977316Z synchronization is disabled when ``model.eval()`` is set or if 2025-07-17T09:05:59.4977449Z ``self.training`` is otherwise ``False``. 2025-07-17T09:05:59.4977505Z 2025-07-17T09:05:59.4977567Z Examples:: 2025-07-17T09:05:59.4977618Z 2025-07-17T09:05:59.4977685Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.4977757Z >>> # With Learnable Parameters 2025-07-17T09:05:59.4977937Z >>> m = nn.SyncBatchNorm(100) 2025-07-17T09:05:59.4978015Z >>> # creating process group (optional) 2025-07-17T09:05:59.4978109Z >>> # ranks is a list of int identifying rank ids. 2025-07-17T09:05:59.4978174Z >>> ranks = list(range(8)) 2025-07-17T09:05:59.4978251Z >>> r1, r2 = ranks[:4], ranks[4:] 2025-07-17T09:05:59.4978343Z >>> # Note: every rank calls into new_group for every 2025-07-17T09:05:59.4978436Z >>> # process group created, even if that rank is not 2025-07-17T09:05:59.4978508Z >>> # part of the group. 2025-07-17T09:05:59.4978662Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2025-07-17T09:05:59.4978780Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2025-07-17T09:05:59.4978866Z >>> # Without Learnable Parameters 2025-07-17T09:05:59.4978983Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2025-07-17T09:05:59.4979074Z >>> input = torch.randn(20, 100, 35, 45, 10) 2025-07-17T09:05:59.4979139Z >>> output = m(input) 2025-07-17T09:05:59.4979199Z 2025-07-17T09:05:59.4979277Z >>> # network is nn.BatchNorm layer 2025-07-17T09:05:59.4979443Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2025-07-17T09:05:59.4979548Z >>> # only single gpu per process is currently supported 2025-07-17T09:05:59.4979678Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2025-07-17T09:05:59.4979759Z >>> sync_bn_network, 2025-07-17T09:05:59.4979841Z >>> device_ids=[args.local_rank], 2025-07-17T09:05:59.4979930Z >>> output_device=args.local_rank) 2025-07-17T09:05:59.4979985Z 2025-07-17T09:05:59.4980131Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4980186Z 2025-07-17T09:05:59.4980257Z warnings.warn(msg) 2025-07-17T09:05:59.4980312Z 2025-07-17T09:05:59.4980439Z --- Parse Warning: 55 / 136 --- 2025-07-17T09:05:59.4980991Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SyncBatchNorm.convert_sync_batchnorm in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py line=825. 2025-07-17T09:05:59.4981146Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4981328Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2025-07-17T09:05:59.4981385Z 2025-07-17T09:05:59.4981445Z Args: 2025-07-17T09:05:59.4981597Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2025-07-17T09:05:59.4981731Z process_group (optional): process group to scope synchronization, 2025-07-17T09:05:59.4981804Z default is the whole world 2025-07-17T09:05:59.4981858Z 2025-07-17T09:05:59.4981920Z Returns: 2025-07-17T09:05:59.4982064Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2025-07-17T09:05:59.4982193Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2025-07-17T09:05:59.4982320Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2025-07-17T09:05:59.4982447Z instead. 2025-07-17T09:05:59.4982502Z 2025-07-17T09:05:59.4982615Z Example:: 2025-07-17T09:05:59.4982679Z 2025-07-17T09:05:59.4982762Z >>> # Network with nn.BatchNorm layer 2025-07-17T09:05:59.4982857Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-07-17T09:05:59.4982934Z >>> module = torch.nn.Sequential( 2025-07-17T09:05:59.4983145Z >>> torch.nn.Linear(20, 100), 2025-07-17T09:05:59.4983230Z >>> torch.nn.BatchNorm1d(100), 2025-07-17T09:05:59.4983300Z >>> ).cuda() 2025-07-17T09:05:59.4983381Z >>> # creating process group (optional) 2025-07-17T09:05:59.4983471Z >>> # ranks is a list of int identifying rank ids. 2025-07-17T09:05:59.4983545Z >>> ranks = list(range(8)) 2025-07-17T09:05:59.4983623Z >>> r1, r2 = ranks[:4], ranks[4:] 2025-07-17T09:05:59.4983714Z >>> # Note: every rank calls into new_group for every 2025-07-17T09:05:59.4983814Z >>> # process group created, even if that rank is not 2025-07-17T09:05:59.4983887Z >>> # part of the group. 2025-07-17T09:05:59.4983975Z >>> # xdoctest: +SKIP("distributed") 2025-07-17T09:05:59.4984122Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2025-07-17T09:05:59.4984253Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2025-07-17T09:05:59.4984431Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2025-07-17T09:05:59.4984492Z 2025-07-17T09:05:59.4984545Z 2025-07-17T09:05:59.4984698Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4984750Z 2025-07-17T09:05:59.4984815Z warnings.warn(msg) 2025-07-17T09:05:59.4984878Z 2025-07-17T09:05:59.4985000Z --- Parse Warning: 56 / 136 --- 2025-07-17T09:05:59.4985630Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Transformer.forward in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py line=186. 2025-07-17T09:05:59.4985793Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4985902Z Take in and process masked source/target sequences. 2025-07-17T09:05:59.4985956Z 2025-07-17T09:05:59.4986024Z .. note:: 2025-07-17T09:05:59.4986076Z 2025-07-17T09:05:59.4986301Z If a boolean tensor is provided for any of the [src/tgt/memory]_mask arguments, positions with a ``True`` value are 2025-07-17T09:05:59.4986396Z not allowed to participate in the attention, 2025-07-17T09:05:59.4986521Z which is the opposite of the definition for :attr:`attn_mask` 2025-07-17T09:05:59.4986637Z in :func:`torch.nn.functional.scaled_dot_product_attention`. 2025-07-17T09:05:59.4986701Z 2025-07-17T09:05:59.4986761Z Args: 2025-07-17T09:05:59.4986856Z src: the sequence to the encoder (required). 2025-07-17T09:05:59.4986941Z tgt: the sequence to the decoder (required). 2025-07-17T09:05:59.4987064Z src_mask: the additive mask for the src sequence (optional). 2025-07-17T09:05:59.4987173Z tgt_mask: the additive mask for the tgt sequence (optional). 2025-07-17T09:05:59.4987301Z memory_mask: the additive mask for the encoder output (optional). 2025-07-17T09:05:59.4987440Z src_key_padding_mask: the Tensor mask for src keys per batch (optional). 2025-07-17T09:05:59.4987574Z tgt_key_padding_mask: the Tensor mask for tgt keys per batch (optional). 2025-07-17T09:05:59.4987725Z memory_key_padding_mask: the Tensor mask for memory keys per batch (optional). 2025-07-17T09:05:59.4987934Z src_is_causal: If specified, applies a causal mask as ``src_mask``. 2025-07-17T09:05:59.4988097Z Default: ``None``; try to detect a causal mask. 2025-07-17T09:05:59.4988166Z Warning: 2025-07-17T09:05:59.4988270Z ``src_is_causal`` provides a hint that ``src_mask`` is 2025-07-17T09:05:59.4988375Z the causal mask. Providing incorrect hints can result in 2025-07-17T09:05:59.4988607Z incorrect execution, including forward and backward 2025-07-17T09:05:59.4988676Z compatibility. 2025-07-17T09:05:59.4988804Z tgt_is_causal: If specified, applies a causal mask as ``tgt_mask``. 2025-07-17T09:05:59.4988889Z Default: ``None``; try to detect a causal mask. 2025-07-17T09:05:59.4988952Z Warning: 2025-07-17T09:05:59.4989049Z ``tgt_is_causal`` provides a hint that ``tgt_mask`` is 2025-07-17T09:05:59.4989159Z the causal mask. Providing incorrect hints can result in 2025-07-17T09:05:59.4989255Z incorrect execution, including forward and backward 2025-07-17T09:05:59.4989326Z compatibility. 2025-07-17T09:05:59.4989435Z memory_is_causal: If specified, applies a causal mask as 2025-07-17T09:05:59.4989506Z ``memory_mask``. 2025-07-17T09:05:59.4989578Z Default: ``False``. 2025-07-17T09:05:59.4989642Z Warning: 2025-07-17T09:05:59.4989728Z ``memory_is_causal`` provides a hint that 2025-07-17T09:05:59.4989836Z ``memory_mask`` is the causal mask. Providing incorrect 2025-07-17T09:05:59.4989937Z hints can result in incorrect execution, including 2025-07-17T09:05:59.4990029Z forward and backward compatibility. 2025-07-17T09:05:59.4990086Z 2025-07-17T09:05:59.4990150Z Shape: 2025-07-17T09:05:59.4990304Z - src: :math:`(S, E)` for unbatched input, :math:`(S, N, E)` if `batch_first=False` or 2025-07-17T09:05:59.4990388Z `(N, S, E)` if `batch_first=True`. 2025-07-17T09:05:59.4990525Z - tgt: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or 2025-07-17T09:05:59.4990602Z `(N, T, E)` if `batch_first=True`. 2025-07-17T09:05:59.4990723Z - src_mask: :math:`(S, S)` or :math:`(N\cdot\text{num\_heads}, S, S)`. 2025-07-17T09:05:59.4990829Z - tgt_mask: :math:`(T, T)` or :math:`(N\cdot\text{num\_heads}, T, T)`. 2025-07-17T09:05:59.4990911Z - memory_mask: :math:`(T, S)`. 2025-07-17T09:05:59.4991060Z - src_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`. 2025-07-17T09:05:59.4991200Z - tgt_key_padding_mask: :math:`(T)` for unbatched input otherwise :math:`(N, T)`. 2025-07-17T09:05:59.4991346Z - memory_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`. 2025-07-17T09:05:59.4991405Z 2025-07-17T09:05:59.4991591Z Note: [src/tgt/memory]_mask ensures that position :math:`i` is allowed to attend the unmasked 2025-07-17T09:05:59.4991724Z positions. If a BoolTensor is provided, positions with ``True`` 2025-07-17T09:05:59.4991878Z are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor 2025-07-17T09:05:59.4991990Z is provided, it will be added to the attention weight. 2025-07-17T09:05:59.4992166Z [src/tgt/memory]_key_padding_mask provides specified elements in the key to be ignored by 2025-07-17T09:05:59.4992299Z the attention. If a BoolTensor is provided, the positions with the 2025-07-17T09:05:59.4992484Z value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged. 2025-07-17T09:05:59.4992544Z 2025-07-17T09:05:59.4992758Z - output: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or 2025-07-17T09:05:59.4992892Z `(N, T, E)` if `batch_first=True`. 2025-07-17T09:05:59.4992948Z 2025-07-17T09:05:59.4993094Z Note: Due to the multi-head attention architecture in the transformer model, 2025-07-17T09:05:59.4993338Z the output sequence length of a transformer is same as the input sequence 2025-07-17T09:05:59.4993419Z (i.e. target) length of the decoder. 2025-07-17T09:05:59.4993470Z 2025-07-17T09:05:59.4993662Z where :math:`S` is the source sequence length, :math:`T` is the target sequence length, :math:`N` is the 2025-07-17T09:05:59.4993750Z batch size, :math:`E` is the feature number 2025-07-17T09:05:59.4993806Z 2025-07-17T09:05:59.4993869Z Examples: 2025-07-17T09:05:59.4993941Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.4994024Z >>> output = transformer_model( 2025-07-17T09:05:59.4994123Z ... src, tgt, src_mask=src_mask, tgt_mask=tgt_mask 2025-07-17T09:05:59.4994189Z ... ) 2025-07-17T09:05:59.4994247Z 2025-07-17T09:05:59.4994400Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.4994454Z 2025-07-17T09:05:59.4994529Z warnings.warn(msg) 2025-07-17T09:05:59.4995024Z 2025-07-17T09:05:59.4995166Z --- Parse Warning: 57 / 136 --- 2025-07-17T09:05:59.4995656Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Unflatten in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py line=60. 2025-07-17T09:05:59.4995817Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.4995874Z 2025-07-17T09:05:59.4996061Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2025-07-17T09:05:59.4996125Z 2025-07-17T09:05:59.4996292Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2025-07-17T09:05:59.4996430Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2025-07-17T09:05:59.4996494Z 2025-07-17T09:05:59.4996681Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2025-07-17T09:05:59.4996839Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2025-07-17T09:05:59.4996944Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2025-07-17T09:05:59.4997002Z 2025-07-17T09:05:59.4997062Z Shape: 2025-07-17T09:05:59.4997199Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2025-07-17T09:05:59.4997341Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2025-07-17T09:05:59.4997476Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2025-07-17T09:05:59.4997562Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2025-07-17T09:05:59.4997618Z 2025-07-17T09:05:59.4997675Z Args: 2025-07-17T09:05:59.4997770Z dim (Union[int, str]): Dimension to be unflattened 2025-07-17T09:05:59.4997974Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2025-07-17T09:05:59.4998028Z 2025-07-17T09:05:59.4998094Z Examples: 2025-07-17T09:05:59.4998165Z >>> input = torch.randn(2, 50) 2025-07-17T09:05:59.4998236Z >>> # With tuple of ints 2025-07-17T09:05:59.4998305Z >>> m = nn.Sequential( 2025-07-17T09:05:59.4998383Z >>> nn.Linear(50, 50), 2025-07-17T09:05:59.4998454Z >>> nn.Unflatten(1, (2, 5, 5)) 2025-07-17T09:05:59.4998517Z >>> ) 2025-07-17T09:05:59.4998582Z >>> output = m(input) 2025-07-17T09:05:59.4998776Z >>> output.size() 2025-07-17T09:05:59.4998896Z torch.Size([2, 2, 5, 5]) 2025-07-17T09:05:59.4998964Z >>> # With torch.Size 2025-07-17T09:05:59.4999024Z >>> m = nn.Sequential( 2025-07-17T09:05:59.4999091Z >>> nn.Linear(50, 50), 2025-07-17T09:05:59.4999173Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2025-07-17T09:05:59.4999230Z >>> ) 2025-07-17T09:05:59.4999305Z >>> output = m(input) 2025-07-17T09:05:59.4999475Z >>> output.size() 2025-07-17T09:05:59.4999541Z torch.Size([2, 2, 5, 5]) 2025-07-17T09:05:59.4999623Z >>> # With namedshape (tuple of tuples) 2025-07-17T09:05:59.4999724Z >>> input = torch.randn(2, 50, names=("N", "features")) 2025-07-17T09:05:59.4999849Z >>> unflatten = nn.Unflatten("features", (("C", 2), ("H", 5), ("W", 5))) 2025-07-17T09:05:59.4999925Z >>> output = unflatten(input) 2025-07-17T09:05:59.4999984Z >>> output.size() 2025-07-17T09:05:59.5000053Z torch.Size([2, 2, 5, 5]) 2025-07-17T09:05:59.5000106Z 2025-07-17T09:05:59.5000260Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5000314Z 2025-07-17T09:05:59.5000379Z warnings.warn(msg) 2025-07-17T09:05:59.5000434Z 2025-07-17T09:05:59.5000555Z --- Parse Warning: 58 / 136 --- 2025-07-17T09:05:59.5001053Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MaxUnpool2d in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py line=406. 2025-07-17T09:05:59.5001216Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5001312Z Computes a partial inverse of :class:`MaxPool2d`. 2025-07-17T09:05:59.5001367Z 2025-07-17T09:05:59.5001528Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2025-07-17T09:05:59.5001582Z 2025-07-17T09:05:59.5001722Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2025-07-17T09:05:59.5001865Z including the indices of the maximal values and computes a partial inverse 2025-07-17T09:05:59.5001968Z in which all non-maximal values are set to zero. 2025-07-17T09:05:59.5002024Z 2025-07-17T09:05:59.5002085Z Note: 2025-07-17T09:05:59.5002273Z This operation may behave nondeterministically when the input indices has repeat values. 2025-07-17T09:05:59.5002499Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2025-07-17T09:05:59.5002552Z 2025-07-17T09:05:59.5002694Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2025-07-17T09:05:59.5002803Z sizes. Hence, the inversion process can get ambiguous. 2025-07-17T09:05:59.5002929Z To accommodate this, you can provide the needed output size 2025-07-17T09:05:59.5003058Z as an additional argument :attr:`output_size` in the forward call. 2025-07-17T09:05:59.5003144Z See the Inputs and Example below. 2025-07-17T09:05:59.5003198Z 2025-07-17T09:05:59.5003263Z Args: 2025-07-17T09:05:59.5003378Z kernel_size (int or tuple): Size of the max pooling window. 2025-07-17T09:05:59.5003501Z stride (int or tuple): Stride of the max pooling window. 2025-07-17T09:05:59.5003596Z It is set to :attr:`kernel_size` by default. 2025-07-17T09:05:59.5003930Z padding (int or tuple): Padding that was added to the input 2025-07-17T09:05:59.5004189Z 2025-07-17T09:05:59.5004334Z Inputs: 2025-07-17T09:05:59.5004507Z - `input`: the input Tensor to invert 2025-07-17T09:05:59.5004795Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2025-07-17T09:05:59.5005083Z - `output_size` (optional): the targeted output size 2025-07-17T09:05:59.5005426Z 2025-07-17T09:05:59.5005555Z Shape: 2025-07-17T09:05:59.5005823Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2025-07-17T09:05:59.5006132Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2025-07-17T09:05:59.5006374Z 2025-07-17T09:05:59.5006515Z .. math:: 2025-07-17T09:05:59.5006896Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2025-07-17T09:05:59.5007174Z 2025-07-17T09:05:59.5007312Z .. math:: 2025-07-17T09:05:59.5007548Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2025-07-17T09:05:59.5007805Z 2025-07-17T09:05:59.5007984Z or as given by :attr:`output_size` in the call operator 2025-07-17T09:05:59.5008196Z 2025-07-17T09:05:59.5008327Z Example:: 2025-07-17T09:05:59.5008473Z 2025-07-17T09:05:59.5008672Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2025-07-17T09:05:59.5009111Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2025-07-17T09:05:59.5009339Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2025-07-17T09:05:59.5009549Z [ 5., 6., 7., 8.], 2025-07-17T09:05:59.5009739Z [ 9., 10., 11., 12.], 2025-07-17T09:05:59.5009946Z [13., 14., 15., 16.]]]]) 2025-07-17T09:05:59.5010198Z >>> output, indices = pool(input) 2025-07-17T09:05:59.5010418Z >>> unpool(output, indices) 2025-07-17T09:05:59.5010670Z tensor([[[[ 0., 0., 0., 0.], 2025-07-17T09:05:59.5010859Z [ 0., 6., 0., 8.], 2025-07-17T09:05:59.5011042Z [ 0., 0., 0., 0.], 2025-07-17T09:05:59.5011228Z [ 0., 14., 0., 16.]]]]) 2025-07-17T09:05:59.5011503Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2025-07-17T09:05:59.5011811Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2025-07-17T09:05:59.5012045Z [ 6., 7., 8., 9., 10.], 2025-07-17T09:05:59.5012245Z [11., 12., 13., 14., 15.], 2025-07-17T09:05:59.5012449Z [16., 17., 18., 19., 20.]]]]) 2025-07-17T09:05:59.5012661Z >>> output, indices = pool(input) 2025-07-17T09:05:59.5012929Z >>> # This call will not work without specifying output_size 2025-07-17T09:05:59.5013217Z >>> unpool(output, indices, output_size=input.size()) 2025-07-17T09:05:59.5013530Z tensor([[[[ 0., 0., 0., 0., 0.], 2025-07-17T09:05:59.5013728Z [ 0., 7., 0., 9., 0.], 2025-07-17T09:05:59.5013917Z [ 0., 0., 0., 0., 0.], 2025-07-17T09:05:59.5014104Z [ 0., 17., 0., 19., 0.]]]]) 2025-07-17T09:05:59.5014292Z 2025-07-17T09:05:59.5014429Z 2025-07-17T09:05:59.5014559Z 2025-07-17T09:05:59.5014817Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5015213Z 2025-07-17T09:05:59.5015430Z warnings.warn(msg) 2025-07-17T09:05:59.5015607Z 2025-07-17T09:05:59.5015813Z --- Parse Warning: 59 / 136 --- 2025-07-17T09:05:59.5016485Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=EmbeddingBag in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py line=272. 2025-07-17T09:05:59.5017218Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5017623Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2025-07-17T09:05:59.5017927Z 2025-07-17T09:05:59.5018186Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2025-07-17T09:05:59.5018651Z and with 2D inputs, this class 2025-07-17T09:05:59.5018835Z 2025-07-17T09:05:59.5019094Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2025-07-17T09:05:59.5019622Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2025-07-17T09:05:59.5020029Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2025-07-17T09:05:59.5020313Z 2025-07-17T09:05:59.5020594Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2025-07-17T09:05:59.5020925Z operations. 2025-07-17T09:05:59.5021071Z 2025-07-17T09:05:59.5021299Z EmbeddingBag also supports per-sample weights as an argument to the forward 2025-07-17T09:05:59.5021649Z pass. This scales the output of the Embedding before performing a weighted 2025-07-17T09:05:59.5021999Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2025-07-17T09:05:59.5022351Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2025-07-17T09:05:59.5022622Z :attr:`per_sample_weights`. 2025-07-17T09:05:59.5022802Z 2025-07-17T09:05:59.5022931Z Args: 2025-07-17T09:05:59.5023139Z num_embeddings (int): size of the dictionary of embeddings 2025-07-17T09:05:59.5023424Z embedding_dim (int): the size of each embedding vector 2025-07-17T09:05:59.5023773Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2025-07-17T09:05:59.5024108Z is renormalized to have norm :attr:`max_norm`. 2025-07-17T09:05:59.5024469Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2025-07-17T09:05:59.5024924Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2025-07-17T09:05:59.5025330Z the words in the mini-batch. Default ``False``. 2025-07-17T09:05:59.5025614Z Note: this option is not supported when ``mode="max"``. 2025-07-17T09:05:59.5025944Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2025-07-17T09:05:59.5026283Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2025-07-17T09:05:59.5026607Z into consideration. ``"mean"`` computes the average of the values 2025-07-17T09:05:59.5026908Z in the bag, ``"max"`` computes the max value over each bag. 2025-07-17T09:05:59.5027157Z Default: ``"mean"`` 2025-07-17T09:05:59.5027487Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2025-07-17T09:05:59.5027885Z Notes for more details regarding sparse gradients. Note: this option is not 2025-07-17T09:05:59.5028186Z supported when ``mode="max"``. 2025-07-17T09:05:59.5028550Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2025-07-17T09:05:59.5028958Z is equivalent to the size of `indices`. This matches the CSR format. 2025-07-17T09:05:59.5029339Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2025-07-17T09:05:59.5029761Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2025-07-17T09:05:59.5030222Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2025-07-17T09:05:59.5030665Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2025-07-17T09:05:59.5031028Z zeros, but can be updated to another value to be used as the padding vector. 2025-07-17T09:05:59.5031514Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2025-07-17T09:05:59.5031796Z reduction. 2025-07-17T09:05:59.5031990Z 2025-07-17T09:05:59.5032126Z Attributes: 2025-07-17T09:05:59.5032404Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2025-07-17T09:05:59.5032743Z initialized from :math:`\mathcal{N}(0, 1)`. 2025-07-17T09:05:59.5032961Z 2025-07-17T09:05:59.5033098Z Examples:: 2025-07-17T09:05:59.5033261Z 2025-07-17T09:05:59.5033443Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2025-07-17T09:05:59.5033715Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2025-07-17T09:05:59.5033963Z >>> # a batch of 2 samples of 4 indices each 2025-07-17T09:05:59.5034224Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2025-07-17T09:05:59.5034506Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2025-07-17T09:05:59.5034754Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-07-17T09:05:59.5034984Z >>> embedding_sum(input, offsets) 2025-07-17T09:05:59.5035199Z tensor([[-0.8861, -5.4350, -0.0523], 2025-07-17T09:05:59.5035395Z [ 1.1306, -2.5798, -1.0044]]) 2025-07-17T09:05:59.5035581Z 2025-07-17T09:05:59.5035729Z >>> # Example with padding_idx 2025-07-17T09:05:59.5035986Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2025-07-17T09:05:59.5036282Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2025-07-17T09:05:59.5036544Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2025-07-17T09:05:59.5036773Z >>> embedding_sum(input, offsets) 2025-07-17T09:05:59.5036975Z tensor([[ 0.0000, 0.0000, 0.0000], 2025-07-17T09:05:59.5037169Z [-0.7082, 3.2145, -2.6251]]) 2025-07-17T09:05:59.5037344Z 2025-07-17T09:05:59.5037522Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2025-07-17T09:05:59.5037792Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2025-07-17T09:05:59.5038048Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2025-07-17T09:05:59.5038275Z embedding.weight, 2025-07-17T09:05:59.5038485Z padding_idx=embedding.padding_idx, 2025-07-17T09:05:59.5038695Z mode='sum') 2025-07-17T09:05:59.5038870Z 2025-07-17T09:05:59.5039101Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5039365Z 2025-07-17T09:05:59.5039503Z warnings.warn(msg) 2025-07-17T09:05:59.5039667Z 2025-07-17T09:05:59.5039883Z --- Parse Warning: 60 / 136 --- 2025-07-17T09:05:59.5040580Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=TripletMarginWithDistanceLoss in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py line=1718. 2025-07-17T09:05:59.5041334Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5041674Z Creates a criterion that measures the triplet loss given input 2025-07-17T09:05:59.5041980Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2025-07-17T09:05:59.5042287Z positive, and negative examples, respectively), and a nonnegative, 2025-07-17T09:05:59.5042761Z real-valued function ("distance function") used to compute the relationship 2025-07-17T09:05:59.5043108Z between the anchor and positive example ("positive distance") and the 2025-07-17T09:05:59.5043400Z anchor and negative example ("negative distance"). 2025-07-17T09:05:59.5043614Z 2025-07-17T09:05:59.5043932Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2025-07-17T09:05:59.5044204Z can be described as: 2025-07-17T09:05:59.5044379Z 2025-07-17T09:05:59.5044520Z .. math:: 2025-07-17T09:05:59.5044700Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2025-07-17T09:05:59.5044949Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2025-07-17T09:05:59.5045191Z 2025-07-17T09:05:59.5045427Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2025-07-17T09:05:59.5045808Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2025-07-17T09:05:59.5046190Z and :math:`margin` is a nonnegative margin representing the minimum difference 2025-07-17T09:05:59.5046543Z between the positive and negative distances that is required for the loss to 2025-07-17T09:05:59.5046891Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2025-07-17T09:05:59.5047171Z that the distance function can handle. 2025-07-17T09:05:59.5047367Z 2025-07-17T09:05:59.5047511Z If :attr:`reduction` is not ``'none'`` 2025-07-17T09:05:59.5047711Z (default ``'mean'``), then: 2025-07-17T09:05:59.5047891Z 2025-07-17T09:05:59.5048029Z .. math:: 2025-07-17T09:05:59.5048179Z \ell(x, y) = 2025-07-17T09:05:59.5048345Z \begin{cases} 2025-07-17T09:05:59.5048576Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2025-07-17T09:05:59.5048878Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2025-07-17T09:05:59.5053945Z \end{cases} 2025-07-17T09:05:59.5054115Z 2025-07-17T09:05:59.5054351Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2025-07-17T09:05:59.5054714Z loss for input tensors using the :math:`l_p` distance as the distance function. 2025-07-17T09:05:59.5054983Z 2025-07-17T09:05:59.5055123Z Args: 2025-07-17T09:05:59.5055391Z distance_function (Callable, optional): A nonnegative, real-valued function that 2025-07-17T09:05:59.5055747Z quantifies the closeness of two tensors. If not specified, 2025-07-17T09:05:59.5056049Z `nn.PairwiseDistance` will be used. Default: ``None`` 2025-07-17T09:05:59.5056376Z margin (float, optional): A nonnegative margin representing the minimum difference 2025-07-17T09:05:59.5056760Z between the positive and negative distances required for the loss to be 0. Larger 2025-07-17T09:05:59.5057153Z margins penalize cases where the negative examples are not distant enough from the 2025-07-17T09:05:59.5057494Z anchors, relative to the positives. Default: :math:`1`. 2025-07-17T09:05:59.5057808Z swap (bool, optional): Whether to use the distance swap described in the paper 2025-07-17T09:05:59.5058178Z `Learning shallow convolutional feature descriptors with triplet losses` by 2025-07-17T09:05:59.5058545Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2025-07-17T09:05:59.5058910Z negative example than the anchor is, swaps the positive example and the anchor in 2025-07-17T09:05:59.5059219Z the loss computation. Default: ``False``. 2025-07-17T09:05:59.5059541Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2025-07-17T09:05:59.5059883Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2025-07-17T09:05:59.5060492Z ``'mean'``: the sum of the output will be divided by the number of 2025-07-17T09:05:59.5060808Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2025-07-17T09:05:59.5061056Z 2025-07-17T09:05:59.5061194Z 2025-07-17T09:05:59.5061328Z Shape: 2025-07-17T09:05:59.5061678Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2025-07-17T09:05:59.5061976Z as supported by the distance function. 2025-07-17T09:05:59.5062272Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2025-07-17T09:05:59.5062540Z otherwise. 2025-07-17T09:05:59.5062698Z 2025-07-17T09:05:59.5062837Z Examples: 2025-07-17T09:05:59.5062986Z 2025-07-17T09:05:59.5063130Z >>> # Initialize embeddings 2025-07-17T09:05:59.5063336Z >>> embedding = nn.Embedding(1000, 128) 2025-07-17T09:05:59.5063559Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2025-07-17T09:05:59.5063792Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2025-07-17T09:05:59.5064018Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2025-07-17T09:05:59.5064231Z >>> anchor = embedding(anchor_ids) 2025-07-17T09:05:59.5064440Z >>> positive = embedding(positive_ids) 2025-07-17T09:05:59.5064645Z >>> negative = embedding(negative_ids) 2025-07-17T09:05:59.5064831Z >>> 2025-07-17T09:05:59.5064987Z >>> # Built-in Distance Function 2025-07-17T09:05:59.5065177Z >>> triplet_loss = \ 2025-07-17T09:05:59.5065558Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2025-07-17T09:05:59.5065886Z >>> output = triplet_loss(anchor, positive, negative) 2025-07-17T09:05:59.5066113Z >>> output.backward() 2025-07-17T09:05:59.5066285Z >>> 2025-07-17T09:05:59.5066435Z >>> # Custom Distance Function 2025-07-17T09:05:59.5066634Z >>> def l_infinity(x1, x2): 2025-07-17T09:05:59.5066866Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2025-07-17T09:05:59.5067071Z >>> 2025-07-17T09:05:59.5067267Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2025-07-17T09:05:59.5067516Z >>> triplet_loss = ( 2025-07-17T09:05:59.5067798Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2025-07-17T09:05:59.5068115Z >>> output = triplet_loss(anchor, positive, negative) 2025-07-17T09:05:59.5068331Z >>> output.backward() 2025-07-17T09:05:59.5068498Z >>> 2025-07-17T09:05:59.5068664Z >>> # Custom Distance Function (Lambda) 2025-07-17T09:05:59.5068866Z >>> triplet_loss = ( 2025-07-17T09:05:59.5069059Z >>> nn.TripletMarginWithDistanceLoss( 2025-07-17T09:05:59.5069331Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2025-07-17T09:05:59.5069613Z >>> output = triplet_loss(anchor, positive, negative) 2025-07-17T09:05:59.5069829Z >>> output.backward() 2025-07-17T09:05:59.5069989Z 2025-07-17T09:05:59.5070120Z Reference: 2025-07-17T09:05:59.5070388Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2025-07-17T09:05:59.5070773Z https://bmva-archive.org.uk/bmvc/2016/papers/paper119/index.html 2025-07-17T09:05:59.5071026Z 2025-07-17T09:05:59.5071253Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2025-07-17T09:05:59.5071522Z 2025-07-17T09:05:59.5071658Z warnings.warn(msg) 2025-07-17T09:05:59.5071822Z 2025-07-17T09:05:59.5072044Z --- Parse Warning: 61 / 136 --- 2025-07-17T09:05:59.5072675Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CTCLoss in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py line=1852. 2025-07-17T09:05:59.5073597Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5073909Z The Connectionist Temporal Classification loss. 2025-07-17T09:05:59.5074126Z 2025-07-17T09:05:59.5074424Z Calculates loss between a continuous (unsegmented) time series and a target sequence. CTCLoss sums over the 2025-07-17T09:05:59.5075034Z probability of possible alignments of input to target, producing a loss value which is differentiable 2025-07-17T09:05:59.5075497Z with respect to each input node. The alignment of input to target is assumed to be "many-to-one", which 2025-07-17T09:05:59.5075926Z limits the length of the target sequence such that it must be :math:`\leq` the input length. 2025-07-17T09:05:59.5076223Z 2025-07-17T09:05:59.5076361Z Args: 2025-07-17T09:05:59.5076559Z blank (int, optional): blank label. Default :math:`0`. 2025-07-17T09:05:59.5076884Z reduction (str, optional): Specifies the reduction to apply to the output: 2025-07-17T09:05:59.5077216Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2025-07-17T09:05:59.5077517Z ``'mean'``: the output losses will be divided by the target lengths and 2025-07-17T09:05:59.5077855Z then the mean over the batch is taken, ``'sum'``: the output losses will be summed. 2025-07-17T09:05:59.5078132Z Default: ``'mean'`` 2025-07-17T09:05:59.5078340Z zero_infinity (bool, optional): 2025-07-17T09:05:59.5078588Z Whether to zero infinite losses and the associated gradients. 2025-07-17T09:05:59.5078848Z Default: ``False`` 2025-07-17T09:05:59.5079078Z Infinite losses mainly occur when the inputs are too short 2025-07-17T09:05:59.5079329Z to be aligned to the targets. 2025-07-17T09:05:59.5079514Z 2025-07-17T09:05:59.5079642Z Shape: 2025-07-17T09:05:59.5079855Z - Log_probs: Tensor of size :math:`(T, N, C)` or :math:`(T, C)`, 2025-07-17T09:05:59.5080119Z where :math:`T = \text{input length}`, 2025-07-17T09:05:59.5080340Z :math:`N = \text{batch size}`, and 2025-07-17T09:05:59.5080591Z :math:`C = \text{number of classes (including blank)}`. 2025-07-17T09:05:59.5080906Z The logarithmized probabilities of the outputs (e.g. obtained with 2025-07-17T09:05:59.5081202Z :func:`torch.nn.functional.log_softmax`). 2025-07-17T09:05:59.5081429Z - Targets: Tensor of size :math:`(N, S)` or 2025-07-17T09:05:59.5081676Z :math:`(\operatorname{sum}(\text{target\_lengths}))`, 2025-07-17T09:05:59.5081916Z where :math:`N = \text{batch size}` and 2025-07-17T09:05:59.5082153Z :math:`S = \text{max target length, if shape is } (N, S)`. 2025-07-17T09:05:59.5082437Z It represents the target sequences. Each element in the target 2025-07-17T09:05:59.5082774Z sequence is a class index. And the target index cannot be blank (default=0). 2025-07-17T09:05:59.5083079Z In the :math:`(N, S)` form, targets are padded to the 2025-07-17T09:05:59.5083328Z length of the longest sequence, and stacked. 2025-07-17T09:05:59.5083587Z In the :math:`(\operatorname{sum}(\text{target\_lengths}))` form, 2025-07-17T09:05:59.5083857Z the targets are assumed to be un-padded and 2025-07-17T09:05:59.5084085Z concatenated within 1 dimension. 2025-07-17T09:05:59.5084343Z - Input_lengths: Tuple or tensor of size :math:`(N)` or :math:`()`, 2025-07-17T09:05:59.5084643Z where :math:`N = \text{batch size}`. It represents the lengths of the 2025-07-17T09:05:59.5084952Z inputs (must each be :math:`\leq T`). And the lengths are specified 2025-07-17T09:05:59.5085278Z for each sequence to achieve masking under the assumption that sequences 2025-07-17T09:05:59.5085631Z are padded to equal lengths. 2025-07-17T09:05:59.5085940Z - Target_lengths: Tuple or tensor of size :math:`(N)` or :math:`()`, 2025-07-17T09:05:59.5086253Z where :math:`N = \text{batch size}`. It represents lengths of the targets. 2025-07-17T09:05:59.5086568Z Lengths are specified for each sequence to achieve masking under the 2025-07-17T09:05:59.5087020Z assumption that sequences are padded to equal lengths. If target shape is 2025-07-17T09:05:59.5087342Z :math:`(N,S)`, target_lengths are effectively the stop index 2025-07-17T09:05:59.5087665Z :math:`s_n` for each target sequence, such that ``target_n = targets[n,0:s_n]`` for 2025-07-17T09:05:59.5087991Z each target in a batch. Lengths must each be :math:`\leq S` 2025-07-17T09:05:59.5088314Z If the targets are given as a 1d tensor that is the concatenation of individual 2025-07-17T09:05:59.5088660Z targets, the target_lengths must add up to the total length of the tensor. 2025-07-17T09:05:59.5088988Z - Output: scalar if :attr:`reduction` is ``'mean'`` (default) or 2025-07-17T09:05:59.5089305Z ``'sum'``. If :attr:`reduction` is ``'none'``, then :math:`(N)` if input is batched or 2025-07-17T09:05:59.5089620Z :math:`()` if input is unbatched, where :math:`N = \text{batch size}`. 2025-07-17T09:05:59.5089871Z 2025-07-17T09:05:59.5090011Z Examples: 2025-07-17T09:05:59.5090155Z 2025-07-17T09:05:59.5090311Z >>> # Target are to be padded 2025-07-17T09:05:59.5090521Z >>> T = 50 # Input sequence length 2025-07-17T09:05:59.5090744Z >>> C = 20 # Number of classes (including blank) 2025-07-17T09:05:59.5090957Z >>> N = 16 # Batch size 2025-07-17T09:05:59.5091225Z >>> S = 30 # Target sequence length of longest target in batch (padding length) 2025-07-17T09:05:59.5091542Z >>> S_min = 10 # Minimum target length, for demonstration purposes 2025-07-17T09:05:59.5091784Z >>> 2025-07-17T09:05:59.5092000Z >>> # Initialize random batch of input vectors, for *size = (T,N,C) 2025-07-17T09:05:59.5092311Z >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_() 2025-07-17T09:05:59.5092555Z >>> 2025-07-17T09:05:59.5092755Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-07-17T09:05:59.5093064Z >>> target = torch.randint(low=1, high=C, size=(N, S), dtype=torch.long) 2025-07-17T09:05:59.5093314Z >>> 2025-07-17T09:05:59.5093529Z >>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long) 2025-07-17T09:05:59.5093799Z >>> target_lengths = torch.randint( 2025-07-17T09:05:59.5093998Z ... low=S_min, 2025-07-17T09:05:59.5094172Z ... high=S, 2025-07-17T09:05:59.5094347Z ... size=(N,), 2025-07-17T09:05:59.5094523Z ... dtype=torch.long, 2025-07-17T09:05:59.5094701Z ... ) 2025-07-17T09:05:59.5094862Z >>> ctc_loss = nn.CTCLoss() 2025-07-17T09:05:59.5095102Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-07-17T09:05:59.5095344Z >>> loss.backward() 2025-07-17T09:05:59.5095517Z >>> 2025-07-17T09:05:59.5095655Z >>> 2025-07-17T09:05:59.5095822Z >>> # Target are to be un-padded 2025-07-17T09:05:59.5096027Z >>> T = 50 # Input sequence length 2025-07-17T09:05:59.5096243Z >>> C = 20 # Number of classes (including blank) 2025-07-17T09:05:59.5096448Z >>> N = 16 # Batch size 2025-07-17T09:05:59.5096616Z >>> 2025-07-17T09:05:59.5096808Z >>> # Initialize random batch of input vectors, for *size = (T,N,C) 2025-07-17T09:05:59.5097111Z >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_() 2025-07-17T09:05:59.5097504Z >>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long) 2025-07-17T09:05:59.5097808Z >>> 2025-07-17T09:05:59.5098004Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-07-17T09:05:59.5098323Z >>> target_lengths = torch.randint(low=1, high=T, size=(N,), dtype=torch.long) 2025-07-17T09:05:59.5098592Z >>> target = torch.randint( 2025-07-17T09:05:59.5098893Z ... low=1, 2025-07-17T09:05:59.5099058Z ... high=C, 2025-07-17T09:05:59.5099237Z ... size=(sum(target_lengths),), 2025-07-17T09:05:59.5099435Z ... dtype=torch.long, 2025-07-17T09:05:59.5099621Z ... ) 2025-07-17T09:05:59.5099780Z >>> ctc_loss = nn.CTCLoss() 2025-07-17T09:05:59.5100009Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-07-17T09:05:59.5100244Z >>> loss.backward() 2025-07-17T09:05:59.5100418Z >>> 2025-07-17T09:05:59.5100556Z >>> 2025-07-17T09:05:59.5100750Z >>> # Target are to be un-padded and unbatched (effectively N=1) 2025-07-17T09:05:59.5101004Z >>> T = 50 # Input sequence length 2025-07-17T09:05:59.5101231Z >>> C = 20 # Number of classes (including blank) 2025-07-17T09:05:59.5101475Z >>> 2025-07-17T09:05:59.5101680Z >>> # Initialize random batch of input vectors, for *size = (T,C) 2025-07-17T09:05:59.5101953Z >>> # xdoctest: +SKIP("FIXME: error in doctest") 2025-07-17T09:05:59.5102238Z >>> input = torch.randn(T, C).log_softmax(1).detach().requires_grad_() 2025-07-17T09:05:59.5102524Z >>> input_lengths = torch.tensor(T, dtype=torch.long) 2025-07-17T09:05:59.5102735Z >>> 2025-07-17T09:05:59.5102925Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-07-17T09:05:59.5103234Z >>> target_lengths = torch.randint(low=1, high=T, size=(), dtype=torch.long) 2025-07-17T09:05:59.5103506Z >>> target = torch.randint( 2025-07-17T09:05:59.5103703Z ... low=1, 2025-07-17T09:05:59.5103871Z ... high=C, 2025-07-17T09:05:59.5104057Z ... size=(target_lengths,), 2025-07-17T09:05:59.5104259Z ... dtype=torch.long, 2025-07-17T09:05:59.5104449Z ... ) 2025-07-17T09:05:59.5104617Z >>> ctc_loss = nn.CTCLoss() 2025-07-17T09:05:59.5104895Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-07-17T09:05:59.5105129Z >>> loss.backward() 2025-07-17T09:05:59.5105378Z 2025-07-17T09:05:59.5105509Z Reference: 2025-07-17T09:05:59.5105708Z A. Graves et al.: Connectionist Temporal Classification: 2025-07-17T09:05:59.5106028Z Labelling Unsegmented Sequence Data with Recurrent Neural Networks: 2025-07-17T09:05:59.5106328Z https://www.cs.toronto.edu/~graves/icml_2006.pdf 2025-07-17T09:05:59.5106571Z 2025-07-17T09:05:59.5106727Z Note: 2025-07-17T09:05:59.5106964Z In order to use CuDNN, the following must be satisfied: :attr:`targets` must be 2025-07-17T09:05:59.5107331Z in concatenated format, all :attr:`input_lengths` must be `T`. :math:`blank=0`, 2025-07-17T09:05:59.5107686Z :attr:`target_lengths` :math:`\leq 256`, the integer arguments must be of 2025-07-17T09:05:59.5107950Z dtype :attr:`torch.int32`. 2025-07-17T09:05:59.5108129Z 2025-07-17T09:05:59.5108356Z The regular implementation uses the (more common in PyTorch) `torch.long` dtype. 2025-07-17T09:05:59.5108629Z 2025-07-17T09:05:59.5108762Z 2025-07-17T09:05:59.5108889Z Note: 2025-07-17T09:05:59.5109136Z In some circumstances when using the CUDA backend with CuDNN, this operator 2025-07-17T09:05:59.5109504Z may select a nondeterministic algorithm to increase performance. If this is 2025-07-17T09:05:59.5109992Z undesirable, you can try to make the operation deterministic (potentially at 2025-07-17T09:05:59.5110419Z a performance cost) by setting ``torch.backends.cudnn.deterministic = 2025-07-17T09:05:59.5110695Z True``. 2025-07-17T09:05:59.5110917Z Please see the notes on :doc:`/notes/randomness` for background. 2025-07-17T09:05:59.5111162Z 2025-07-17T09:05:59.5111517Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5111783Z 2025-07-17T09:05:59.5111926Z warnings.warn(msg) 2025-07-17T09:05:59.5112091Z 2025-07-17T09:05:59.5112302Z --- Parse Warning: 62 / 136 --- 2025-07-17T09:05:59.5112991Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RelaxedBernoulli in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/relaxed_bernoulli.py line=120. 2025-07-17T09:05:59.5113739Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5114016Z 2025-07-17T09:05:59.5114209Z Creates a RelaxedBernoulli distribution, parametrized by 2025-07-17T09:05:59.5114525Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2025-07-17T09:05:59.5114826Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2025-07-17T09:05:59.5115141Z so the values are in (0, 1), and has reparametrizable samples. 2025-07-17T09:05:59.5115369Z 2025-07-17T09:05:59.5115503Z Example:: 2025-07-17T09:05:59.5115639Z 2025-07-17T09:05:59.5115796Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-07-17T09:05:59.5116024Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2025-07-17T09:05:59.5116245Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2025-07-17T09:05:59.5116451Z >>> m.sample() 2025-07-17T09:05:59.5116644Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2025-07-17T09:05:59.5116847Z 2025-07-17T09:05:59.5116978Z Args: 2025-07-17T09:05:59.5117154Z temperature (Tensor): relaxation temperature 2025-07-17T09:05:59.5117411Z probs (Number, Tensor): the probability of sampling `1` 2025-07-17T09:05:59.5117692Z logits (Number, Tensor): the log-odds of sampling `1` 2025-07-17T09:05:59.5117907Z 2025-07-17T09:05:59.5118130Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5118399Z 2025-07-17T09:05:59.5118534Z warnings.warn(msg) 2025-07-17T09:05:59.5118689Z 2025-07-17T09:05:59.5118882Z --- Parse Warning: 63 / 136 --- 2025-07-17T09:05:59.5119623Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=LowRankMultivariateNormal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/lowrank_multivariate_normal.py line=56. 2025-07-17T09:05:59.5120417Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5120689Z 2025-07-17T09:05:59.5120940Z Creates a multivariate normal distribution with covariance matrix having a low-rank form 2025-07-17T09:05:59.5121298Z parameterized by :attr:`cov_factor` and :attr:`cov_diag`:: 2025-07-17T09:05:59.5121518Z 2025-07-17T09:05:59.5121699Z covariance_matrix = cov_factor @ cov_factor.T + cov_diag 2025-07-17T09:05:59.5121927Z 2025-07-17T09:05:59.5122064Z Example: 2025-07-17T09:05:59.5122239Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK) 2025-07-17T09:05:59.5122489Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-07-17T09:05:59.5122715Z >>> m = LowRankMultivariateNormal( 2025-07-17T09:05:59.5122964Z ... torch.zeros(2), torch.tensor([[1.0], [0.0]]), torch.ones(2) 2025-07-17T09:05:59.5123192Z ... ) 2025-07-17T09:05:59.5123445Z >>> m.sample() # normally distributed with mean=`[0,0]`, cov_factor=`[[1],[0]]`, cov_diag=`[1,1]` 2025-07-17T09:05:59.5123875Z tensor([-0.2102, -0.5429]) 2025-07-17T09:05:59.5124105Z 2025-07-17T09:05:59.5124228Z Args: 2025-07-17T09:05:59.5124447Z loc (Tensor): mean of the distribution with shape `batch_shape + event_shape` 2025-07-17T09:05:59.5124802Z cov_factor (Tensor): factor part of low-rank form of covariance matrix with shape 2025-07-17T09:05:59.5125093Z `batch_shape + event_shape + (rank,)` 2025-07-17T09:05:59.5125497Z cov_diag (Tensor): diagonal part of low-rank form of covariance matrix with shape 2025-07-17T09:05:59.5125783Z `batch_shape + event_shape` 2025-07-17T09:05:59.5125963Z 2025-07-17T09:05:59.5126089Z Note: 2025-07-17T09:05:59.5126324Z The computation for determinant and inverse of covariance matrix is avoided when 2025-07-17T09:05:59.5126680Z `cov_factor.shape[1] << cov_factor.shape[0]` thanks to `Woodbury matrix identity 2025-07-17T09:05:59.5127010Z `_ and 2025-07-17T09:05:59.5127364Z `matrix determinant lemma `_. 2025-07-17T09:05:59.5127744Z Thanks to these formulas, we just need to compute the determinant and inverse of 2025-07-17T09:05:59.5128030Z the small size "capacitance" matrix:: 2025-07-17T09:05:59.5128219Z 2025-07-17T09:05:59.5128401Z capacitance = I + cov_factor.T @ inv(cov_diag) @ cov_factor 2025-07-17T09:05:59.5128624Z 2025-07-17T09:05:59.5128846Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5129113Z 2025-07-17T09:05:59.5129250Z warnings.warn(msg) 2025-07-17T09:05:59.5129413Z 2025-07-17T09:05:59.5129614Z --- Parse Warning: 64 / 136 --- 2025-07-17T09:05:59.5130325Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RelaxedOneHotCategorical in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/relaxed_categorical.py line=109. 2025-07-17T09:05:59.5131098Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5131365Z 2025-07-17T09:05:59.5131574Z Creates a RelaxedOneHotCategorical distribution parametrized by 2025-07-17T09:05:59.5131885Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2025-07-17T09:05:59.5132206Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2025-07-17T09:05:59.5132505Z its samples are on simplex, and are reparametrizable. 2025-07-17T09:05:59.5132718Z 2025-07-17T09:05:59.5132845Z Example:: 2025-07-17T09:05:59.5132982Z 2025-07-17T09:05:59.5133136Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-07-17T09:05:59.5133383Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2025-07-17T09:05:59.5133629Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2025-07-17T09:05:59.5133847Z >>> m.sample() 2025-07-17T09:05:59.5134027Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2025-07-17T09:05:59.5134217Z 2025-07-17T09:05:59.5134340Z Args: 2025-07-17T09:05:59.5134505Z temperature (Tensor): relaxation temperature 2025-07-17T09:05:59.5134725Z probs (Tensor): event probabilities 2025-07-17T09:05:59.5134991Z logits (Tensor): unnormalized log probability for each event 2025-07-17T09:05:59.5135236Z 2025-07-17T09:05:59.5135450Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5135711Z 2025-07-17T09:05:59.5135845Z warnings.warn(msg) 2025-07-17T09:05:59.5136007Z 2025-07-17T09:05:59.5136195Z --- Parse Warning: 65 / 136 --- 2025-07-17T09:05:59.5136870Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MixtureSameFamily in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/mixture_same_family.py line=15. 2025-07-17T09:05:59.5137752Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5138023Z 2025-07-17T09:05:59.5138231Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2025-07-17T09:05:59.5138566Z distribution where all component are from different parameterizations of 2025-07-17T09:05:59.5139004Z the same distribution type. It is parameterized by a `Categorical` 2025-07-17T09:05:59.5139313Z "selecting distribution" (over `k` component) and a component 2025-07-17T09:05:59.5139618Z distribution, i.e., a `Distribution` with a rightmost batch shape 2025-07-17T09:05:59.5139903Z (equal to `[k]`) which indexes each (batch of) component. 2025-07-17T09:05:59.5140108Z 2025-07-17T09:05:59.5140243Z Examples:: 2025-07-17T09:05:59.5140386Z 2025-07-17T09:05:59.5140533Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T09:05:59.5140794Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2025-07-17T09:05:59.5141064Z >>> # weighted normal distributions 2025-07-17T09:05:59.5141143Z >>> mix = D.Categorical(torch.ones(5,)) 2025-07-17T09:05:59.5141247Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2025-07-17T09:05:59.5141326Z >>> gmm = MixtureSameFamily(mix, comp) 2025-07-17T09:05:59.5141383Z 2025-07-17T09:05:59.5141506Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2025-07-17T09:05:59.5141597Z >>> # weighted bivariate normal distributions 2025-07-17T09:05:59.5141669Z >>> mix = D.Categorical(torch.ones(5,)) 2025-07-17T09:05:59.5141750Z >>> comp = D.Independent(D.Normal( 2025-07-17T09:05:59.5141837Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2025-07-17T09:05:59.5141914Z >>> gmm = MixtureSameFamily(mix, comp) 2025-07-17T09:05:59.5141970Z 2025-07-17T09:05:59.5142088Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2025-07-17T09:05:59.5142208Z >>> # consisting of 5 random weighted bivariate normal distributions 2025-07-17T09:05:59.5142292Z >>> mix = D.Categorical(torch.rand(3,5)) 2025-07-17T09:05:59.5142367Z >>> comp = D.Independent(D.Normal( 2025-07-17T09:05:59.5142460Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2025-07-17T09:05:59.5142533Z >>> gmm = MixtureSameFamily(mix, comp) 2025-07-17T09:05:59.5142596Z 2025-07-17T09:05:59.5142655Z Args: 2025-07-17T09:05:59.5142783Z mixture_distribution: `torch.distributions.Categorical`-like 2025-07-17T09:05:59.5142904Z instance. Manages the probability of selecting component. 2025-07-17T09:05:59.5143012Z The number of categories must match the rightmost batch 2025-07-17T09:05:59.5143133Z dimension of the `component_distribution`. Must have either 2025-07-17T09:05:59.5143227Z scalar `batch_shape` or `batch_shape` matching 2025-07-17T09:05:59.5143320Z `component_distribution.batch_shape[:-1]` 2025-07-17T09:05:59.5143451Z component_distribution: `torch.distributions.Distribution`-like 2025-07-17T09:05:59.5143560Z instance. Right-most batch dimension indexes component. 2025-07-17T09:05:59.5143631Z 2025-07-17T09:05:59.5143798Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5143854Z 2025-07-17T09:05:59.5143933Z warnings.warn(msg) 2025-07-17T09:05:59.5143988Z 2025-07-17T09:05:59.5144117Z --- Parse Warning: 66 / 136 --- 2025-07-17T09:05:59.5144611Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=record_function in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py line=734. 2025-07-17T09:05:59.5144771Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5144996Z Context manager/function decorator that adds a label to a code block/function when running autograd profiler. 2025-07-17T09:05:59.5145247Z Label will only appear if CPU activity tracing is enabled. 2025-07-17T09:05:59.5145382Z 2025-07-17T09:05:59.5145477Z It is useful when tracing the code profile. 2025-07-17T09:05:59.5145532Z 2025-07-17T09:05:59.5145597Z Args: 2025-07-17T09:05:59.5145691Z name (str): Label assigned to the block of code. 2025-07-17T09:05:59.5145974Z node_id (int): ID of node, for distributed profiling. Unset in 2025-07-17T09:05:59.5146053Z non-distributed cases. 2025-07-17T09:05:59.5146106Z 2025-07-17T09:05:59.5146170Z Example: 2025-07-17T09:05:59.5146281Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD_PROFILER) 2025-07-17T09:05:59.5146370Z >>> x = torch.randn((1, 1), requires_grad=True) 2025-07-17T09:05:59.5146477Z >>> with torch.autograd.profiler.profile() as prof: 2025-07-17T09:05:59.5146551Z ... y = x**2 2025-07-17T09:05:59.5146656Z ... with torch.autograd.profiler.record_function( 2025-07-17T09:05:59.5146726Z ... "label-z" 2025-07-17T09:05:59.5146797Z ... ): # label the block 2025-07-17T09:05:59.5146868Z ... z = y**3 2025-07-17T09:05:59.5146932Z ... y.backward() 2025-07-17T09:05:59.5147010Z >>> # xdoctest: +IGNORE_WANT 2025-07-17T09:05:59.5147105Z >>> # NOTE: some columns were removed for brevity 2025-07-17T09:05:59.5147235Z >>> print(prof.key_averages().table(sort_by="self_cpu_time_total")) 2025-07-17T09:05:59.5147365Z ----------------------------------- --------------- --------------- --------------- 2025-07-17T09:05:59.5147487Z Name Self CPU total % CPU time avg Number of Calls 2025-07-17T09:05:59.5147606Z ----------------------------------- --------------- --------------- --------------- 2025-07-17T09:05:59.5147700Z pow 60.77% 47.470us 3 2025-07-17T09:05:59.5147783Z mul 21.73% 25.465us 2 2025-07-17T09:05:59.5147888Z PowBackward0 12.03% 121.891us 1 2025-07-17T09:05:59.5148024Z torch::autograd::AccumulateGrad 2.70% 6.324us 1 2025-07-17T09:05:59.5148114Z label-z 2.13% 12.421us 1 2025-07-17T09:05:59.5148228Z torch::autograd::GraphRoot 0.64% 1.503us 1 2025-07-17T09:05:59.5148349Z ----------------------------------- --------------- --------------- --------------- 2025-07-17T09:05:59.5148422Z Self CPU time total: 234.344us 2025-07-17T09:05:59.5148493Z CUDA time total: 0.000us 2025-07-17T09:05:59.5148549Z 2025-07-17T09:05:59.5148605Z 2025-07-17T09:05:59.5148787Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5148842Z 2025-07-17T09:05:59.5148918Z warnings.warn(msg) 2025-07-17T09:05:59.5148973Z 2025-07-17T09:05:59.5149104Z --- Parse Warning: 67 / 136 --- 2025-07-17T09:05:59.5149640Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DeviceMesh.__getitem__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py line=685. 2025-07-17T09:05:59.5149796Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-07-17T09:05:59.5149853Z 2025-07-17T09:05:59.5150023Z Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh. 2025-07-17T09:05:59.5150178Z The submesh created consists of the dimensions and the communicators indicated by 2025-07-17T09:05:59.5150248Z ``mesh_dim_names`` 2025-07-17T09:05:59.5150382Z 2025-07-17T09:05:59.5150499Z Args: 2025-07-17T09:05:59.5150639Z mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the 2025-07-17T09:05:59.5150760Z mesh dimension of the DeviceMesh to create the submesh for. 2025-07-17T09:05:59.5150822Z Returns: 2025-07-17T09:05:59.5150898Z A :class:`DeviceMesh` object 2025-07-17T09:05:59.5150956Z 2025-07-17T09:05:59.5151232Z The following program runs on each process/rank in an SPMD manner in a world size of 8. 2025-07-17T09:05:59.5151303Z In the first example: 2025-07-17T09:05:59.5151460Z Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]). 2025-07-17T09:05:59.5151610Z Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of DeviceMesh:([4, 5, 6, 7]). 2025-07-17T09:05:59.5151744Z Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of DeviceMesh:([0, 4]). 2025-07-17T09:05:59.5151881Z Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of DeviceMesh:([1, 5]). 2025-07-17T09:05:59.5152004Z Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of DeviceMesh:([2, 6]). 2025-07-17T09:05:59.5152136Z Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of DeviceMesh:([3, 7]). 2025-07-17T09:05:59.5152192Z 2025-07-17T09:05:59.5152259Z In the second example: 2025-07-17T09:05:59.5152416Z Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]). 2025-07-17T09:05:59.5152570Z Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]). 2025-07-17T09:05:59.5152718Z Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]). 2025-07-17T09:05:59.5152873Z Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]). 2025-07-17T09:05:59.5152926Z 2025-07-17T09:05:59.5152993Z Example:: 2025-07-17T09:05:59.5153048Z 2025-07-17T09:05:59.5153128Z >>> # xdoctest: +SKIP("no rank") 2025-07-17T09:05:59.5153234Z >>> from torch.distributed.device_mesh import DeviceMesh 2025-07-17T09:05:59.5153304Z >>> 2025-07-17T09:05:59.5153428Z >>> # Initialize a 2D device mesh as (2, 4) to represent the topology 2025-07-17T09:05:59.5153520Z >>> # of cross-host(dim 0), and within-host (dim 1). 2025-07-17T09:05:59.5153675Z >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2025-07-17T09:05:59.5153751Z >>> tp_mesh = mesh_2d["tp"] 2025-07-17T09:05:59.5153818Z >>> dp_mesh = mesh_2d["dp"] 2025-07-17T09:05:59.5153876Z >>> 2025-07-17T09:05:59.5153950Z >>> # Initialize a 3D mesh. 2025-07-17T09:05:59.5154115Z >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp")) 2025-07-17T09:05:59.5154299Z >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh. 2025-07-17T09:05:59.5154380Z >>> dp_cp_mesh = mesh_3d["dp", "cp"] 2025-07-17T09:05:59.5154459Z >>> cp_dp_mesh = mesh_3d["cp", "dp"] 2025-07-17T09:05:59.5154513Z 2025-07-17T09:05:59.5154893Z 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)) 2025-07-17T09:05:59.5154956Z 2025-07-17T09:05:59.5155100Z mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2025-07-17T09:05:59.5155173Z ^ 2025-07-17T09:05:59.5155243Z warnings.warn(msg) 2025-07-17T09:05:59.5155297Z 2025-07-17T09:05:59.5155431Z --- Parse Warning: 68 / 136 --- 2025-07-17T09:05:59.5155954Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=batch_isend_irecv in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=2706. 2025-07-17T09:05:59.5156238Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5156296Z 2025-07-17T09:05:59.5156453Z Send or Receive a batch of tensors asynchronously and return a list of requests. 2025-07-17T09:05:59.5156509Z 2025-07-17T09:05:59.5156757Z Process each of the operations in ``p2p_op_list`` and return the corresponding 2025-07-17T09:05:59.5156879Z requests. NCCL, Gloo, and UCC backend are currently supported. 2025-07-17T09:05:59.5156936Z 2025-07-17T09:05:59.5156997Z Args: 2025-07-17T09:05:59.5157139Z p2p_op_list: A list of point-to-point operations(type of each operator is 2025-07-17T09:05:59.5157286Z ``torch.distributed.P2POp``). The order of the isend/irecv in the list 2025-07-17T09:05:59.5157423Z matters and it needs to match with corresponding isend/irecv on the 2025-07-17T09:05:59.5157493Z remote end. 2025-07-17T09:05:59.5157550Z 2025-07-17T09:05:59.5157613Z Returns: 2025-07-17T09:05:59.5157764Z A list of distributed request objects returned by calling the corresponding 2025-07-17T09:05:59.5157839Z op in the op_list. 2025-07-17T09:05:59.5157894Z 2025-07-17T09:05:59.5157955Z Examples: 2025-07-17T09:05:59.5158028Z >>> # xdoctest: +SKIP("no rank") 2025-07-17T09:05:59.5158149Z >>> send_tensor = torch.arange(2, dtype=torch.float32) + 2 * rank 2025-07-17T09:05:59.5158244Z >>> recv_tensor = torch.randn(2, dtype=torch.float32) 2025-07-17T09:05:59.5158376Z >>> send_op = dist.P2POp(dist.isend, send_tensor, (rank + 1) % world_size) 2025-07-17T09:05:59.5158451Z >>> recv_op = dist.P2POp( 2025-07-17T09:05:59.5158574Z ... dist.irecv, recv_tensor, (rank - 1 + world_size) % world_size 2025-07-17T09:05:59.5158633Z ... ) 2025-07-17T09:05:59.5158733Z >>> reqs = batch_isend_irecv([send_op, recv_op]) 2025-07-17T09:05:59.5158797Z >>> for req in reqs: 2025-07-17T09:05:59.5158863Z >>> req.wait() 2025-07-17T09:05:59.5158927Z >>> recv_tensor 2025-07-17T09:05:59.5158994Z tensor([2, 3]) # Rank 0 2025-07-17T09:05:59.5159064Z tensor([0, 1]) # Rank 1 2025-07-17T09:05:59.5159120Z 2025-07-17T09:05:59.5159290Z .. note:: Note that when this API is used with the NCCL PG backend, users must set 2025-07-17T09:05:59.5159424Z the current GPU device with `torch.cuda.set_device`, otherwise it will 2025-07-17T09:05:59.5159503Z lead to unexpected hang issues. 2025-07-17T09:05:59.5159560Z 2025-07-17T09:05:59.5159686Z In addition, if this API is the first collective call in the ``group`` 2025-07-17T09:05:59.5159821Z passed to ``dist.P2POp``, all ranks of the ``group`` must participate in 2025-07-17T09:05:59.5159957Z this API call; otherwise, the behavior is undefined. If this API call is 2025-07-17T09:05:59.5160090Z not the first collective call in the ``group``, batched P2P operations 2025-07-17T09:05:59.5160218Z involving only a subset of ranks of the ``group`` are allowed. 2025-07-17T09:05:59.5160275Z 2025-07-17T09:05:59.5160426Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5160479Z 2025-07-17T09:05:59.5160550Z warnings.warn(msg) 2025-07-17T09:05:59.5160617Z 2025-07-17T09:05:59.5160752Z --- Parse Warning: 69 / 136 --- 2025-07-17T09:05:59.5161259Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=all_reduce in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=2838. 2025-07-17T09:05:59.5161416Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5161472Z 2025-07-17T09:05:59.5161712Z Reduces the tensor data across all machines in a way that all get the final result. 2025-07-17T09:05:59.5161821Z 2025-07-17T09:05:59.5161955Z After the call ``tensor`` is going to be bitwise identical in all processes. 2025-07-17T09:05:59.5162015Z 2025-07-17T09:05:59.5162090Z Complex tensors are supported. 2025-07-17T09:05:59.5162151Z 2025-07-17T09:05:59.5162208Z Args: 2025-07-17T09:05:59.5162442Z tensor (Tensor): Input and output of the collective. The function 2025-07-17T09:05:59.5162515Z operates in-place. 2025-07-17T09:05:59.5162603Z op (optional): One of the values from 2025-07-17T09:05:59.5162687Z ``torch.distributed.ReduceOp`` 2025-07-17T09:05:59.5162817Z enum. Specifies an operation used for element-wise reductions. 2025-07-17T09:05:59.5162965Z group (ProcessGroup, optional): The process group to work on. If None, 2025-07-17T09:05:59.5163060Z the default process group will be used. 2025-07-17T09:05:59.5163182Z async_op (bool, optional): Whether this op should be an async op 2025-07-17T09:05:59.5163244Z 2025-07-17T09:05:59.5163302Z Returns: 2025-07-17T09:05:59.5163394Z Async work handle, if async_op is set to True. 2025-07-17T09:05:59.5163491Z None, if not async_op or if not part of the group 2025-07-17T09:05:59.5163546Z 2025-07-17T09:05:59.5163620Z Examples: 2025-07-17T09:05:59.5163697Z >>> # xdoctest: +SKIP("no rank") 2025-07-17T09:05:59.5163792Z >>> # All tensors below are of torch.int64 type. 2025-07-17T09:05:59.5163875Z >>> # We have 2 process groups, 2 ranks. 2025-07-17T09:05:59.5163963Z >>> device = torch.device(f"cuda:{rank}") 2025-07-17T09:05:59.5164103Z >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank 2025-07-17T09:05:59.5164167Z >>> tensor 2025-07-17T09:05:59.5164241Z tensor([1, 2], device='cuda:0') # Rank 0 2025-07-17T09:05:59.5164322Z tensor([3, 4], device='cuda:1') # Rank 1 2025-07-17T09:05:59.5164410Z >>> dist.all_reduce(tensor, op=ReduceOp.SUM) 2025-07-17T09:05:59.5164473Z >>> tensor 2025-07-17T09:05:59.5164548Z tensor([4, 6], device='cuda:0') # Rank 0 2025-07-17T09:05:59.5164624Z tensor([4, 6], device='cuda:1') # Rank 1 2025-07-17T09:05:59.5164680Z 2025-07-17T09:05:59.5164776Z >>> # All tensors below are of torch.cfloat type. 2025-07-17T09:05:59.5164852Z >>> # We have 2 process groups, 2 ranks. 2025-07-17T09:05:59.5164932Z >>> tensor = torch.tensor( 2025-07-17T09:05:59.5165029Z ... [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device 2025-07-17T09:05:59.5165094Z ... ) + 2 * rank * (1 + 1j) 2025-07-17T09:05:59.5165154Z >>> tensor 2025-07-17T09:05:59.5165244Z tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0 2025-07-17T09:05:59.5165330Z tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1 2025-07-17T09:05:59.5165412Z >>> dist.all_reduce(tensor, op=ReduceOp.SUM) 2025-07-17T09:05:59.5165479Z >>> tensor 2025-07-17T09:05:59.5165559Z tensor([4.+4.j, 6.+6.j], device='cuda:0') # Rank 0 2025-07-17T09:05:59.5165644Z tensor([4.+4.j, 6.+6.j], device='cuda:1') # Rank 1 2025-07-17T09:05:59.5165702Z 2025-07-17T09:05:59.5165757Z 2025-07-17T09:05:59.5165904Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5165963Z 2025-07-17T09:05:59.5166033Z warnings.warn(msg) 2025-07-17T09:05:59.5166092Z 2025-07-17T09:05:59.5166214Z --- Parse Warning: 70 / 136 --- 2025-07-17T09:05:59.5166736Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=gather_object in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=3198. 2025-07-17T09:05:59.5166887Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5167023Z 2025-07-17T09:05:59.5167163Z Gathers picklable objects from the whole group in a single process. 2025-07-17T09:05:59.5167270Z 2025-07-17T09:05:59.5167412Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2025-07-17T09:05:59.5167507Z object must be picklable in order to be gathered. 2025-07-17T09:05:59.5167565Z 2025-07-17T09:05:59.5167623Z Args: 2025-07-17T09:05:59.5167839Z obj (Any): Input object. Must be picklable. 2025-07-17T09:05:59.5167967Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2025-07-17T09:05:59.5168094Z should be correctly sized as the size of the group for this 2025-07-17T09:05:59.5168223Z collective and will contain the output. Must be ``None`` on non-dst 2025-07-17T09:05:59.5168300Z ranks. (default is ``None``) 2025-07-17T09:05:59.5168490Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). 2025-07-17T09:05:59.5168613Z (If both ``dst`` and ``group_dst`` are None, default is global rank 0) 2025-07-17T09:05:59.5168761Z group: (ProcessGroup, optional): The process group to work on. If None, 2025-07-17T09:05:59.5168881Z the default process group will be used. Default is ``None``. 2025-07-17T09:05:59.5169084Z group_dst (int, optional): Destination rank on ``group``. Invalid to specify both ``dst`` and ``group_dst`` 2025-07-17T09:05:59.5169147Z 2025-07-17T09:05:59.5169206Z Returns: 2025-07-17T09:05:59.5169318Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2025-07-17T09:05:59.5169389Z output of the collective. 2025-07-17T09:05:59.5169452Z 2025-07-17T09:05:59.5169585Z .. note:: Note that this API differs slightly from the gather collective 2025-07-17T09:05:59.5169720Z since it does not provide an async_op handle and thus will be a blocking 2025-07-17T09:05:59.5169776Z call. 2025-07-17T09:05:59.5169833Z 2025-07-17T09:05:59.5169979Z .. note:: For NCCL-based processed groups, internal tensor representations 2025-07-17T09:05:59.5170107Z of objects must be moved to the GPU device before communication takes 2025-07-17T09:05:59.5170203Z place. In this case, the device used is given by 2025-07-17T09:05:59.5170340Z ``torch.cuda.current_device()`` and it is the user's responsibility to 2025-07-17T09:05:59.5170481Z ensure that this is set so that each rank has an individual GPU, via 2025-07-17T09:05:59.5170557Z ``torch.cuda.set_device()``. 2025-07-17T09:05:59.5170618Z 2025-07-17T09:05:59.5170680Z .. warning:: 2025-07-17T09:05:59.5170832Z Object collectives have a number of serious performance and scalability 2025-07-17T09:05:59.5170948Z limitations. See :ref:`object_collectives` for details. 2025-07-17T09:05:59.5171005Z 2025-07-17T09:05:59.5171063Z .. warning:: 2025-07-17T09:05:59.5171194Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2025-07-17T09:05:59.5171323Z known to be insecure. It is possible to construct malicious pickle data 2025-07-17T09:05:59.5171458Z which will execute arbitrary code during unpickling. Only call this 2025-07-17T09:05:59.5171535Z function with data you trust. 2025-07-17T09:05:59.5171597Z 2025-07-17T09:05:59.5171656Z .. warning:: 2025-07-17T09:05:59.5171795Z Calling :func:`gather_object` with GPU tensors is not well supported 2025-07-17T09:05:59.5171925Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2025-07-17T09:05:59.5172040Z pickled. Please consider using :func:`gather` instead. 2025-07-17T09:05:59.5172098Z 2025-07-17T09:05:59.5172157Z Example:: 2025-07-17T09:05:59.5172257Z >>> # xdoctest: +SKIP("need process group init") 2025-07-17T09:05:59.5172368Z >>> # Note: Process group initialization omitted on each rank. 2025-07-17T09:05:59.5172459Z >>> import torch.distributed as dist 2025-07-17T09:05:59.5172598Z >>> # Assumes world_size of 3. 2025-07-17T09:05:59.5172773Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2025-07-17T09:05:59.5172856Z >>> output = [None for _ in gather_objects] 2025-07-17T09:05:59.5172931Z >>> dist.gather_object( 2025-07-17T09:05:59.5173012Z ... gather_objects[dist.get_rank()], 2025-07-17T09:05:59.5173103Z ... output if dist.get_rank() == 0 else None, 2025-07-17T09:05:59.5173266Z ... dst=0 2025-07-17T09:05:59.5173331Z ... ) 2025-07-17T09:05:59.5173392Z >>> # On rank 0 2025-07-17T09:05:59.5173454Z >>> output 2025-07-17T09:05:59.5173518Z ['foo', 12, {1: 2}] 2025-07-17T09:05:59.5173575Z 2025-07-17T09:05:59.5173732Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5173791Z 2025-07-17T09:05:59.5173862Z warnings.warn(msg) 2025-07-17T09:05:59.5173916Z 2025-07-17T09:05:59.5174050Z --- Parse Warning: 71 / 136 --- 2025-07-17T09:05:59.5174569Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=all_gather in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=3794. 2025-07-17T09:05:59.5174729Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5174783Z 2025-07-17T09:05:59.5174881Z Gathers tensors from the whole group in a list. 2025-07-17T09:05:59.5174935Z 2025-07-17T09:05:59.5175024Z Complex and uneven sized tensors are supported. 2025-07-17T09:05:59.5175081Z 2025-07-17T09:05:59.5175146Z Args: 2025-07-17T09:05:59.5175264Z tensor_list (list[Tensor]): Output list. It should contain 2025-07-17T09:05:59.5175403Z correctly-sized tensors to be used for output of the collective. 2025-07-17T09:05:59.5175483Z Uneven sized tensors are supported. 2025-07-17T09:05:59.5175603Z tensor (Tensor): Tensor to be broadcast from current process. 2025-07-17T09:05:59.5175742Z group (ProcessGroup, optional): The process group to work on. If None, 2025-07-17T09:05:59.5175826Z the default process group will be used. 2025-07-17T09:05:59.5175949Z async_op (bool, optional): Whether this op should be an async op 2025-07-17T09:05:59.5176004Z 2025-07-17T09:05:59.5176068Z Returns: 2025-07-17T09:05:59.5176161Z Async work handle, if async_op is set to True. 2025-07-17T09:05:59.5176261Z None, if not async_op or if not part of the group 2025-07-17T09:05:59.5176318Z 2025-07-17T09:05:59.5176384Z Examples: 2025-07-17T09:05:59.5176472Z >>> # xdoctest: +SKIP("need process group init") 2025-07-17T09:05:59.5176560Z >>> # All tensors below are of torch.int64 dtype. 2025-07-17T09:05:59.5176640Z >>> # We have 2 process groups, 2 ranks. 2025-07-17T09:05:59.5176727Z >>> device = torch.device(f"cuda:{rank}") 2025-07-17T09:05:59.5176795Z >>> tensor_list = [ 2025-07-17T09:05:59.5176925Z ... torch.zeros(2, dtype=torch.int64, device=device) for _ in range(2) 2025-07-17T09:05:59.5176981Z ... ] 2025-07-17T09:05:59.5177044Z >>> tensor_list 2025-07-17T09:05:59.5177168Z [tensor([0, 0], device='cuda:0'), tensor([0, 0], device='cuda:0')] # Rank 0 2025-07-17T09:05:59.5177302Z [tensor([0, 0], device='cuda:1'), tensor([0, 0], device='cuda:1')] # Rank 1 2025-07-17T09:05:59.5177442Z >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank 2025-07-17T09:05:59.5177557Z >>> tensor 2025-07-17T09:05:59.5177634Z tensor([1, 2], device='cuda:0') # Rank 0 2025-07-17T09:05:59.5177720Z tensor([3, 4], device='cuda:1') # Rank 1 2025-07-17T09:05:59.5177809Z >>> dist.all_gather(tensor_list, tensor) 2025-07-17T09:05:59.5177898Z >>> tensor_list 2025-07-17T09:05:59.5178030Z [tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0')] # Rank 0 2025-07-17T09:05:59.5178207Z [tensor([1, 2], device='cuda:1'), tensor([3, 4], device='cuda:1')] # Rank 1 2025-07-17T09:05:59.5178322Z 2025-07-17T09:05:59.5178416Z >>> # All tensors below are of torch.cfloat dtype. 2025-07-17T09:05:59.5178491Z >>> # We have 2 process groups, 2 ranks. 2025-07-17T09:05:59.5178556Z >>> tensor_list = [ 2025-07-17T09:05:59.5178799Z ... torch.zeros(2, dtype=torch.cfloat, device=device) for _ in range(2) 2025-07-17T09:05:59.5178855Z ... ] 2025-07-17T09:05:59.5178933Z >>> tensor_list 2025-07-17T09:05:59.5179085Z [tensor([0.+0.j, 0.+0.j], device='cuda:0'), tensor([0.+0.j, 0.+0.j], device='cuda:0')] # Rank 0 2025-07-17T09:05:59.5179239Z [tensor([0.+0.j, 0.+0.j], device='cuda:1'), tensor([0.+0.j, 0.+0.j], device='cuda:1')] # Rank 1 2025-07-17T09:05:59.5179314Z >>> tensor = torch.tensor( 2025-07-17T09:05:59.5179413Z ... [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device 2025-07-17T09:05:59.5179482Z ... ) + 2 * rank * (1 + 1j) 2025-07-17T09:05:59.5179559Z >>> tensor 2025-07-17T09:05:59.5179653Z tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0 2025-07-17T09:05:59.5179749Z tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1 2025-07-17T09:05:59.5179858Z >>> dist.all_gather(tensor_list, tensor) 2025-07-17T09:05:59.5179925Z >>> tensor_list 2025-07-17T09:05:59.5180067Z [tensor([1.+1.j, 2.+2.j], device='cuda:0'), tensor([3.+3.j, 4.+4.j], device='cuda:0')] # Rank 0 2025-07-17T09:05:59.5180199Z [tensor([1.+1.j, 2.+2.j], device='cuda:1'), tensor([3.+3.j, 4.+4.j], device='cuda:1')] # Rank 1 2025-07-17T09:05:59.5180259Z 2025-07-17T09:05:59.5180315Z 2025-07-17T09:05:59.5180482Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5180536Z 2025-07-17T09:05:59.5180609Z warnings.warn(msg) 2025-07-17T09:05:59.5180664Z 2025-07-17T09:05:59.5180796Z --- Parse Warning: 72 / 136 --- 2025-07-17T09:05:59.5181319Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=all_to_all_single in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=4500. 2025-07-17T09:05:59.5181477Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5181533Z 2025-07-17T09:05:59.5181690Z Split input tensor and then scatter the split list to all processes in a group. 2025-07-17T09:05:59.5181745Z 2025-07-17T09:05:59.5181902Z Later the received tensors are concatenated from all the processes in the group 2025-07-17T09:05:59.5181983Z and returned as a single output tensor. 2025-07-17T09:05:59.5182043Z 2025-07-17T09:05:59.5182117Z Complex tensors are supported. 2025-07-17T09:05:59.5182170Z 2025-07-17T09:05:59.5182228Z Args: 2025-07-17T09:05:59.5182337Z output (Tensor): Gathered concatenated output tensor. 2025-07-17T09:05:59.5182427Z input (Tensor): Input tensor to scatter. 2025-07-17T09:05:59.5182559Z output_split_sizes: (list[Int], optional): Output split sizes for dim 0 2025-07-17T09:05:59.5182690Z if specified None or empty, dim 0 of ``output`` tensor must divide 2025-07-17T09:05:59.5182765Z equally by ``world_size``. 2025-07-17T09:05:59.5182906Z input_split_sizes: (list[Int], optional): Input split sizes for dim 0 2025-07-17T09:05:59.5183025Z if specified None or empty, dim 0 of ``input`` tensor must divide 2025-07-17T09:05:59.5183108Z equally by ``world_size``. 2025-07-17T09:05:59.5183250Z group (ProcessGroup, optional): The process group to work on. If None, 2025-07-17T09:05:59.5183341Z the default process group will be used. 2025-07-17T09:05:59.5183463Z async_op (bool, optional): Whether this op should be an async op. 2025-07-17T09:05:59.5183527Z 2025-07-17T09:05:59.5183660Z Returns: 2025-07-17T09:05:59.5183755Z Async work handle, if async_op is set to True. 2025-07-17T09:05:59.5183898Z None, if not async_op or if not part of the group. 2025-07-17T09:05:59.5183963Z 2025-07-17T09:05:59.5184029Z .. warning:: 2025-07-17T09:05:59.5184148Z `all_to_all_single` is experimental and subject to change. 2025-07-17T09:05:59.5184209Z 2025-07-17T09:05:59.5184268Z Examples: 2025-07-17T09:05:59.5184457Z >>> # xdoctest: +SKIP("Undefined rank") 2025-07-17T09:05:59.5184538Z >>> input = torch.arange(4) + rank * 4 2025-07-17T09:05:59.5184603Z >>> input 2025-07-17T09:05:59.5184673Z tensor([0, 1, 2, 3]) # Rank 0 2025-07-17T09:05:59.5184745Z tensor([4, 5, 6, 7]) # Rank 1 2025-07-17T09:05:59.5184810Z tensor([8, 9, 10, 11]) # Rank 2 2025-07-17T09:05:59.5184883Z tensor([12, 13, 14, 15]) # Rank 3 2025-07-17T09:05:59.5184975Z >>> output = torch.empty([4], dtype=torch.int64) 2025-07-17T09:05:59.5185068Z >>> dist.all_to_all_single(output, input) 2025-07-17T09:05:59.5185130Z >>> output 2025-07-17T09:05:59.5185202Z tensor([0, 4, 8, 12]) # Rank 0 2025-07-17T09:05:59.5185331Z tensor([1, 5, 9, 13]) # Rank 1 2025-07-17T09:05:59.5185399Z tensor([2, 6, 10, 14]) # Rank 2 2025-07-17T09:05:59.5185464Z tensor([3, 7, 11, 15]) # Rank 3 2025-07-17T09:05:59.5185521Z 2025-07-17T09:05:59.5185633Z >>> # Essentially, it is similar to following operation: 2025-07-17T09:05:59.5185725Z >>> scatter_list = list(input.chunk(world_size)) 2025-07-17T09:05:59.5185815Z >>> gather_list = list(output.chunk(world_size)) 2025-07-17T09:05:59.5185888Z >>> for i in range(world_size): 2025-07-17T09:05:59.5186045Z >>> dist.scatter(gather_list[i], scatter_list if i == rank else [], src = i) 2025-07-17T09:05:59.5186102Z 2025-07-17T09:05:59.5186191Z >>> # Another example with uneven split 2025-07-17T09:05:59.5186254Z >>> input 2025-07-17T09:05:59.5186353Z tensor([0, 1, 2, 3, 4, 5]) # Rank 0 2025-07-17T09:05:59.5186449Z tensor([10, 11, 12, 13, 14, 15, 16, 17, 18]) # Rank 1 2025-07-17T09:05:59.5186542Z tensor([20, 21, 22, 23, 24]) # Rank 2 2025-07-17T09:05:59.5186640Z tensor([30, 31, 32, 33, 34, 35, 36]) # Rank 3 2025-07-17T09:05:59.5186717Z >>> input_splits 2025-07-17T09:05:59.5186797Z [2, 2, 1, 1] # Rank 0 2025-07-17T09:05:59.5186872Z [3, 2, 2, 2] # Rank 1 2025-07-17T09:05:59.5186944Z [2, 1, 1, 1] # Rank 2 2025-07-17T09:05:59.5187023Z [2, 2, 2, 1] # Rank 3 2025-07-17T09:05:59.5187091Z >>> output_splits 2025-07-17T09:05:59.5187173Z [2, 3, 2, 2] # Rank 0 2025-07-17T09:05:59.5187252Z [2, 2, 1, 2] # Rank 1 2025-07-17T09:05:59.5187333Z [1, 2, 1, 2] # Rank 2 2025-07-17T09:05:59.5187411Z [1, 2, 1, 1] # Rank 3 2025-07-17T09:05:59.5187484Z >>> output = ... 2025-07-17T09:05:59.5187609Z >>> dist.all_to_all_single(output, input, output_splits, input_splits) 2025-07-17T09:05:59.5187674Z >>> output 2025-07-17T09:05:59.5187766Z tensor([ 0, 1, 10, 11, 12, 20, 21, 30, 31]) # Rank 0 2025-07-17T09:05:59.5187856Z tensor([ 2, 3, 13, 14, 22, 32, 33]) # Rank 1 2025-07-17T09:05:59.5187950Z tensor([ 4, 15, 16, 23, 34, 35]) # Rank 2 2025-07-17T09:05:59.5188123Z tensor([ 5, 17, 18, 24, 36]) # Rank 3 2025-07-17T09:05:59.5188277Z 2025-07-17T09:05:59.5188333Z 2025-07-17T09:05:59.5188440Z >>> # Another example with tensors of torch.cfloat type. 2025-07-17T09:05:59.5188512Z >>> input = torch.tensor( 2025-07-17T09:05:59.5188605Z ... [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat 2025-07-17T09:05:59.5188671Z ... ) + 4 * rank * (1 + 1j) 2025-07-17T09:05:59.5188990Z >>> input 2025-07-17T09:05:59.5189099Z tensor([1+1j, 2+2j, 3+3j, 4+4j]) # Rank 0 2025-07-17T09:05:59.5189209Z tensor([5+5j, 6+6j, 7+7j, 8+8j]) # Rank 1 2025-07-17T09:05:59.5189320Z tensor([9+9j, 10+10j, 11+11j, 12+12j]) # Rank 2 2025-07-17T09:05:59.5189436Z tensor([13+13j, 14+14j, 15+15j, 16+16j]) # Rank 3 2025-07-17T09:05:59.5189525Z >>> output = torch.empty([4], dtype=torch.int64) 2025-07-17T09:05:59.5189618Z >>> dist.all_to_all_single(output, input) 2025-07-17T09:05:59.5189682Z >>> output 2025-07-17T09:05:59.5189789Z tensor([1+1j, 5+5j, 9+9j, 13+13j]) # Rank 0 2025-07-17T09:05:59.5189892Z tensor([2+2j, 6+6j, 10+10j, 14+14j]) # Rank 1 2025-07-17T09:05:59.5190007Z tensor([3+3j, 7+7j, 11+11j, 15+15j]) # Rank 2 2025-07-17T09:05:59.5190108Z tensor([4+4j, 8+8j, 12+12j, 16+16j]) # Rank 3 2025-07-17T09:05:59.5190175Z 2025-07-17T09:05:59.5190321Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5190378Z 2025-07-17T09:05:59.5190456Z warnings.warn(msg) 2025-07-17T09:05:59.5190513Z 2025-07-17T09:05:59.5190653Z --- Parse Warning: 73 / 136 --- 2025-07-17T09:05:59.5191161Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=all_to_all in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=4642. 2025-07-17T09:05:59.5191326Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5191383Z 2025-07-17T09:05:59.5191603Z Scatters list of input tensors to all processes in a group and return gathered list of tensors in output list. 2025-07-17T09:05:59.5191661Z 2025-07-17T09:05:59.5191740Z Complex tensors are supported. 2025-07-17T09:05:59.5191797Z 2025-07-17T09:05:59.5191866Z Args: 2025-07-17T09:05:59.5192003Z output_tensor_list (list[Tensor]): List of tensors to be gathered one 2025-07-17T09:05:59.5192068Z per rank. 2025-07-17T09:05:59.5192204Z input_tensor_list (list[Tensor]): List of tensors to scatter one per rank. 2025-07-17T09:05:59.5192350Z group (ProcessGroup, optional): The process group to work on. If None, 2025-07-17T09:05:59.5192437Z the default process group will be used. 2025-07-17T09:05:59.5192566Z async_op (bool, optional): Whether this op should be an async op. 2025-07-17T09:05:59.5192624Z 2025-07-17T09:05:59.5192689Z Returns: 2025-07-17T09:05:59.5192782Z Async work handle, if async_op is set to True. 2025-07-17T09:05:59.5192876Z None, if not async_op or if not part of the group. 2025-07-17T09:05:59.5192943Z 2025-07-17T09:05:59.5193009Z .. warning:: 2025-07-17T09:05:59.5193116Z `all_to_all` is experimental and subject to change. 2025-07-17T09:05:59.5193173Z 2025-07-17T09:05:59.5193235Z Examples: 2025-07-17T09:05:59.5193311Z >>> # xdoctest: +SKIP("Undefined rank") 2025-07-17T09:05:59.5193388Z >>> input = torch.arange(4) + rank * 4 2025-07-17T09:05:59.5193459Z >>> input = list(input.chunk(4)) 2025-07-17T09:05:59.5193519Z >>> input 2025-07-17T09:05:59.5193625Z [tensor([0]), tensor([1]), tensor([2]), tensor([3])] # Rank 0 2025-07-17T09:05:59.5193813Z [tensor([4]), tensor([5]), tensor([6]), tensor([7])] # Rank 1 2025-07-17T09:05:59.5193962Z [tensor([8]), tensor([9]), tensor([10]), tensor([11])] # Rank 2 2025-07-17T09:05:59.5194066Z [tensor([12]), tensor([13]), tensor([14]), tensor([15])] # Rank 3 2025-07-17T09:05:59.5194213Z >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4)) 2025-07-17T09:05:59.5194412Z >>> dist.all_to_all(output, input) 2025-07-17T09:05:59.5194473Z >>> output 2025-07-17T09:05:59.5194576Z [tensor([0]), tensor([4]), tensor([8]), tensor([12])] # Rank 0 2025-07-17T09:05:59.5194673Z [tensor([1]), tensor([5]), tensor([9]), tensor([13])] # Rank 1 2025-07-17T09:05:59.5194769Z [tensor([2]), tensor([6]), tensor([10]), tensor([14])] # Rank 2 2025-07-17T09:05:59.5194870Z [tensor([3]), tensor([7]), tensor([11]), tensor([15])] # Rank 3 2025-07-17T09:05:59.5194925Z 2025-07-17T09:05:59.5195033Z >>> # Essentially, it is similar to following operation: 2025-07-17T09:05:59.5195106Z >>> scatter_list = input 2025-07-17T09:05:59.5195175Z >>> gather_list = output 2025-07-17T09:05:59.5195244Z >>> for i in range(world_size): 2025-07-17T09:05:59.5195389Z >>> dist.scatter(gather_list[i], scatter_list if i == rank else [], src=i) 2025-07-17T09:05:59.5195444Z 2025-07-17T09:05:59.5195506Z >>> input 2025-07-17T09:05:59.5195600Z tensor([0, 1, 2, 3, 4, 5]) # Rank 0 2025-07-17T09:05:59.5195694Z tensor([10, 11, 12, 13, 14, 15, 16, 17, 18]) # Rank 1 2025-07-17T09:05:59.5195783Z tensor([20, 21, 22, 23, 24]) # Rank 2 2025-07-17T09:05:59.5195878Z tensor([30, 31, 32, 33, 34, 35, 36]) # Rank 3 2025-07-17T09:05:59.5195939Z >>> input_splits 2025-07-17T09:05:59.5196022Z [2, 2, 1, 1] # Rank 0 2025-07-17T09:05:59.5196098Z [3, 2, 2, 2] # Rank 1 2025-07-17T09:05:59.5196175Z [2, 1, 1, 1] # Rank 2 2025-07-17T09:05:59.5196248Z [2, 2, 2, 1] # Rank 3 2025-07-17T09:05:59.5196319Z >>> output_splits 2025-07-17T09:05:59.5196398Z [2, 3, 2, 2] # Rank 0 2025-07-17T09:05:59.5196480Z [2, 2, 1, 2] # Rank 1 2025-07-17T09:05:59.5196553Z [1, 2, 1, 2] # Rank 2 2025-07-17T09:05:59.5196635Z [1, 2, 1, 1] # Rank 3 2025-07-17T09:05:59.5196720Z >>> input = list(input.split(input_splits)) 2025-07-17T09:05:59.5196787Z >>> input 2025-07-17T09:05:59.5196918Z [tensor([0, 1]), tensor([2, 3]), tensor([4]), tensor([5])] # Rank 0 2025-07-17T09:05:59.5197040Z [tensor([10, 11, 12]), tensor([13, 14]), tensor([15, 16]), tensor([17, 18])] # Rank 1 2025-07-17T09:05:59.5197163Z [tensor([20, 21]), tensor([22]), tensor([23]), tensor([24])] # Rank 2 2025-07-17T09:05:59.5197277Z [tensor([30, 31]), tensor([32, 33]), tensor([34, 35]), tensor([36])] # Rank 3 2025-07-17T09:05:59.5197348Z >>> output = ... 2025-07-17T09:05:59.5197422Z >>> dist.all_to_all(output, input) 2025-07-17T09:05:59.5197490Z >>> output 2025-07-17T09:05:59.5197602Z [tensor([0, 1]), tensor([10, 11, 12]), tensor([20, 21]), tensor([30, 31])] # Rank 0 2025-07-17T09:05:59.5197728Z [tensor([2, 3]), tensor([13, 14]), tensor([22]), tensor([32, 33])] # Rank 1 2025-07-17T09:05:59.5197846Z [tensor([4]), tensor([15, 16]), tensor([23]), tensor([34, 35])] # Rank 2 2025-07-17T09:05:59.5198046Z [tensor([5]), tensor([17, 18]), tensor([24]), tensor([36])] # Rank 3 2025-07-17T09:05:59.5198157Z 2025-07-17T09:05:59.5198269Z >>> # Another example with tensors of torch.cfloat type. 2025-07-17T09:05:59.5198338Z >>> input = torch.tensor( 2025-07-17T09:05:59.5198435Z ... [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat 2025-07-17T09:05:59.5198505Z ... ) + 4 * rank * (1 + 1j) 2025-07-17T09:05:59.5198694Z >>> input = list(input.chunk(4)) 2025-07-17T09:05:59.5198752Z >>> input 2025-07-17T09:05:59.5198883Z [tensor([1+1j]), tensor([2+2j]), tensor([3+3j]), tensor([4+4j])] # Rank 0 2025-07-17T09:05:59.5199004Z [tensor([5+5j]), tensor([6+6j]), tensor([7+7j]), tensor([8+8j])] # Rank 1 2025-07-17T09:05:59.5199130Z [tensor([9+9j]), tensor([10+10j]), tensor([11+11j]), tensor([12+12j])] # Rank 2 2025-07-17T09:05:59.5199260Z [tensor([13+13j]), tensor([14+14j]), tensor([15+15j]), tensor([16+16j])] # Rank 3 2025-07-17T09:05:59.5199388Z >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4)) 2025-07-17T09:05:59.5199459Z >>> dist.all_to_all(output, input) 2025-07-17T09:05:59.5199524Z >>> output 2025-07-17T09:05:59.5199646Z [tensor([1+1j]), tensor([5+5j]), tensor([9+9j]), tensor([13+13j])] # Rank 0 2025-07-17T09:05:59.5199776Z [tensor([2+2j]), tensor([6+6j]), tensor([10+10j]), tensor([14+14j])] # Rank 1 2025-07-17T09:05:59.5199900Z [tensor([3+3j]), tensor([7+7j]), tensor([11+11j]), tensor([15+15j])] # Rank 2 2025-07-17T09:05:59.5200025Z [tensor([4+4j]), tensor([8+8j]), tensor([12+12j]), tensor([16+16j])] # Rank 3 2025-07-17T09:05:59.5200084Z 2025-07-17T09:05:59.5200139Z 2025-07-17T09:05:59.5200295Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5200352Z 2025-07-17T09:05:59.5200430Z warnings.warn(msg) 2025-07-17T09:05:59.5200493Z 2025-07-17T09:05:59.5200629Z --- Parse Warning: 74 / 136 --- 2025-07-17T09:05:59.5201105Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=__doc__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/launch.py line=2. 2025-07-17T09:05:59.5201265Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5201327Z 2025-07-17T09:05:59.5201414Z Module ``torch.distributed.launch``. 2025-07-17T09:05:59.5201470Z 2025-07-17T09:05:59.5201625Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2025-07-17T09:05:59.5201726Z training processes on each of the training nodes. 2025-07-17T09:05:59.5201788Z 2025-07-17T09:05:59.5201854Z .. warning:: 2025-07-17T09:05:59.5201916Z 2025-07-17T09:05:59.5202070Z This module is going to be deprecated in favor of :ref:`torchrun `. 2025-07-17T09:05:59.5202132Z 2025-07-17T09:05:59.5202278Z The utility can be used for single-node distributed training, in which one or 2025-07-17T09:05:59.5202430Z more processes per node will be spawned. The utility can be used for either 2025-07-17T09:05:59.5202566Z CPU training or GPU training. If the utility is used for GPU training, 2025-07-17T09:05:59.5202714Z each distributed process will be operating on a single GPU. This can achieve 2025-07-17T09:05:59.5202860Z well-improved single-node training performance. It can also be used in 2025-07-17T09:05:59.5203027Z multi-node distributed training, by spawning up multiple processes on each node 2025-07-17T09:05:59.5203173Z for well-improved multi-node distributed training performance as well. 2025-07-17T09:05:59.5203315Z This will especially be beneficial for systems with multiple Infiniband 2025-07-17T09:05:59.5203472Z interfaces that have direct-GPU support, since all of them can be utilized for 2025-07-17T09:05:59.5203636Z aggregated communication bandwidth. 2025-07-17T09:05:59.5203750Z 2025-07-17T09:05:59.5203885Z In both cases of single-node distributed training or multi-node distributed 2025-07-17T09:05:59.5204031Z training, this utility will launch the given number of processes per node 2025-07-17T09:05:59.5204164Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2025-07-17T09:05:59.5204404Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2025-07-17T09:05:59.5204532Z and each process will be operating on a single GPU from *GPU 0 to 2025-07-17T09:05:59.5204615Z GPU (nproc_per_node - 1)*. 2025-07-17T09:05:59.5204673Z 2025-07-17T09:05:59.5204748Z **How to use this module:** 2025-07-17T09:05:59.5204804Z 2025-07-17T09:05:59.5204901Z 1. Single-Node multi-process distributed training 2025-07-17T09:05:59.5204957Z 2025-07-17T09:05:59.5205027Z :: 2025-07-17T09:05:59.5205082Z 2025-07-17T09:05:59.5205225Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-07-17T09:05:59.5205342Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2025-07-17T09:05:59.5205420Z arguments of your training script) 2025-07-17T09:05:59.5205488Z 2025-07-17T09:05:59.5205617Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2025-07-17T09:05:59.5205677Z 2025-07-17T09:05:59.5205737Z 2025-07-17T09:05:59.5205837Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2025-07-17T09:05:59.5205894Z 2025-07-17T09:05:59.5205962Z :: 2025-07-17T09:05:59.5206021Z 2025-07-17T09:05:59.5206163Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-07-17T09:05:59.5206267Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2025-07-17T09:05:59.5206397Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2025-07-17T09:05:59.5206500Z and all other arguments of your training script) 2025-07-17T09:05:59.5206565Z 2025-07-17T09:05:59.5206628Z Node 2: 2025-07-17T09:05:59.5206680Z 2025-07-17T09:05:59.5206748Z :: 2025-07-17T09:05:59.5206800Z 2025-07-17T09:05:59.5206941Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-07-17T09:05:59.5207037Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2025-07-17T09:05:59.5207159Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2025-07-17T09:05:59.5207249Z and all other arguments of your training script) 2025-07-17T09:05:59.5207314Z 2025-07-17T09:05:59.5207419Z 3. To look up what optional arguments this module offers: 2025-07-17T09:05:59.5207481Z 2025-07-17T09:05:59.5207540Z :: 2025-07-17T09:05:59.5207600Z 2025-07-17T09:05:59.5207687Z python -m torch.distributed.launch --help 2025-07-17T09:05:59.5207748Z 2025-07-17T09:05:59.5207808Z 2025-07-17T09:05:59.5207875Z **Important Notices:** 2025-07-17T09:05:59.5207941Z 2025-07-17T09:05:59.5208061Z 1. This utility and multi-process distributed (single-node or 2025-07-17T09:05:59.5208218Z multi-node) GPU training currently only achieves the best performance using 2025-07-17T09:05:59.5208367Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2025-07-17T09:05:59.5208442Z use for GPU training. 2025-07-17T09:05:59.5208506Z 2025-07-17T09:05:59.5208643Z 2. In your training program, you must parse the command-line argument: 2025-07-17T09:05:59.5208780Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2025-07-17T09:05:59.5208915Z If your training program uses GPUs, you should ensure that your code only 2025-07-17T09:05:59.5209035Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2025-07-17T09:05:59.5209095Z 2025-07-17T09:05:59.5209168Z Parsing the local_rank argument 2025-07-17T09:05:59.5209315Z 2025-07-17T09:05:59.5209419Z :: 2025-07-17T09:05:59.5209475Z 2025-07-17T09:05:59.5209547Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.5209612Z >>> import argparse 2025-07-17T09:05:59.5209710Z >>> parser = argparse.ArgumentParser() 2025-07-17T09:05:59.5209831Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2025-07-17T09:05:59.5209914Z >>> args = parser.parse_args() 2025-07-17T09:05:59.5210072Z 2025-07-17T09:05:59.5210163Z Set your device to local rank using either 2025-07-17T09:05:59.5210218Z 2025-07-17T09:05:59.5210281Z :: 2025-07-17T09:05:59.5210336Z 2025-07-17T09:05:59.5210463Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2025-07-17T09:05:59.5210516Z 2025-07-17T09:05:59.5210580Z or 2025-07-17T09:05:59.5210634Z 2025-07-17T09:05:59.5210692Z :: 2025-07-17T09:05:59.5210754Z 2025-07-17T09:05:59.5210844Z >>> with torch.cuda.device(args.local_rank): 2025-07-17T09:05:59.5210919Z >>> # your code to run 2025-07-17T09:05:59.5210980Z >>> ... 2025-07-17T09:05:59.5211043Z 2025-07-17T09:05:59.5211117Z .. versionchanged:: 2.0.0 2025-07-17T09:05:59.5211176Z 2025-07-17T09:05:59.5211326Z The launcher will passes the ``--local-rank=`` argument to your script. 2025-07-17T09:05:59.5211476Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2025-07-17T09:05:59.5211571Z previously used underscored ``--local_rank``. 2025-07-17T09:05:59.5211626Z 2025-07-17T09:05:59.5211765Z For backward compatibility, it may be necessary for users to handle both 2025-07-17T09:05:59.5211929Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2025-07-17T09:05:59.5212060Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2025-07-17T09:05:59.5212210Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2025-07-17T09:05:59.5212357Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2025-07-17T09:05:59.5212455Z including ``"--local-rank"`` should be sufficient. 2025-07-17T09:05:59.5212511Z 2025-07-17T09:05:59.5212647Z 3. In your training program, you are supposed to call the following function 2025-07-17T09:05:59.5212801Z at the beginning to start the distributed backend. It is strongly recommended 2025-07-17T09:05:59.5212934Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2025-07-17T09:05:59.5213060Z but ``env://`` is the one that is officially supported by this module. 2025-07-17T09:05:59.5213115Z 2025-07-17T09:05:59.5213179Z :: 2025-07-17T09:05:59.5213235Z 2025-07-17T09:05:59.5213366Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2025-07-17T09:05:59.5213454Z >>> init_method='env://') 2025-07-17T09:05:59.5213518Z 2025-07-17T09:05:59.5213655Z 4. In your training program, you can either use regular distributed functions 2025-07-17T09:05:59.5213805Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2025-07-17T09:05:59.5213934Z training program uses GPUs for training and you would like to use 2025-07-17T09:05:59.5214061Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2025-07-17T09:05:59.5214139Z here is how to configure it. 2025-07-17T09:05:59.5214200Z 2025-07-17T09:05:59.5214259Z :: 2025-07-17T09:05:59.5214316Z 2025-07-17T09:05:59.5214433Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2025-07-17T09:05:59.5214524Z >>> device_ids=[args.local_rank], 2025-07-17T09:05:59.5214618Z >>> output_device=args.local_rank) 2025-07-17T09:05:59.5214672Z 2025-07-17T09:05:59.5214818Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2025-07-17T09:05:59.5215068Z that your code will be operating on. This is generally the local rank of the 2025-07-17T09:05:59.5215214Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2025-07-17T09:05:59.5215343Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2025-07-17T09:05:59.5215414Z utility 2025-07-17T09:05:59.5215470Z 2025-07-17T09:05:59.5215725Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2025-07-17T09:05:59.5215858Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2025-07-17T09:05:59.5215988Z ``--use-env=True``. You must adjust the subprocess example above to replace 2025-07-17T09:05:59.5216104Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2025-07-17T09:05:59.5216220Z will not pass ``--local-rank`` when you specify this flag. 2025-07-17T09:05:59.5216277Z 2025-07-17T09:05:59.5216344Z .. warning:: 2025-07-17T09:05:59.5216405Z 2025-07-17T09:05:59.5216538Z ``local_rank`` is NOT globally unique: it is only unique per process 2025-07-17T09:05:59.5216660Z on a machine. Thus, don't use it to decide if you should, e.g., 2025-07-17T09:05:59.5220741Z write to a networked filesystem. See 2025-07-17T09:05:59.5220915Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2025-07-17T09:05:59.5221045Z how things can go wrong if you don't do this correctly. 2025-07-17T09:05:59.5221103Z 2025-07-17T09:05:59.5221156Z 2025-07-17T09:05:59.5221216Z 2025-07-17T09:05:59.5221272Z 2025-07-17T09:05:59.5221434Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5221493Z 2025-07-17T09:05:59.5221570Z warnings.warn(msg) 2025-07-17T09:05:59.5221631Z 2025-07-17T09:05:59.5221789Z --- Parse Warning: 75 / 136 --- 2025-07-17T09:05:59.5222410Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ZeroRedundancyOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py line=284. 2025-07-17T09:05:59.5222576Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5222633Z 2025-07-17T09:05:59.5222881Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2025-07-17T09:05:59.5222937Z 2025-07-17T09:05:59.5223099Z The sharing is done as described by `ZeRO `_. 2025-07-17T09:05:59.5223152Z 2025-07-17T09:05:59.5223252Z The local optimizer instance in each rank is only 2025-07-17T09:05:59.5223401Z responsible for updating approximately ``1 / world_size`` parameters and 2025-07-17T09:05:59.5223532Z hence only needs to keep ``1 / world_size`` optimizer states. After 2025-07-17T09:05:59.5223681Z parameters are updated locally, each rank will broadcast its parameters to 2025-07-17T09:05:59.5223811Z all other peers to keep all model replicas in the same state. 2025-07-17T09:05:59.5223934Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2025-07-17T09:05:59.5224091Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2025-07-17T09:05:59.5224166Z memory consumption. 2025-07-17T09:05:59.5224225Z 2025-07-17T09:05:59.5224387Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2025-07-17T09:05:59.5224524Z of parameters at each rank. Each parameter belongs to a single rank and is 2025-07-17T09:05:59.5224675Z not divided among ranks. The partition is arbitrary and might not match the 2025-07-17T09:05:59.5224761Z the parameter registration or usage order. 2025-07-17T09:05:59.5224817Z 2025-07-17T09:05:59.5224878Z Arguments: 2025-07-17T09:05:59.5225112Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2025-07-17T09:05:59.5225407Z or :class:`dict` s giving all parameters, which will be sharded 2025-07-17T09:05:59.5225477Z across ranks. 2025-07-17T09:05:59.5225534Z 2025-07-17T09:05:59.5225600Z Keyword Args: 2025-07-17T09:05:59.5225735Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2025-07-17T09:05:59.5225806Z optimizer. 2025-07-17T09:05:59.5226059Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2025-07-17T09:05:59.5226193Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2025-07-17T09:05:59.5226294Z :meth:`torch.distributed.init_process_group`). 2025-07-17T09:05:59.5226447Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2025-07-17T09:05:59.5226578Z packed into buckets to speed up communication, and ``param.data`` 2025-07-17T09:05:59.5226697Z fields point to bucket views at different offsets; if ``False``, 2025-07-17T09:05:59.5226829Z each individual parameter is communicated separately, and each 2025-07-17T09:05:59.5226926Z ``params.data`` stays intact (default: ``False``). 2025-07-17T09:05:59.5227056Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2025-07-17T09:05:59.5227180Z overlapped with :class:`DistributedDataParallel` 's gradient 2025-07-17T09:05:59.5227310Z synchronization; this requires (1) either a functional optimizer 2025-07-17T09:05:59.5227420Z for the ``optimizer_class`` argument or one with a functional 2025-07-17T09:05:59.5227534Z equivalent and (2) registering a DDP communication hook 2025-07-17T09:05:59.5227655Z constructed from one of the functions in ``ddp_zero_hook.py``; 2025-07-17T09:05:59.5227764Z parameters are packed into buckets matching those in 2025-07-17T09:05:59.5227860Z :class:`DistributedDataParallel`, meaning that the 2025-07-17T09:05:59.5227959Z ``parameters_as_bucket_view`` argument is ignored. 2025-07-17T09:05:59.5228080Z If ``False``, :meth:`step` runs disjointly after the backward pass 2025-07-17T09:05:59.5228150Z (per normal). 2025-07-17T09:05:59.5228218Z (default: ``False``) 2025-07-17T09:05:59.5228353Z **defaults: any trailing arguments, which are forwarded to the local 2025-07-17T09:05:59.5228417Z optimizer. 2025-07-17T09:05:59.5228479Z 2025-07-17T09:05:59.5228548Z Example:: 2025-07-17T09:05:59.5228608Z 2025-07-17T09:05:59.5228679Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.5228749Z >>> import torch.nn as nn 2025-07-17T09:05:59.5228877Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2025-07-17T09:05:59.5228998Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2025-07-17T09:05:59.5229143Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2025-07-17T09:05:59.5229228Z >>> ddp = DDP(model, device_ids=[rank]) 2025-07-17T09:05:59.5229316Z >>> opt = ZeroRedundancyOptimizer( 2025-07-17T09:05:59.5229388Z >>> ddp.parameters(), 2025-07-17T09:05:59.5229476Z >>> optimizer_class=torch.optim.Adam, 2025-07-17T09:05:59.5229535Z >>> lr=0.01 2025-07-17T09:05:59.5229598Z >>> ) 2025-07-17T09:05:59.5229674Z >>> ddp(inputs).sum().backward() 2025-07-17T09:05:59.5229738Z >>> opt.step() 2025-07-17T09:05:59.5229794Z 2025-07-17T09:05:59.5229862Z .. warning:: 2025-07-17T09:05:59.5229986Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2025-07-17T09:05:59.5230089Z passed-in parameters are the same dense type. 2025-07-17T09:05:59.5230145Z 2025-07-17T09:05:59.5230212Z .. warning:: 2025-07-17T09:05:59.5230338Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2025-07-17T09:05:59.5230533Z the way that overlapping :class:`DistributedDataParallel` with 2025-07-17T09:05:59.5230737Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2025-07-17T09:05:59.5230865Z two or three training iterations do not perform parameter updates in 2025-07-17T09:05:59.5230988Z the optimizer step, depending on if ``static_graph=False`` or 2025-07-17T09:05:59.5231221Z ``static_graph=True``, respectively. This is because it needs 2025-07-17T09:05:59.5231342Z information about the gradient bucketing strategy used by 2025-07-17T09:05:59.5231472Z :class:`DistributedDataParallel`, which is not finalized until the 2025-07-17T09:05:59.5231602Z second forward pass if ``static_graph=False`` or until the third 2025-07-17T09:05:59.5231727Z forward pass if ``static_graph=True``. To adjust for this, one option 2025-07-17T09:05:59.5231808Z is to prepend dummy inputs. 2025-07-17T09:05:59.5231861Z 2025-07-17T09:05:59.5232018Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2025-07-17T09:05:59.5232076Z 2025-07-17T09:05:59.5232230Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5232287Z 2025-07-17T09:05:59.5232357Z warnings.warn(msg) 2025-07-17T09:05:59.5232413Z 2025-07-17T09:05:59.5232546Z --- Parse Warning: 76 / 136 --- 2025-07-17T09:05:59.5233146Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PostLocalSGDOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/post_localSGD_optimizer.py line=9. 2025-07-17T09:05:59.5233311Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5233366Z 2025-07-17T09:05:59.5233602Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2025-07-17T09:05:59.5233703Z This optimizer runs local optimizer at every step. 2025-07-17T09:05:59.5233903Z After the warm-up stage, it averages parameters periodically after the local optimizer is applied. 2025-07-17T09:05:59.5233956Z 2025-07-17T09:05:59.5234017Z Args: 2025-07-17T09:05:59.5234088Z optim: The local optimizer. 2025-07-17T09:05:59.5234217Z averager: A model averager instance to run post-localSGD algorithm. 2025-07-17T09:05:59.5234278Z 2025-07-17T09:05:59.5234344Z Example:: 2025-07-17T09:05:59.5234408Z 2025-07-17T09:05:59.5234493Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T09:05:59.5234565Z >>> import torch 2025-07-17T09:05:59.5234649Z >>> import torch.distributed as dist 2025-07-17T09:05:59.5234816Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2025-07-17T09:05:59.5234885Z >>> import torch.nn as nn 2025-07-17T09:05:59.5235010Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2025-07-17T09:05:59.5235171Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2025-07-17T09:05:59.5235249Z >>> PostLocalSGDState, 2025-07-17T09:05:59.5235317Z >>> post_localSGD_hook, 2025-07-17T09:05:59.5235376Z >>> ) 2025-07-17T09:05:59.5235436Z >>> 2025-07-17T09:05:59.5235543Z >>> model = nn.parallel.DistributedDataParallel( 2025-07-17T09:05:59.5235641Z >>> module, device_ids=[rank], output_device=rank 2025-07-17T09:05:59.5235700Z >>> ) 2025-07-17T09:05:59.5235760Z >>> 2025-07-17T09:05:59.5235855Z >>> # Register a post-localSGD communication hook. 2025-07-17T09:05:59.5236039Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2025-07-17T09:05:59.5236145Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-07-17T09:05:59.5236211Z >>> 2025-07-17T09:05:59.5236332Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2025-07-17T09:05:59.5236553Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2025-07-17T09:05:59.5236710Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-07-17T09:05:59.5236843Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2025-07-17T09:05:59.5236925Z >>> opt = PostLocalSGDOptimizer( 2025-07-17T09:05:59.5237094Z >>> optim=local_optim, 2025-07-17T09:05:59.5237250Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2025-07-17T09:05:59.5237314Z >>> ) 2025-07-17T09:05:59.5237369Z >>> 2025-07-17T09:05:59.5237519Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2025-07-17T09:05:59.5237697Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2025-07-17T09:05:59.5237919Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2025-07-17T09:05:59.5237997Z >>> for step in range(0, 200): 2025-07-17T09:05:59.5238068Z >>> opt.zero_grad() 2025-07-17T09:05:59.5238148Z >>> loss = loss_fn(output, labels) 2025-07-17T09:05:59.5238219Z >>> loss.backward() 2025-07-17T09:05:59.5238285Z >>> opt.step() 2025-07-17T09:05:59.5238337Z 2025-07-17T09:05:59.5238497Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5238556Z 2025-07-17T09:05:59.5238624Z warnings.warn(msg) 2025-07-17T09:05:59.5238677Z 2025-07-17T09:05:59.5238809Z --- Parse Warning: 77 / 136 --- 2025-07-17T09:05:59.5239358Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/optimizer.py line=129. 2025-07-17T09:05:59.5239530Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5239584Z 2025-07-17T09:05:59.5239740Z DistributedOptimizer takes remote references to parameters scattered 2025-07-17T09:05:59.5239884Z across workers and applies the given optimizer locally for each parameter. 2025-07-17T09:05:59.5239944Z 2025-07-17T09:05:59.5240080Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2025-07-17T09:05:59.5240185Z to retrieve the gradients for specific parameters. 2025-07-17T09:05:59.5240239Z 2025-07-17T09:05:59.5240309Z Concurrent calls to 2025-07-17T09:05:59.5240443Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2025-07-17T09:05:59.5240535Z either from the same or different clients, will 2025-07-17T09:05:59.5240669Z be serialized on each worker -- as each worker's optimizer can only work 2025-07-17T09:05:59.5240794Z on one set of gradients at a time. However, there is no guarantee that 2025-07-17T09:05:59.5240943Z the full forward-backward-optimizer sequence will execute for one client 2025-07-17T09:05:59.5241071Z at a time. This means that the gradients being applied may not correspond 2025-07-17T09:05:59.5241201Z to the latest forward pass executed on a given worker. Also, there is no 2025-07-17T09:05:59.5241275Z guaranteed ordering across workers. 2025-07-17T09:05:59.5241333Z 2025-07-17T09:05:59.5241489Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2025-07-17T09:05:59.5241633Z by default, so that optimizer updates are not blocked by the Python Global 2025-07-17T09:05:59.5241777Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2025-07-17T09:05:59.5241922Z Model Parallel). This feature is currently enabled for most optimizers. You 2025-07-17T09:05:59.5242072Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2025-07-17T09:05:59.5242145Z for your own custom optimizers. 2025-07-17T09:05:59.5242262Z 2025-07-17T09:05:59.5242374Z Args: 2025-07-17T09:05:59.5242498Z optimizer_class (optim.Optimizer): the class of optimizer to 2025-07-17T09:05:59.5242573Z instantiate on each worker. 2025-07-17T09:05:59.5242695Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2025-07-17T09:05:59.5242759Z to optimize. 2025-07-17T09:05:59.5242987Z args: arguments to pass to the optimizer constructor on each worker. 2025-07-17T09:05:59.5243129Z kwargs: arguments to pass to the optimizer constructor on each worker. 2025-07-17T09:05:59.5243186Z 2025-07-17T09:05:59.5243252Z Example:: 2025-07-17T09:05:59.5243332Z >>> # xdoctest: +SKIP("distributed") 2025-07-17T09:05:59.5243437Z >>> import torch.distributed.autograd as dist_autograd 2025-07-17T09:05:59.5243529Z >>> import torch.distributed.rpc as rpc 2025-07-17T09:05:59.5243596Z >>> from torch import optim 2025-07-17T09:05:59.5243717Z >>> from torch.distributed.optim import DistributedOptimizer 2025-07-17T09:05:59.5243776Z >>> 2025-07-17T09:05:59.5243869Z >>> with dist_autograd.context() as context_id: 2025-07-17T09:05:59.5243938Z >>> # Forward pass. 2025-07-17T09:05:59.5244060Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2025-07-17T09:05:59.5244174Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2025-07-17T09:05:59.5244263Z >>> loss = rref1.to_here() + rref2.to_here() 2025-07-17T09:05:59.5244318Z >>> 2025-07-17T09:05:59.5244386Z >>> # Backward pass. 2025-07-17T09:05:59.5244484Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2025-07-17T09:05:59.5244550Z >>> 2025-07-17T09:05:59.5244617Z >>> # Optimizer. 2025-07-17T09:05:59.5244711Z >>> dist_optim = DistributedOptimizer( 2025-07-17T09:05:59.5244777Z >>> optim.SGD, 2025-07-17T09:05:59.5244850Z >>> [rref1, rref2], 2025-07-17T09:05:59.5244912Z >>> lr=0.05, 2025-07-17T09:05:59.5244967Z >>> ) 2025-07-17T09:05:59.5245042Z >>> dist_optim.step(context_id) 2025-07-17T09:05:59.5245095Z 2025-07-17T09:05:59.5245197Z __ https://github.com/pytorch/tutorials/pull/1465 2025-07-17T09:05:59.5245254Z 2025-07-17T09:05:59.5245406Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5245460Z 2025-07-17T09:05:59.5245534Z warnings.warn(msg) 2025-07-17T09:05:59.5245587Z 2025-07-17T09:05:59.5245713Z --- Parse Warning: 78 / 136 --- 2025-07-17T09:05:59.5246295Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_sharding in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/experimental/_register_sharding.py line=25. 2025-07-17T09:05:59.5246450Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5246506Z 2025-07-17T09:05:59.5246681Z :meth:`register_sharding` is an experimental API that allows users to register sharding 2025-07-17T09:05:59.5246817Z strategies for an operator when the tensor inputs and outputs are DTensor. 2025-07-17T09:05:59.5246969Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``, 2025-07-17T09:05:59.5247117Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2) 2025-07-17T09:05:59.5247279Z when users would like to overwrite default sharding strategies of existing operators. 2025-07-17T09:05:59.5247332Z 2025-07-17T09:05:59.5247395Z Args: 2025-07-17T09:05:59.5247484Z op (Union[OpOverload, List[OpOverload]]): 2025-07-17T09:05:59.5247602Z An op or a list of ops to register the customized sharding function. 2025-07-17T09:05:59.5247659Z 2025-07-17T09:05:59.5247719Z Returns: 2025-07-17T09:05:59.5247953Z A function decorator which can be used to wrap a function that defines the sharding 2025-07-17T09:05:59.5248173Z strategy for the operator specified in ``op``. The defined sharding strategy will be 2025-07-17T09:05:59.5248333Z registered to DTensor and will override the default sharding strategy if DTensor has 2025-07-17T09:05:59.5248619Z already implemented the operator. The customized sharding function takes the same inputs 2025-07-17T09:05:59.5248767Z as the original op (except that if an arg is a :class:`torch.Tensor`, it will be 2025-07-17T09:05:59.5248923Z replaced by a tensor-like object that DTensor uses internally). The function should 2025-07-17T09:05:59.5249089Z return a sequence of 2-tuples, each specifying acceptable output placements and its 2025-07-17T09:05:59.5249167Z corresponding intput placements. 2025-07-17T09:05:59.5249223Z 2025-07-17T09:05:59.5249282Z Example: 2025-07-17T09:05:59.5249375Z >>> # xdoctest: +SKIP("distributed") 2025-07-17T09:05:59.5249471Z >>> @register_sharding(aten._softmax.default) 2025-07-17T09:05:59.5249578Z >>> def custom_softmax_sharding(x, dim, half_to_float): 2025-07-17T09:05:59.5249666Z >>> softmax_dim = dim if dim >= 0 else dim + x.ndim 2025-07-17T09:05:59.5249738Z >>> acceptable_shardings = [] 2025-07-17T09:05:59.5249796Z >>> 2025-07-17T09:05:59.5249916Z >>> all_replicate = ([Replicate()], [Replicate(), None, None]) 2025-07-17T09:05:59.5250009Z >>> acceptable_shardings.append(all_replicate) 2025-07-17T09:05:59.5250073Z >>> 2025-07-17T09:05:59.5250156Z >>> for sharding_dim in range(x.ndim): 2025-07-17T09:05:59.5250233Z >>> if sharding_dim != softmax_dim: 2025-07-17T09:05:59.5250306Z >>> all_sharded = ( 2025-07-17T09:05:59.5250377Z >>> [Shard(sharding_dim)], 2025-07-17T09:05:59.5250459Z >>> [Shard(sharding_dim), None, None], 2025-07-17T09:05:59.5250519Z >>> ) 2025-07-17T09:05:59.5250620Z >>> acceptable_shardings.append(all_sharded) 2025-07-17T09:05:59.5250675Z >>> 2025-07-17T09:05:59.5250763Z >>> return acceptable_shardings 2025-07-17T09:05:59.5250819Z 2025-07-17T09:05:59.5250943Z .. note:: This API is currently experimental and subject to change 2025-07-17T09:05:59.5250999Z 2025-07-17T09:05:59.5251149Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5251204Z 2025-07-17T09:05:59.5251274Z warnings.warn(msg) 2025-07-17T09:05:59.5251330Z 2025-07-17T09:05:59.5251457Z --- Parse Warning: 79 / 136 --- 2025-07-17T09:05:59.5251997Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=local_map in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/experimental/_func_map.py line=35. 2025-07-17T09:05:59.5252161Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5252221Z 2025-07-17T09:05:59.5252391Z :meth:`local_map` is an experimental API that allows users to pass :class:`DTensor` s 2025-07-17T09:05:59.5252556Z to a function that is written to be applied on ``torch.Tensor`` s. It is done by extracting 2025-07-17T09:05:59.5252717Z the local components of :class:`DTensor`, call the function, and wrap the outputs to 2025-07-17T09:05:59.5252822Z :class:`DTensor` according to the ``out_placements``. 2025-07-17T09:05:59.5252875Z 2025-07-17T09:05:59.5252938Z Args: 2025-07-17T09:05:59.5253064Z func (Callable): the function to be applied on each local shard of 2025-07-17T09:05:59.5253137Z :class:`DTensor` s. 2025-07-17T09:05:59.5253278Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2025-07-17T09:05:59.5253432Z the desired placements of the :class:`DTensor` s in ``func``'s flattened output. 2025-07-17T09:05:59.5253700Z If the flattened ``output`` is a single value, the ``out_placements`` should be 2025-07-17T09:05:59.5253845Z of type `PlacementType`. Otherwise if the flattened ``output`` has multiple 2025-07-17T09:05:59.5253999Z values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1 2025-07-17T09:05:59.5254195Z mapping to the flattened ``output``. 2025-07-17T09:05:59.5254329Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2025-07-17T09:05:59.5254494Z placements (a `Tuple[Placement]` value). For non-Tensor output, the `PlacementType` 2025-07-17T09:05:59.5254563Z should be `None`. 2025-07-17T09:05:59.5254702Z Note that the only exception is when no :class:`DTensor` argument is passed 2025-07-17T09:05:59.5254842Z in. In this case, even if `out_placements` is not `None`, the result function 2025-07-17T09:05:59.5254993Z should ignore the desired placements because the function is not running with 2025-07-17T09:05:59.5255072Z :class:`DTensor` s. 2025-07-17T09:05:59.5255180Z in_placements (Tuple[`PlacementType`, ...], optional): 2025-07-17T09:05:59.5255351Z the required placements of the :class:`DTensor` s in the flattened inputs of ``func``. 2025-07-17T09:05:59.5255494Z If ``in_placements`` is specified, :meth:`local_map` would examine whether the 2025-07-17T09:05:59.5255637Z placements of each :class:`DTensor` argument is the same as the required 2025-07-17T09:05:59.5255753Z placements or not. If the placements are not the same and 2025-07-17T09:05:59.5255902Z ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if 2025-07-17T09:05:59.5256047Z ``redistribute_inputs`` is ``True``, the argument will be first redistributed to 2025-07-17T09:05:59.5256204Z the required sharding placements before passing its local tensor to ``func``. 2025-07-17T09:05:59.5256340Z The only exception is when required placements are not ``None`` and the 2025-07-17T09:05:59.5256487Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2025-07-17T09:05:59.5256617Z will be skipped and the argument will be directly passed to ``func``. 2025-07-17T09:05:59.5256753Z If ``in_placements`` is ``None``, no placements examination will be performed. 2025-07-17T09:05:59.5256815Z Default: None 2025-07-17T09:05:59.5256933Z in_grad_placements (Tuple[`PlacementType`, ...], optional): 2025-07-17T09:05:59.5257059Z the placements hint of the :class:`DTensor` s gradient corresponds 2025-07-17T09:05:59.5257192Z to the flattened input DTensor. This argument is the hint that user 2025-07-17T09:05:59.5257306Z can give to :meth:`to_local` in case the gradient layout of the 2025-07-17T09:05:59.5257441Z local tensor input does not match its :class:`DTensor` input layout. 2025-07-17T09:05:59.5257559Z If not specified, we will assume the gradient layout of the local 2025-07-17T09:05:59.5257692Z tensor input remains the same as the original :class:`DTensor` input 2025-07-17T09:05:59.5257802Z and use that for gradient computation. Default: None. 2025-07-17T09:05:59.5257905Z device_mesh (:class:`DeviceMesh`, optional): 2025-07-17T09:05:59.5258039Z the device mesh that the output :class:`DTensor` s are placed on. If not 2025-07-17T09:05:59.5258190Z specified, this will be inferred from the first input :class:`DTensor`'s device 2025-07-17T09:05:59.5258257Z mesh. Default: None. 2025-07-17T09:05:59.5258321Z 2025-07-17T09:05:59.5258385Z Keyword Args: 2025-07-17T09:05:59.5258471Z redistribute_inputs (bool, optional): 2025-07-17T09:05:59.5258618Z the bool value indicating whether to reshard the input :class:`DTensor` s when 2025-07-17T09:05:59.5258873Z their placements are different from the required input placements. If this 2025-07-17T09:05:59.5259008Z value is ``False`` and some :class:`DTensor` input has a different placement, 2025-07-17T09:05:59.5259099Z an exception will be raised. Default: False. 2025-07-17T09:05:59.5259162Z 2025-07-17T09:05:59.5259218Z Returns: 2025-07-17T09:05:59.5259491Z A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor` 2025-07-17T09:05:59.5259640Z and returns a :class:`DTensor` constructed from the return value of ``func``. 2025-07-17T09:05:59.5259702Z 2025-07-17T09:05:59.5259763Z Raises: 2025-07-17T09:05:59.5259914Z AssertionError: For any non-DTensor output, we require its corresponding 2025-07-17T09:05:59.5260072Z output placement in ``out_placements`` be None. An AssertionError will be raised 2025-07-17T09:05:59.5260144Z if this is not the case. 2025-07-17T09:05:59.5260201Z 2025-07-17T09:05:59.5260367Z ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs 2025-07-17T09:05:59.5260467Z a redistribution according to ``in_placements``. 2025-07-17T09:05:59.5260527Z 2025-07-17T09:05:59.5260590Z Example: 2025-07-17T09:05:59.5260670Z >>> # xdoctest: +SKIP("distributed") 2025-07-17T09:05:59.5260761Z >>> def mm_allreduce_forward(device_mesh, W, X): 2025-07-17T09:05:59.5260841Z >>> partial_sum_tensor = torch.mm(W, X) 2025-07-17T09:05:59.5260982Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2025-07-17T09:05:59.5261065Z >>> return reduced_tensor 2025-07-17T09:05:59.5261125Z >>> 2025-07-17T09:05:59.5261216Z >>> W = torch.randn(12, 8, requires_grad=False) 2025-07-17T09:05:59.5261290Z >>> X = torch.randn(8, 16, requires_grad=False) 2025-07-17T09:05:59.5261360Z >>> Y = torch.mm(W, X) 2025-07-17T09:05:59.5261478Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2025-07-17T09:05:59.5261592Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2025-07-17T09:05:59.5261645Z >>> 2025-07-17T09:05:59.5261810Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor convertion 2025-07-17T09:05:59.5261890Z >>> local_mm_allreduce_forward = local_map( 2025-07-17T09:05:59.5261961Z >>> mm_allreduce_forward, 2025-07-17T09:05:59.5262035Z >>> out_placements=[Replicate()], 2025-07-17T09:05:59.5262112Z >>> in_placements=[col_wise, row_wise], 2025-07-17T09:05:59.5262183Z >>> device_mesh=device_mesh, 2025-07-17T09:05:59.5262242Z >>> ) 2025-07-17T09:05:59.5262297Z >>> 2025-07-17T09:05:59.5262367Z >>> W_dt = distribute_tensor( 2025-07-17T09:05:59.5262443Z ... W, device_mesh, (col_wise) 2025-07-17T09:05:59.5262517Z ... ) # col-wisely sharded W tensor 2025-07-17T09:05:59.5262592Z >>> X_dt = distribute_tensor( 2025-07-17T09:05:59.5262658Z ... X, device_mesh, (row_wise) 2025-07-17T09:05:59.5262731Z ... ) # row-wisely sharded X tensor 2025-07-17T09:05:59.5262811Z >>> Y_dt = local_mm_allreduce_forward( 2025-07-17T09:05:59.5262883Z ... device_mesh, W_dt, X_dt 2025-07-17T09:05:59.5262980Z ... ) # apply local_mm_allreduce_forward to DTensors 2025-07-17T09:05:59.5263036Z 2025-07-17T09:05:59.5263158Z .. note:: This API is currently experimental and subject to change 2025-07-17T09:05:59.5263215Z 2025-07-17T09:05:59.5263364Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5263424Z 2025-07-17T09:05:59.5263490Z warnings.warn(msg) 2025-07-17T09:05:59.5263545Z 2025-07-17T09:05:59.5263682Z --- Parse Warning: 80 / 136 --- 2025-07-17T09:05:59.5264256Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PrepareModuleInput in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py line=428. 2025-07-17T09:05:59.5264536Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5264591Z 2025-07-17T09:05:59.5264925Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2025-07-17T09:05:59.5265117Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2025-07-17T09:05:59.5265177Z 2025-07-17T09:05:59.5265236Z Keyword Args: 2025-07-17T09:05:59.5265419Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-07-17T09:05:59.5265609Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2025-07-17T09:05:59.5265821Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2025-07-17T09:05:59.5265899Z as a placeholder. default: None. 2025-07-17T09:05:59.5266039Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-07-17T09:05:59.5266254Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-07-17T09:05:59.5266496Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2025-07-17T09:05:59.5266579Z input_kwarg_layouts (Dict[str, Placement]): 2025-07-17T09:05:59.5266793Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2025-07-17T09:05:59.5266855Z default: None 2025-07-17T09:05:59.5266959Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2025-07-17T09:05:59.5267166Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-07-17T09:05:59.5267261Z have the desired DTensor layouts. default: None. 2025-07-17T09:05:59.5267336Z use_local_output (bool, optional): 2025-07-17T09:05:59.5267539Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2025-07-17T09:05:59.5267594Z Returns: 2025-07-17T09:05:59.5267785Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2025-07-17T09:05:59.5267837Z 2025-07-17T09:05:59.5267903Z Example:: 2025-07-17T09:05:59.5267976Z >>> # xdoctest: +SKIP(failing) 2025-07-17T09:05:59.5268162Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2025-07-17T09:05:59.5268280Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-07-17T09:05:59.5268338Z >>> ... 2025-07-17T09:05:59.5268516Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-07-17T09:05:59.5268601Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-07-17T09:05:59.5268663Z >>> 2025-07-17T09:05:59.5268854Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2025-07-17T09:05:59.5268954Z >>> # and then redistributed to Replicated DTensor. 2025-07-17T09:05:59.5269022Z >>> parallelize_module( 2025-07-17T09:05:59.5269111Z >>> block, # this can be a submodule or module 2025-07-17T09:05:59.5269172Z >>> tp_mesh, 2025-07-17T09:05:59.5269251Z >>> parallelize_plan={ 2025-07-17T09:05:59.5269333Z >>> "attn": PrepareModuleInput( 2025-07-17T09:05:59.5269444Z >>> input_layouts=(Shard(0), None, None, ...), 2025-07-17T09:05:59.5269555Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2025-07-17T09:05:59.5269702Z >>> ), 2025-07-17T09:05:59.5269823Z >>> } 2025-07-17T09:05:59.5269883Z >>> ) 2025-07-17T09:05:59.5269939Z 2025-07-17T09:05:59.5270107Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5270160Z 2025-07-17T09:05:59.5270229Z warnings.warn(msg) 2025-07-17T09:05:59.5270283Z 2025-07-17T09:05:59.5270522Z --- Parse Warning: 81 / 136 --- 2025-07-17T09:05:59.5271123Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PrepareModuleOutput in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py line=597. 2025-07-17T09:05:59.5271282Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5271338Z 2025-07-17T09:05:59.5271567Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2025-07-17T09:05:59.5271771Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2025-07-17T09:05:59.5271823Z 2025-07-17T09:05:59.5271885Z Keyword Args: 2025-07-17T09:05:59.5271989Z output_layouts (Union[Placement, Tuple[Placement]]): 2025-07-17T09:05:59.5272191Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2025-07-17T09:05:59.5272409Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2025-07-17T09:05:59.5272503Z ``None`` need to be specified as a placeholder. 2025-07-17T09:05:59.5272622Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2025-07-17T09:05:59.5272844Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2025-07-17T09:05:59.5272921Z have the desired DTensor layouts. 2025-07-17T09:05:59.5273004Z use_local_output (bool, optional): 2025-07-17T09:05:59.5273205Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2025-07-17T09:05:59.5273265Z Returns: 2025-07-17T09:05:59.5273437Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2025-07-17T09:05:59.5273495Z 2025-07-17T09:05:59.5273558Z Example:: 2025-07-17T09:05:59.5273630Z >>> # xdoctest: +SKIP(failing) 2025-07-17T09:05:59.5273817Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2025-07-17T09:05:59.5273941Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-07-17T09:05:59.5273997Z >>> ... 2025-07-17T09:05:59.5274180Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-07-17T09:05:59.5274267Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-07-17T09:05:59.5274329Z >>> 2025-07-17T09:05:59.5274553Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2025-07-17T09:05:59.5274645Z >>> # and then redistributed to Sharded DTensor. 2025-07-17T09:05:59.5274714Z >>> parallelize_module( 2025-07-17T09:05:59.5274805Z >>> block, # this can be a submodule or module 2025-07-17T09:05:59.5274879Z >>> tp_mesh, 2025-07-17T09:05:59.5274970Z >>> parallelize_plan = PrepareModuleOutput( 2025-07-17T09:05:59.5275050Z >>> output_layouts=Replicate(), 2025-07-17T09:05:59.5275128Z >>> desired_output_layouts=Shard(0) 2025-07-17T09:05:59.5275185Z >>> ) 2025-07-17T09:05:59.5275240Z >>> ) 2025-07-17T09:05:59.5275296Z 2025-07-17T09:05:59.5275444Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5275560Z 2025-07-17T09:05:59.5275623Z warnings.warn(msg) 2025-07-17T09:05:59.5275753Z 2025-07-17T09:05:59.5275868Z --- Parse Warning: 82 / 136 --- 2025-07-17T09:05:59.5276442Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PrepareModuleInputOutput in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py line=705. 2025-07-17T09:05:59.5276697Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5276753Z 2025-07-17T09:05:59.5277001Z Configure the nn.Module's inputs (and outputs) to convert the input tensors (and output tensors, respectively) of the nn.Module 2025-07-17T09:05:59.5277243Z to DTensors at runtime according to ``input_layouts`` (and output_layouts, respectively), and perform layout redistribution 2025-07-17T09:05:59.5277458Z according to the ``desired_input_layouts`` (and ``desired_output_layouts``, respectively). This is a combination of 2025-07-17T09:05:59.5277594Z :class:`PrepareModuleInput` and :class:`PrepareModuleOutput`. 2025-07-17T09:05:59.5277652Z 2025-07-17T09:05:59.5277716Z Keyword Args: 2025-07-17T09:05:59.5277838Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-07-17T09:05:59.5278030Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2025-07-17T09:05:59.5278239Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2025-07-17T09:05:59.5278318Z as a placeholder. default: None. 2025-07-17T09:05:59.5278457Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-07-17T09:05:59.5278669Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-07-17T09:05:59.5278894Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2025-07-17T09:05:59.5278981Z input_kwarg_layouts (Dict[str, Placement]): 2025-07-17T09:05:59.5279191Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2025-07-17T09:05:59.5279255Z default: None 2025-07-17T09:05:59.5279365Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2025-07-17T09:05:59.5279573Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-07-17T09:05:59.5279666Z have the desired DTensor layouts. default: None. 2025-07-17T09:05:59.5279741Z use_local_input (bool, optional): 2025-07-17T09:05:59.5279950Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2025-07-17T09:05:59.5280046Z output_layouts (Union[Placement, Tuple[Placement]]): 2025-07-17T09:05:59.5280250Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2025-07-17T09:05:59.5280461Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2025-07-17T09:05:59.5280556Z ``None`` need to be specified as a placeholder. 2025-07-17T09:05:59.5280676Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2025-07-17T09:05:59.5280897Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2025-07-17T09:05:59.5280973Z have the desired DTensor layouts. 2025-07-17T09:05:59.5281048Z use_local_output (bool, optional): 2025-07-17T09:05:59.5281243Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2025-07-17T09:05:59.5281305Z Returns: 2025-07-17T09:05:59.5281581Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs and outputs. 2025-07-17T09:05:59.5281695Z 2025-07-17T09:05:59.5281757Z Example:: 2025-07-17T09:05:59.5281829Z >>> # xdoctest: +SKIP(failing) 2025-07-17T09:05:59.5282031Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInputOutput 2025-07-17T09:05:59.5282395Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-07-17T09:05:59.5282453Z >>> ... 2025-07-17T09:05:59.5282629Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-07-17T09:05:59.5282712Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-07-17T09:05:59.5282770Z >>> 2025-07-17T09:05:59.5282959Z >>> # According to the style specified below, the first input of attn will be annotated as Sharded DTensor 2025-07-17T09:05:59.5283164Z >>> # and then redistributed to Replicated DTensor, and the output of the TransformerBlock will be annotated 2025-07-17T09:05:59.5283298Z >>> # as Replicated DTensor and then redistributed to Sharded DTensor. 2025-07-17T09:05:59.5283377Z >>> parallelize_module( 2025-07-17T09:05:59.5283463Z >>> block, # this can be a submodule or module 2025-07-17T09:05:59.5283529Z >>> tp_mesh, 2025-07-17T09:05:59.5283603Z >>> parallelize_plan={ 2025-07-17T09:05:59.5283699Z >>> "attn": PrepareModuleInputOutput( 2025-07-17T09:05:59.5283797Z >>> input_layouts=(Shard(0), None, None, ...), 2025-07-17T09:05:59.5283900Z >>> desired_input_layouts=(Replicate(), None, None, ...), 2025-07-17T09:05:59.5283982Z >>> output_layouts=Replicate(), 2025-07-17T09:05:59.5284067Z >>> desired_output_layouts=Shard(0), 2025-07-17T09:05:59.5284127Z >>> ), 2025-07-17T09:05:59.5284183Z >>> } 2025-07-17T09:05:59.5284245Z >>> ) 2025-07-17T09:05:59.5284299Z 2025-07-17T09:05:59.5284463Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5284517Z 2025-07-17T09:05:59.5284582Z warnings.warn(msg) 2025-07-17T09:05:59.5284636Z 2025-07-17T09:05:59.5284776Z --- Parse Warning: 83 / 136 --- 2025-07-17T09:05:59.5285320Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_CustomReducer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/pipelining/microbatch.py line=29. 2025-07-17T09:05:59.5285483Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5285536Z 2025-07-17T09:05:59.5285677Z Custom reducer class that can be used to specify a custom operation that 2025-07-17T09:05:59.5285786Z reduces losses of multiple microbatches into one value. 2025-07-17T09:05:59.5285839Z 2025-07-17T09:05:59.5285903Z Example: 2025-07-17T09:05:59.5285969Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.5286052Z >>> sum_reducer = _CustomReducer( 2025-07-17T09:05:59.5286120Z >>> torch.tensor(0.0), 2025-07-17T09:05:59.5286188Z >>> lambda a, b: a + b 2025-07-17T09:05:59.5286244Z >>> ) 2025-07-17T09:05:59.5286307Z 2025-07-17T09:05:59.5286456Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5286516Z 2025-07-17T09:05:59.5286581Z warnings.warn(msg) 2025-07-17T09:05:59.5286642Z 2025-07-17T09:05:59.5286756Z --- Parse Warning: 84 / 136 --- 2025-07-17T09:05:59.5287329Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_sharded_optimizer_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/optimizer.py line=221. 2025-07-17T09:05:59.5287481Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5287603Z 2025-07-17T09:05:59.5287780Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2025-07-17T09:05:59.5287838Z 2025-07-17T09:05:59.5287941Z This is the current recommended way to checkpoint FSDP. 2025-07-17T09:05:59.5288006Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.5288109Z >>> import torch.distributed.checkpoint as dist_cp 2025-07-17T09:05:59.5288170Z >>> # Save 2025-07-17T09:05:59.5288342Z >>> model: torch.nn.Model 2025-07-17T09:05:59.5288425Z >>> optim_params = model.parameters() 2025-07-17T09:05:59.5288516Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2025-07-17T09:05:59.5288571Z >>> # Save 2025-07-17T09:05:59.5288711Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2025-07-17T09:05:59.5288776Z >>> state_dict = { 2025-07-17T09:05:59.5288884Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2025-07-17T09:05:59.5288959Z >>> "model": model.state_dict() 2025-07-17T09:05:59.5289021Z >>> } 2025-07-17T09:05:59.5289091Z >>> dist_cp.save_state_dict( 2025-07-17T09:05:59.5289166Z >>> state_dict=optim_state, 2025-07-17T09:05:59.5289277Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2025-07-17T09:05:59.5289364Z >>> planner=dist_cp.DefaultSavePlanner(), 2025-07-17T09:05:59.5289417Z >>> ) 2025-07-17T09:05:59.5289478Z >>> 2025-07-17T09:05:59.5289541Z >>> # Load 2025-07-17T09:05:59.5289677Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2025-07-17T09:05:59.5289769Z >>> model_state_dict = model_tp.state_dict() 2025-07-17T09:05:59.5289834Z >>> checkpoint = { 2025-07-17T09:05:59.5289908Z >>> "model": model_state_dict 2025-07-17T09:05:59.5289968Z >>> } 2025-07-17T09:05:59.5290038Z >>> dist_cp.load_state_dict( 2025-07-17T09:05:59.5290109Z >>> state_dict=checkpoint, 2025-07-17T09:05:59.5290232Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2025-07-17T09:05:59.5290313Z >>> planner=dist_cp.DefaultLoadPlanner(), 2025-07-17T09:05:59.5290374Z >>> ) 2025-07-17T09:05:59.5290471Z >>> model.load_state_dict(checkpoint["model_state"]) 2025-07-17T09:05:59.5290536Z >>> 2025-07-17T09:05:59.5290642Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2025-07-17T09:05:59.5290717Z >>> model_state_dict, 2025-07-17T09:05:59.5290792Z >>> optimizer_key="optimizer", 2025-07-17T09:05:59.5290904Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2025-07-17T09:05:59.5290962Z >>> ) 2025-07-17T09:05:59.5291021Z >>> 2025-07-17T09:05:59.5291117Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2025-07-17T09:05:59.5291195Z >>> model, optim, optim_state["optimizer"] 2025-07-17T09:05:59.5291253Z >>> ) 2025-07-17T09:05:59.5291305Z >>> 2025-07-17T09:05:59.5291399Z >>> optim.load_state_dict(flattened_osd) 2025-07-17T09:05:59.5291453Z 2025-07-17T09:05:59.5291599Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5291652Z 2025-07-17T09:05:59.5291720Z warnings.warn(msg) 2025-07-17T09:05:59.5291775Z 2025-07-17T09:05:59.5291902Z --- Parse Warning: 85 / 136 --- 2025-07-17T09:05:59.5292426Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SavePlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/planner.py line=122. 2025-07-17T09:05:59.5292583Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5292638Z 2025-07-17T09:05:59.5292810Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2025-07-17T09:05:59.5292866Z 2025-07-17T09:05:59.5293043Z SavePlanners are stateful objects that can be used to customize the whole save process. 2025-07-17T09:05:59.5293218Z 2025-07-17T09:05:59.5293380Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2025-07-17T09:05:59.5293458Z will be visible to the whole process. 2025-07-17T09:05:59.5293511Z 2025-07-17T09:05:59.5293684Z A planner subclass can expect the following sequence of calls during save_state_dict: 2025-07-17T09:05:59.5293841Z 2025-07-17T09:05:59.5293926Z 1) set_up_planner - called on all ranks. 2025-07-17T09:05:59.5294010Z Signals the start of a checkpoint save. 2025-07-17T09:05:59.5294065Z 2025-07-17T09:05:59.5294143Z 2) create_local_plan - called on all ranks. 2025-07-17T09:05:59.5294317Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2025-07-17T09:05:59.5294370Z 2025-07-17T09:05:59.5294483Z 3) create_global_plan - called on the coordinator rank only. 2025-07-17T09:05:59.5294611Z Takes the SavePlan from all ranks and make any global decision. 2025-07-17T09:05:59.5294671Z 2025-07-17T09:05:59.5294745Z 4) finish_plan - called on all ranks. 2025-07-17T09:05:59.5294875Z This gives each rank a chance to adjust to global planning decisions. 2025-07-17T09:05:59.5294930Z 2025-07-17T09:05:59.5295028Z 5) resolve_data - called multiple times on each rank 2025-07-17T09:05:59.5295159Z Lookups a value on the `state_dict` for the storage layer to write. 2025-07-17T09:05:59.5295213Z 2025-07-17T09:05:59.5295392Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2025-07-17T09:05:59.5295504Z most changes can be expressed by changes in a single method. 2025-07-17T09:05:59.5295561Z 2025-07-17T09:05:59.5295639Z There are 3 usual patterns of extension: 2025-07-17T09:05:59.5295697Z 2025-07-17T09:05:59.5295849Z Rewriting state_dict. This is the simplest way to extend the save process as it 2025-07-17T09:05:59.5295988Z doesn't requite understanding the intrincacies of how SavePlan works: 2025-07-17T09:05:59.5296043Z 2025-07-17T09:05:59.5296120Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T09:05:59.5296206Z >>> class RenamePlanner(DefaultSavePlanner): 2025-07-17T09:05:59.5296284Z >>> def set_up_planner( 2025-07-17T09:05:59.5296347Z >>> self, 2025-07-17T09:05:59.5296428Z >>> state_dict: STATE_DICT_TYPE, 2025-07-17T09:05:59.5296515Z >>> storage_meta: Optional[StorageMeta], 2025-07-17T09:05:59.5296588Z >>> is_coordinator: bool, 2025-07-17T09:05:59.5296654Z >>> ) -> None: 2025-07-17T09:05:59.5296726Z >>> # prefix all keys with `foo_`` 2025-07-17T09:05:59.5296905Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2025-07-17T09:05:59.5296960Z 2025-07-17T09:05:59.5297155Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2025-07-17T09:05:59.5297210Z 2025-07-17T09:05:59.5297295Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T09:05:59.5297380Z >>> class FP16Planner(DefaultSavePlanner): 2025-07-17T09:05:59.5297459Z >>> def create_local_plan(self): 2025-07-17T09:05:59.5297536Z >>> plan = super().create_local_plan() 2025-07-17T09:05:59.5297604Z >>> for p in plan: 2025-07-17T09:05:59.5297686Z >>> if p.tensor_data is not None: 2025-07-17T09:05:59.5297795Z >>> p.tensor_data.properties.dtype = torch.float16 2025-07-17T09:05:59.5297859Z >>> return plan 2025-07-17T09:05:59.5297915Z >>> 2025-07-17T09:05:59.5297991Z >>> def resolve_data(self, write_item): 2025-07-17T09:05:59.5298074Z >>> item = super().resolve_data(write_item) 2025-07-17T09:05:59.5298238Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2025-07-17T09:05:59.5298376Z 2025-07-17T09:05:59.5298576Z Using the global planning step to make central decisions that can't be made individually by each rank 2025-07-17T09:05:59.5298682Z 2025-07-17T09:05:59.5298758Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T09:05:59.5298834Z >>> from itertools import zip_longest 2025-07-17T09:05:59.5298909Z >>> from dataclasses import replace 2025-07-17T09:05:59.5299013Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2025-07-17T09:05:59.5299278Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2025-07-17T09:05:59.5299363Z >>> # This sample doesn't handle ShardedTensors 2025-07-17T09:05:59.5299453Z >>> def create_global_plan(self, all_plans): 2025-07-17T09:05:59.5299556Z >>> iters = [iter(all_plans[0].items)] * len(all_plans) 2025-07-17T09:05:59.5299632Z >>> items_per_rank = [ 2025-07-17T09:05:59.5299721Z >>> [item for item in items if item is not None] 2025-07-17T09:05:59.5299824Z >>> for items in zip(*zip_longest(*iters), strict=True) 2025-07-17T09:05:59.5299881Z >>> ] 2025-07-17T09:05:59.5299952Z >>> all_plans = [ 2025-07-17T09:05:59.5300026Z >>> replace(plan, items=items) 2025-07-17T09:05:59.5300153Z >>> for plan, items in zip(all_plans, items_per_rank, strict=True) 2025-07-17T09:05:59.5300211Z >>> ] 2025-07-17T09:05:59.5300305Z >>> return super().create_global_plan(all_plans) 2025-07-17T09:05:59.5300365Z 2025-07-17T09:05:59.5300523Z Finally, some planners need to save additional metadata in the checkpoint, this is 2025-07-17T09:05:59.5300685Z accomplished by having each rank contribute their data items in the local plan and 2025-07-17T09:05:59.5300764Z the global planner aggregate them: 2025-07-17T09:05:59.5300826Z 2025-07-17T09:05:59.5300899Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T09:05:59.5300998Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2025-07-17T09:05:59.5301081Z >>> def create_local_plan(self) -> SavePlan: 2025-07-17T09:05:59.5301161Z >>> plan = super().create_local_plan() 2025-07-17T09:05:59.5301264Z >>> return replace(plan, planner_data="per-rank-data") 2025-07-17T09:05:59.5301320Z >>> 2025-07-17T09:05:59.5301495Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2025-07-17T09:05:59.5301618Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2025-07-17T09:05:59.5301715Z >>> merged_data = [p.planner_data for p in global_plan] 2025-07-17T09:05:59.5301821Z >>> metadata = replace(metadata, planner_data=merged_data) 2025-07-17T09:05:59.5301896Z >>> return global_plan, metadata 2025-07-17T09:05:59.5301961Z 2025-07-17T09:05:59.5302112Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5302169Z 2025-07-17T09:05:59.5302238Z warnings.warn(msg) 2025-07-17T09:05:59.5302292Z 2025-07-17T09:05:59.5302424Z --- Parse Warning: 86 / 136 --- 2025-07-17T09:05:59.5302951Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=LoadPlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/planner.py line=305. 2025-07-17T09:05:59.5303114Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5303168Z 2025-07-17T09:05:59.5303343Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2025-07-17T09:05:59.5303399Z 2025-07-17T09:05:59.5303568Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2025-07-17T09:05:59.5303621Z 2025-07-17T09:05:59.5303790Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2025-07-17T09:05:59.5303927Z will be visible to the whole process. 2025-07-17T09:05:59.5304032Z 2025-07-17T09:05:59.5304197Z A planner subclass can expect the following sequence of calls during load_state_dict: 2025-07-17T09:05:59.5304255Z 2025-07-17T09:05:59.5304332Z 1) set_up_planner - called on all ranks. 2025-07-17T09:05:59.5304416Z Signals the start of loading a checkpoint. 2025-07-17T09:05:59.5304469Z 2025-07-17T09:05:59.5304658Z 2) create_local_plan - called on all ranks. 2025-07-17T09:05:59.5304831Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2025-07-17T09:05:59.5304889Z 2025-07-17T09:05:59.5304998Z 3) create_global_plan - called on the coordinator rank only. 2025-07-17T09:05:59.5305119Z Takes the LoadPlan from all ranks and make any global decision. 2025-07-17T09:05:59.5305181Z 2025-07-17T09:05:59.5305332Z 4) load_bytes - called multiple times on each rank 2025-07-17T09:05:59.5305443Z This is called once per non-tensor value in state_dict. 2025-07-17T09:05:59.5305499Z 2025-07-17T09:05:59.5305639Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2025-07-17T09:05:59.5305754Z They are called in pair for each Tensor value in state_dict. 2025-07-17T09:05:59.5305814Z 2025-07-17T09:05:59.5305988Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2025-07-17T09:05:59.5306107Z most changes can be expressed by changes in a single method. 2025-07-17T09:05:59.5306162Z 2025-07-17T09:05:59.5306248Z There are two usual patterns of extension: 2025-07-17T09:05:59.5306303Z 2025-07-17T09:05:59.5306459Z Rewriting state_dict. This is the simplest way to extend the load process as it 2025-07-17T09:05:59.5306607Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2025-07-17T09:05:59.5306746Z to keep a reference to the original state_dict as load happens in place so 2025-07-17T09:05:59.5306835Z we need to be able to perform it in place 2025-07-17T09:05:59.5306898Z 2025-07-17T09:05:59.5306975Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T09:05:59.5307062Z >>> class RenamePlanner(DefaultLoadPlanner): 2025-07-17T09:05:59.5307135Z >>> def set_up_planner( 2025-07-17T09:05:59.5307193Z >>> self, 2025-07-17T09:05:59.5307269Z >>> state_dict: STATE_DICT_TYPE, 2025-07-17T09:05:59.5307341Z >>> metadata: Metadata, 2025-07-17T09:05:59.5307415Z >>> is_coordinator: bool, 2025-07-17T09:05:59.5307474Z >>> ) -> None: 2025-07-17T09:05:59.5307569Z >>> self.original_state_dict = state_dict 2025-07-17T09:05:59.5307679Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2025-07-17T09:05:59.5307742Z >>> 2025-07-17T09:05:59.5307818Z >>> if self.flatten_sharded_tensors: 2025-07-17T09:05:59.5307920Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2025-07-17T09:05:59.5307978Z >>> 2025-07-17T09:05:59.5308056Z >>> if self.flatten_state_dict: 2025-07-17T09:05:59.5308168Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2025-07-17T09:05:59.5308228Z >>> 2025-07-17T09:05:59.5308297Z >>> self.state_dict = state_dict 2025-07-17T09:05:59.5308366Z >>> self.metadata = metadata 2025-07-17T09:05:59.5308452Z >>> self.is_coordinator = is_coordinator 2025-07-17T09:05:59.5308509Z >>> 2025-07-17T09:05:59.5308597Z >>> def load_bytes(self, read_item, value): 2025-07-17T09:05:59.5308665Z >>> # Remove the "foo_" prefix 2025-07-17T09:05:59.5308857Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False) 2025-07-17T09:05:59.5308914Z 2025-07-17T09:05:59.5308978Z 2025-07-17T09:05:59.5309128Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2025-07-17T09:05:59.5309187Z 2025-07-17T09:05:59.5309349Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T09:05:59.5309520Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2025-07-17T09:05:59.5309595Z >>> def resolve_tensor(self, read_item): 2025-07-17T09:05:59.5309682Z >>> tensor = super().resolve_tensor(read_item) 2025-07-17T09:05:59.5309779Z >>> return torch.empty_like(tensor, device="cpu") 2025-07-17T09:05:59.5309843Z >>> 2025-07-17T09:05:59.5310056Z >>> def commit_tensor(self, read_item, tensor): 2025-07-17T09:05:59.5310163Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2025-07-17T09:05:59.5310220Z 2025-07-17T09:05:59.5310388Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5310447Z 2025-07-17T09:05:59.5310512Z warnings.warn(msg) 2025-07-17T09:05:59.5310567Z 2025-07-17T09:05:59.5310689Z --- Parse Warning: 87 / 136 --- 2025-07-17T09:05:59.5311228Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=get_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict.py line=1118. 2025-07-17T09:05:59.5311385Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5311447Z 2025-07-17T09:05:59.5311550Z Return the model state_dict and optimizers state_dict. 2025-07-17T09:05:59.5311607Z 2025-07-17T09:05:59.5311741Z ``get_state_dict`` can process any module that is parallelized by PyTorch 2025-07-17T09:05:59.5311897Z FSDP/fully_shard, DDP/replicate, tensor_parallel/parallelize_module, and any 2025-07-17T09:05:59.5312043Z combination of these parallelisms. The main functions of ``get_state_dict`` 2025-07-17T09:05:59.5312177Z are: 1.) returning a model and optimizer state_dict that can be resharded 2025-07-17T09:05:59.5312306Z with a different number of trainers and/or different parallelisms. 2025-07-17T09:05:59.5312458Z 2.) hiding the parallelism-specific state_dict APIs. Users don't have to call 2025-07-17T09:05:59.5312521Z these APIs. 2025-07-17T09:05:59.5312605Z 3.) sanity checking the result state_dict. 2025-07-17T09:05:59.5312658Z 2025-07-17T09:05:59.5312791Z The keys of the result state dictionary are the canonical FQNs (Fully 2025-07-17T09:05:59.5312927Z Qualified Names). A canonical FQN refers to the FQN based on a parameter's 2025-07-17T09:05:59.5313070Z position in an nn.Module hierarchy. More specifically, a canonical FQN to a 2025-07-17T09:05:59.5313192Z parameter is the FQN returned by ``module.named_parameters()`` or 2025-07-17T09:05:59.5313317Z ``module.named_buffers()`` when the module is not distributed by any 2025-07-17T09:05:59.5313476Z parallelisms. Since the optimizer internally uses parameter IDs to represent 2025-07-17T09:05:59.5313604Z a parameter, there will be a conversion from the parameter IDs to the 2025-07-17T09:05:59.5313684Z canonical FQNs when calling this API. 2025-07-17T09:05:59.5313742Z 2025-07-17T09:05:59.5313874Z ``get_state_dict`` can also process a module that is not parallelized. In 2025-07-17T09:05:59.5314002Z such a case, ``get_state_dict`` only performs one function -- converting the 2025-07-17T09:05:59.5314098Z optimizer parameter IDs to the canonical FQNs. 2025-07-17T09:05:59.5314156Z 2025-07-17T09:05:59.5314219Z Example: 2025-07-17T09:05:59.5314291Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.5314363Z >>> import torch 2025-07-17T09:05:59.5314500Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-07-17T09:05:59.5314620Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2025-07-17T09:05:59.5314753Z >>> from torch.distributed.checkpoint.state_dict import get_state_dict 2025-07-17T09:05:59.5314814Z 2025-07-17T09:05:59.5314896Z >>> fsdp_model = FSDP(copy.deepcopy(model)) 2025-07-17T09:05:59.5315012Z >>> fsdp_optim = torch.optim.Adam(model.parameters(), lr=1e-3) 2025-07-17T09:05:59.5315205Z >>> ddp_model = DDP(copy.deepcopy(model)) 2025-07-17T09:05:59.5315315Z >>> ddp_optim = torch.optim.Adam(model.parameters(), lr=1e-3) 2025-07-17T09:05:59.5315373Z 2025-07-17T09:05:59.5315429Z 2025-07-17T09:05:59.5315573Z >>> ddp_state_dict, ddp_optim_state_dict = get_state_dict(ddp_model, ddp_optim) 2025-07-17T09:05:59.5315796Z >>> fsdp_state_dict, fsdp_optim_state_dict = get_state_dict( 2025-07-17T09:05:59.5315879Z ... fsdp_model, fsdp_optim 2025-07-17T09:05:59.5315937Z ... ) 2025-07-17T09:05:59.5315993Z 2025-07-17T09:05:59.5316126Z >>> # if we simply call ddp_model.state_dict() and fsdp_model.state_dict(), 2025-07-17T09:05:59.5316202Z >>> # the asserts will fail. 2025-07-17T09:05:59.5316291Z >>> assert ddp_state_dict == fsdp_state_dict 2025-07-17T09:05:59.5316391Z >>> assert ddp_optim_state == fsdp_optim_state_dict 2025-07-17T09:05:59.5316448Z 2025-07-17T09:05:59.5316509Z 2025-07-17T09:05:59.5316568Z Args: 2025-07-17T09:05:59.5316666Z model (nn.Module): the nn.Module to the model. 2025-07-17T09:05:59.5316783Z optimizers (Union[None, Optimizer, Iterable[Optimizer]]): 2025-07-17T09:05:59.5316891Z The optimizers that are used to optimize ``model``. 2025-07-17T09:05:59.5317066Z submodules (deprecated): Optional[set[nn.Module]]: only return the model parameters 2025-07-17T09:05:59.5317143Z that belong to the submodules. 2025-07-17T09:05:59.5317256Z options (StateDictOptions): the options to control how 2025-07-17T09:05:59.5317390Z model state_dict and optimizer state_dict should be returned. See 2025-07-17T09:05:59.5317478Z `StateDictOptions` for the details. 2025-07-17T09:05:59.5317533Z 2025-07-17T09:05:59.5317590Z Returns: 2025-07-17T09:05:59.5317710Z ``Tuple`` that contain model state_dict and optimizer state_dict. 2025-07-17T09:05:59.5317769Z 2025-07-17T09:05:59.5317907Z :rtype: typing.Tuple[typing.Dict[str, ValueType], OptimizerStateType] 2025-07-17T09:05:59.5317965Z 2025-07-17T09:05:59.5318116Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5318172Z 2025-07-17T09:05:59.5318239Z warnings.warn(msg) 2025-07-17T09:05:59.5318295Z 2025-07-17T09:05:59.5318428Z --- Parse Warning: 88 / 136 --- 2025-07-17T09:05:59.5318962Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=save in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=96. 2025-07-17T09:05:59.5319123Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5319180Z 2025-07-17T09:05:59.5319260Z Save a distributed model in SPMD style. 2025-07-17T09:05:59.5319323Z 2025-07-17T09:05:59.5319446Z This function is different from ``torch.save()`` as it handles 2025-07-17T09:05:59.5319605Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2025-07-17T09:05:59.5319666Z 2025-07-17T09:05:59.5319818Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2025-07-17T09:05:59.5319923Z save will call ``state_dict`` before serialization. 2025-07-17T09:05:59.5319976Z 2025-07-17T09:05:59.5320045Z .. warning:: 2025-07-17T09:05:59.5320184Z There is no guarantees of Backwards Compatibility across PyTorch versions 2025-07-17T09:05:59.5320262Z for saved state_dicts. 2025-07-17T09:05:59.5320315Z 2025-07-17T09:05:59.5320381Z .. warning:: 2025-07-17T09:05:59.5320514Z If using the `process_group` argument, make sure that only its ranks 2025-07-17T09:05:59.5320643Z call `save_state_dict` and that all data in state_dict belong to it. 2025-07-17T09:05:59.5320697Z 2025-07-17T09:05:59.5320823Z .. note:: 2025-07-17T09:05:59.5320976Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2025-07-17T09:05:59.5321184Z the shard_group should be calling `save_state_dict` and the corresponding process 2025-07-17T09:05:59.5321257Z group needs to be passed in. 2025-07-17T09:05:59.5321315Z 2025-07-17T09:05:59.5321371Z .. note:: 2025-07-17T09:05:59.5321631Z If no process group is available, this function assumes the intention is to save the 2025-07-17T09:05:59.5321713Z state_dict in the local process. 2025-07-17T09:05:59.5321766Z 2025-07-17T09:05:59.5321826Z .. note: 2025-07-17T09:05:59.5321918Z Rank 0 is assumed to be the coordinator rank. 2025-07-17T09:05:59.5321974Z 2025-07-17T09:05:59.5322032Z 2025-07-17T09:05:59.5322092Z Args: 2025-07-17T09:05:59.5322197Z state_dict (Dict[str, Any]): The state_dict to save. 2025-07-17T09:05:59.5322291Z checkpoint_id (Union[str, os.PathLike, None]): 2025-07-17T09:05:59.5322425Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-07-17T09:05:59.5322557Z depends on the storage. It can be a path to a folder or to a file. 2025-07-17T09:05:59.5322667Z It can also be a key if the storage is a key-value store. 2025-07-17T09:05:59.5322737Z (Default: ``None``) 2025-07-17T09:05:59.5322823Z storage_writer (Optional[StorageWriter]): 2025-07-17T09:05:59.5322953Z Instance of StorageWriter used to perform writes. If this is not 2025-07-17T09:05:59.5323076Z specified, DCP will automatically infer the writer based on the 2025-07-17T09:05:59.5323200Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-07-17T09:05:59.5323276Z be raised. (Default: ``None``) 2025-07-17T09:05:59.5323354Z planner (Optional[SavePlanner]): 2025-07-17T09:05:59.5323482Z Instance of SavePlanner. If this is not specified, the default 2025-07-17T09:05:59.5323567Z planner will be used. (Default: ``None``) 2025-07-17T09:05:59.5323663Z process_group (Optional[ProcessGroup]): 2025-07-17T09:05:59.5323778Z ProcessGroup to be used for cross-rank synchronization. 2025-07-17T09:05:59.5323852Z (Default: ``None``) 2025-07-17T09:05:59.5323914Z no_dist (bool): 2025-07-17T09:05:59.5324027Z If ``True``, this function will assume the intent is to load 2025-07-17T09:05:59.5324136Z a checkpoint without using cross-rank synchronization. 2025-07-17T09:05:59.5324207Z (Default: ``False``) 2025-07-17T09:05:59.5324262Z 2025-07-17T09:05:59.5324326Z Returns: 2025-07-17T09:05:59.5324430Z Metadata: Metadata object for the saved checkpoint. 2025-07-17T09:05:59.5324487Z 2025-07-17T09:05:59.5324543Z Example: 2025-07-17T09:05:59.5324612Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.5324682Z >>> my_model = MyModule() 2025-07-17T09:05:59.5324736Z 2025-07-17T09:05:59.5324817Z >>> state_dict = {"model": my_model} 2025-07-17T09:05:59.5324873Z 2025-07-17T09:05:59.5325017Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter( 2025-07-17T09:05:59.5325082Z ... "/checkpoint/1" 2025-07-17T09:05:59.5325141Z ... ) 2025-07-17T09:05:59.5325229Z >>> torch.distributed.checkpoint.save( 2025-07-17T09:05:59.5325303Z >>> state_dict=state_dict, 2025-07-17T09:05:59.5325381Z >>> storage_writer=fs_storage_writer, 2025-07-17T09:05:59.5325439Z >>> ) 2025-07-17T09:05:59.5325492Z 2025-07-17T09:05:59.5325556Z .. note:: 2025-07-17T09:05:59.5325687Z save_state_dict uses collectives to coordinate writes across ranks. 2025-07-17T09:05:59.5325818Z For NCCL-based process groups, internal tensor representations of 2025-07-17T09:05:59.5325959Z objects must be moved to the GPU device before communication takes place. 2025-07-17T09:05:59.5326160Z In this case, the device used is given by ``torch.cuda.current_device()`` 2025-07-17T09:05:59.5326334Z and it is the user's responsibility to ensure that this is set so that 2025-07-17T09:05:59.5326449Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2025-07-17T09:05:59.5326510Z 2025-07-17T09:05:59.5326656Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5326717Z 2025-07-17T09:05:59.5326888Z warnings.warn(msg) 2025-07-17T09:05:59.5326951Z 2025-07-17T09:05:59.5327067Z --- Parse Warning: 89 / 136 --- 2025-07-17T09:05:59.5327615Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=async_save in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=221. 2025-07-17T09:05:59.5327769Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5327935Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2025-07-17T09:05:59.5328109Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2025-07-17T09:05:59.5328168Z 2025-07-17T09:05:59.5328234Z .. warning:: 2025-07-17T09:05:59.5328343Z This feature is experimental and subject to change. 2025-07-17T09:05:59.5328438Z MUST CALL CLOSE AFTER LAST CHECKPOINT IS SAVED 2025-07-17T09:05:59.5328498Z 2025-07-17T09:05:59.5328555Z Args: 2025-07-17T09:05:59.5328663Z state_dict (Dict[str, Any]): The state_dict to save. 2025-07-17T09:05:59.5328759Z checkpoint_id (Union[str, os.PathLike, None]): 2025-07-17T09:05:59.5328891Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-07-17T09:05:59.5329014Z depends on the storage. It can be a path to a folder or to a file. 2025-07-17T09:05:59.5329125Z It can also be a key if the storage is a key-value store. 2025-07-17T09:05:59.5329195Z (Default: ``None``) 2025-07-17T09:05:59.5329289Z storage_writer (Optional[StorageWriter]): 2025-07-17T09:05:59.5329412Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2025-07-17T09:05:59.5329560Z this is not specified, DCP will automatically infer the writer based on the 2025-07-17T09:05:59.5329689Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-07-17T09:05:59.5329769Z be raised. (Default: ``None``) 2025-07-17T09:05:59.5329851Z planner (Optional[SavePlanner]): 2025-07-17T09:05:59.5329971Z Instance of SavePlanner. If this is not specified, the default 2025-07-17T09:05:59.5330057Z planner will be used. (Default: ``None``) 2025-07-17T09:05:59.5330143Z process_group (Optional[ProcessGroup]): 2025-07-17T09:05:59.5330261Z ProcessGroup to be used for cross-rank synchronization. 2025-07-17T09:05:59.5330330Z (Default: ``None``) 2025-07-17T09:05:59.5330434Z async_checkpointer_type (AsyncCheckpointerType): 2025-07-17T09:05:59.5330544Z whether to do checkpoint in separate thread or process 2025-07-17T09:05:59.5330645Z (Default: ``AsyncCheckpointerType.THREAD``) 2025-07-17T09:05:59.5330715Z async_stager (AsyncStager): 2025-07-17T09:05:59.5330875Z provides staging implementation. If storage_writer implements AsyncStager 2025-07-17T09:05:59.5331002Z and async_stager is provided, async_stager will be used for staging 2025-07-17T09:05:59.5331060Z 2025-07-17T09:05:59.5331116Z Returns: 2025-07-17T09:05:59.5331247Z Future: A future holding the resultant Metadata object from `save`. 2025-07-17T09:05:59.5331303Z 2025-07-17T09:05:59.5331364Z Example: 2025-07-17T09:05:59.5331440Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.5331581Z >>> my_model = MyModule() 2025-07-17T09:05:59.5331689Z 2025-07-17T09:05:59.5331765Z >>> state_dict = {"model": my_model} 2025-07-17T09:05:59.5331822Z 2025-07-17T09:05:59.5331964Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter( 2025-07-17T09:05:59.5332033Z ... "/checkpoint/1" 2025-07-17T09:05:59.5332088Z ... ) 2025-07-17T09:05:59.5332322Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2025-07-17T09:05:59.5332399Z >>> state_dict=state_dict, 2025-07-17T09:05:59.5332482Z >>> storage_writer=fs_storage_writer, 2025-07-17T09:05:59.5332542Z >>> ) 2025-07-17T09:05:59.5332606Z >>> 2025-07-17T09:05:59.5332673Z >>> # ... do some work ... 2025-07-17T09:05:59.5332738Z >>> 2025-07-17T09:05:59.5332812Z >>> checkpoint_future.result() 2025-07-17T09:05:59.5332872Z 2025-07-17T09:05:59.5332928Z 2025-07-17T09:05:59.5333074Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5333138Z 2025-07-17T09:05:59.5333205Z warnings.warn(msg) 2025-07-17T09:05:59.5333264Z 2025-07-17T09:05:59.5333388Z --- Parse Warning: 90 / 136 --- 2025-07-17T09:05:59.5333916Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_loader.py line=62. 2025-07-17T09:05:59.5334072Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5334136Z 2025-07-17T09:05:59.5334259Z Load a checkpoint into a distributed state dict in SPMD style. 2025-07-17T09:05:59.5334320Z 2025-07-17T09:05:59.5334456Z Each rank must have the same keys in their ``state_dict`` provided to this 2025-07-17T09:05:59.5334599Z API. Mismatched keys may result in hangs or errors. If unsure, you can use 2025-07-17T09:05:59.5334730Z the ``utils._assert_same_keys`` API to check (but may incur communication 2025-07-17T09:05:59.5334792Z costs). 2025-07-17T09:05:59.5334848Z 2025-07-17T09:05:59.5334965Z Each rank will try to read the least amount of data necessary 2025-07-17T09:05:59.5335098Z to fulfill the requested `state_dict`. When loading :class:`ShardedTensor` 2025-07-17T09:05:59.5335250Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2025-07-17T09:05:59.5335307Z 2025-07-17T09:05:59.5335464Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2025-07-17T09:05:59.5335615Z load will first call ``state_dict`` before attempting deserialization, followed by 2025-07-17T09:05:59.5335729Z ``load_state_dict`` once the deserialization is complete. 2025-07-17T09:05:59.5335882Z For each non-``Stateful`` object, load will deserialize the object, and then replace 2025-07-17T09:05:59.5335979Z it in the ``state_dict`` with the deserialized object. 2025-07-17T09:05:59.5336040Z 2025-07-17T09:05:59.5336099Z .. warning:: 2025-07-17T09:05:59.5336211Z All tensors in ``state_dict`` must be allocated on their 2025-07-17T09:05:59.5336317Z destination device *prior to* calling this function. 2025-07-17T09:05:59.5336376Z 2025-07-17T09:05:59.5336514Z All non-tensor data is loaded using `torch.load()` and modified in place 2025-07-17T09:05:59.5336586Z on state_dict. 2025-07-17T09:05:59.5336646Z 2025-07-17T09:05:59.5336711Z .. warning:: 2025-07-17T09:05:59.5336834Z Users must call `load_state_dict` on the root module to ensure load 2025-07-17T09:05:59.5336950Z pos-processing and non-tensor data properly propagates. 2025-07-17T09:05:59.5337003Z 2025-07-17T09:05:59.5337060Z .. note: 2025-07-17T09:05:59.5337190Z If no process group is initialized, this function will assume the intent 2025-07-17T09:05:59.5337396Z is to load a checkpoint into the local process. This can be useful in the 2025-07-17T09:05:59.5337601Z case of local inference, and when using regular Tensors (as opposed to DTensor 2025-07-17T09:05:59.5337674Z or ShardedTensor) 2025-07-17T09:05:59.5337736Z 2025-07-17T09:05:59.5337794Z .. note: 2025-07-17T09:05:59.5337893Z Rank 0 is assumed to be the coordinator rank. 2025-07-17T09:05:59.5338080Z 2025-07-17T09:05:59.5338152Z Args: 2025-07-17T09:05:59.5338281Z state_dict (Dict[str, Any]): The state_dict to load the checkpoint into. 2025-07-17T09:05:59.5338389Z checkpoint_id (Union[str, os.PathLike, None]): 2025-07-17T09:05:59.5338515Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-07-17T09:05:59.5338642Z depends on the storage. It can be a path to a folder or to a file. 2025-07-17T09:05:59.5338752Z It can also be a key if the storage is a key-value store. 2025-07-17T09:05:59.5338831Z (Default: ``None``) 2025-07-17T09:05:59.5338917Z storage_reader (Optional[StorageReader]): 2025-07-17T09:05:59.5339052Z Instance of StorageWriter used to perform reads. If this is not 2025-07-17T09:05:59.5339174Z specified, DCP will automatically infer the reader based on the 2025-07-17T09:05:59.5339300Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-07-17T09:05:59.5339377Z be raised. (Default: ``None``) 2025-07-17T09:05:59.5339463Z planner (Optional[LoadPlanner]): 2025-07-17T09:05:59.5339584Z Instance of LoadPlanner. If this is not specified, the default 2025-07-17T09:05:59.5339676Z planner will be used. (Default: ``None``) 2025-07-17T09:05:59.5339761Z process_group (Optional[ProcessGroup]): 2025-07-17T09:05:59.5339886Z ProcessGroup to be used for cross-rank synchronization. 2025-07-17T09:05:59.5339954Z (Default: ``None``) 2025-07-17T09:05:59.5340083Z no_dist (bool): If ``True``, this function will assume the intent is to load 2025-07-17T09:05:59.5340246Z a checkpoint without using cross-rank synchronization. (Default: ``False``) 2025-07-17T09:05:59.5340307Z Returns: 2025-07-17T09:05:59.5340370Z None. 2025-07-17T09:05:59.5340428Z 2025-07-17T09:05:59.5340489Z Examples 2025-07-17T09:05:59.5340558Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.5340633Z >>> my_model = MyModule() 2025-07-17T09:05:59.5340723Z >>> optimizer = Adagrad(my_model.parameters()) 2025-07-17T09:05:59.5340814Z >>> model_state_dict = my_model.state_dict() 2025-07-17T09:05:59.5340955Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader( 2025-07-17T09:05:59.5341029Z ... "/checkpoint/1" 2025-07-17T09:05:59.5341087Z ... ) 2025-07-17T09:05:59.5341148Z 2025-07-17T09:05:59.5341262Z >>> torch.distributed.checkpoint.load_state_dict( 2025-07-17T09:05:59.5341343Z >>> state_dict=model_state_dict, 2025-07-17T09:05:59.5341430Z >>> storage_reader=fs_storage_reader, 2025-07-17T09:05:59.5341484Z >>> ) 2025-07-17T09:05:59.5341546Z 2025-07-17T09:05:59.5341666Z >>> # module.load_state_dict() function might have customized steps 2025-07-17T09:05:59.5341753Z >>> # to flush the state_dict, must call it to 2025-07-17T09:05:59.5341830Z >>> # ensure correct behavior. 2025-07-17T09:05:59.5341921Z >>> my_model.load_state_dict(model_state_dict) 2025-07-17T09:05:59.5341976Z 2025-07-17T09:05:59.5342044Z .. note:: 2025-07-17T09:05:59.5342173Z load_state_dict uses collectives to coordinate reads across ranks. 2025-07-17T09:05:59.5342311Z For NCCL-based process groups, internal tensor representations of 2025-07-17T09:05:59.5342450Z objects must be moved to the GPU device before communication takes place. 2025-07-17T09:05:59.5342590Z In this case, the device used is given by ``torch.cuda.current_device()`` 2025-07-17T09:05:59.5342894Z and it is the user's responsibility to ensure that this is set so that each 2025-07-17T09:05:59.5343014Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2025-07-17T09:05:59.5343070Z 2025-07-17T09:05:59.5343223Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5343279Z 2025-07-17T09:05:59.5343456Z warnings.warn(msg) 2025-07-17T09:05:59.5343516Z 2025-07-17T09:05:59.5343642Z --- Parse Warning: 91 / 136 --- 2025-07-17T09:05:59.5344235Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BroadcastingTorchSaveReader in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/format_utils.py line=40. 2025-07-17T09:05:59.5344386Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5344455Z 2025-07-17T09:05:59.5344634Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2025-07-17T09:05:59.5344785Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2025-07-17T09:05:59.5344837Z 2025-07-17T09:05:59.5344942Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2025-07-17T09:05:59.5344998Z 2025-07-17T09:05:59.5345063Z .. warning:: 2025-07-17T09:05:59.5345178Z Current implementation only supports loading Tensors. 2025-07-17T09:05:59.5345242Z 2025-07-17T09:05:59.5345422Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T09:05:59.5345494Z >>> sd = {"mode": model} 2025-07-17T09:05:59.5345554Z >>> dcp.load( 2025-07-17T09:05:59.5345617Z >>> sd, 2025-07-17T09:05:59.5345718Z >>> storage_reader=BroadcastingTorchSaveReader(), 2025-07-17T09:05:59.5345801Z >>> planner=DynamicMetaLoadPlanner(), 2025-07-17T09:05:59.5345878Z >>> checkpoint_id="path_to_model.pt" 2025-07-17T09:05:59.5345937Z >>> ) 2025-07-17T09:05:59.5346004Z 2025-07-17T09:05:59.5346150Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5346211Z 2025-07-17T09:05:59.5346275Z warnings.warn(msg) 2025-07-17T09:05:59.5346338Z 2025-07-17T09:05:59.5346459Z --- Parse Warning: 92 / 136 --- 2025-07-17T09:05:59.5347038Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DynamicMetaLoadPlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/format_utils.py line=151. 2025-07-17T09:05:59.5347191Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5347250Z 2025-07-17T09:05:59.5347462Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2025-07-17T09:05:59.5347652Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2025-07-17T09:05:59.5347734Z metadata file, like Torch Save files. 2025-07-17T09:05:59.5347793Z 2025-07-17T09:05:59.5347906Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2025-07-17T09:05:59.5347972Z 2025-07-17T09:05:59.5348036Z .. warning:: 2025-07-17T09:05:59.5348146Z Current implementation only supports loading Tensors. 2025-07-17T09:05:59.5348206Z 2025-07-17T09:05:59.5348285Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T09:05:59.5348349Z >>> sd = {"mode": model} 2025-07-17T09:05:59.5348408Z >>> dcp.load( 2025-07-17T09:05:59.5348471Z >>> sd, 2025-07-17T09:05:59.5348567Z >>> storage_reader=BroadcastingTorchSaveReader(), 2025-07-17T09:05:59.5348652Z >>> planner=DynamicMetaLoadPlanner(), 2025-07-17T09:05:59.5348727Z >>> checkpoint_id="path_to_model.pt" 2025-07-17T09:05:59.5348788Z >>> ) 2025-07-17T09:05:59.5348842Z 2025-07-17T09:05:59.5349078Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5349198Z 2025-07-17T09:05:59.5349272Z warnings.warn(msg) 2025-07-17T09:05:59.5349325Z 2025-07-17T09:05:59.5349447Z --- Parse Warning: 93 / 136 --- 2025-07-17T09:05:59.5350126Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=init_from_local_shards in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py line=361. 2025-07-17T09:05:59.5350290Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5350345Z 2025-07-17T09:05:59.5350492Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2025-07-17T09:05:59.5350590Z Needs to be called on all ranks in an SPMD fashion. 2025-07-17T09:05:59.5350651Z 2025-07-17T09:05:59.5350706Z Args: 2025-07-17T09:05:59.5350874Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2025-07-17T09:05:59.5350992Z of shards that represent the local shards on this rank. 2025-07-17T09:05:59.5351134Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2025-07-17T09:05:59.5351226Z shape of the overall sharded tensor. 2025-07-17T09:05:59.5351284Z 2025-07-17T09:05:59.5351355Z Keyword args: 2025-07-17T09:05:59.5351517Z process_group (ProcessGroup, optional): The process group to work on. If None, 2025-07-17T09:05:59.5351607Z the default process group will be used. 2025-07-17T09:05:59.5351719Z init_rrefs (bool, optional): Whether or not to initialize 2025-07-17T09:05:59.5351853Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2025-07-17T09:05:59.5351975Z Need to initialize the RPC Framework if specified as ``True``. 2025-07-17T09:05:59.5352050Z Default: ``False``. 2025-07-17T09:05:59.5352106Z 2025-07-17T09:05:59.5352167Z Returns: 2025-07-17T09:05:59.5352264Z A :class:`ShardedTensor` object handle on this rank 2025-07-17T09:05:59.5352325Z 2025-07-17T09:05:59.5352378Z 2025-07-17T09:05:59.5352439Z Examples: 2025-07-17T09:05:59.5352598Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2025-07-17T09:05:59.5352718Z each shard have a (5, 5) local tensor, we can do it like below: 2025-07-17T09:05:59.5352782Z 2025-07-17T09:05:59.5352845Z on rank 0: 2025-07-17T09:05:59.5352929Z >>> # xdoctest: +SKIP("not distributed") 2025-07-17T09:05:59.5353009Z >>> local_shard_metadata = ShardMetadata( 2025-07-17T09:05:59.5353081Z >>> shard_offsets=[0, 0], 2025-07-17T09:05:59.5353148Z >>> shard_lengths=[5, 5], 2025-07-17T09:05:59.5353226Z >>> placement="rank:0/cuda:0" 2025-07-17T09:05:59.5353281Z >>> ) 2025-07-17T09:05:59.5353405Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2025-07-17T09:05:59.5353528Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2025-07-17T09:05:59.5353586Z 2025-07-17T09:05:59.5353643Z on rank 1: 2025-07-17T09:05:59.5353718Z >>> # xdoctest: +SKIP("not distributed") 2025-07-17T09:05:59.5353794Z >>> local_shard_metadata = ShardMetadata( 2025-07-17T09:05:59.5353861Z >>> shard_offsets=[5, 0], 2025-07-17T09:05:59.5353927Z >>> shard_lengths=[5, 5], 2025-07-17T09:05:59.5353997Z >>> placement="rank:1/cuda:1" 2025-07-17T09:05:59.5354054Z >>> ) 2025-07-17T09:05:59.5354165Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2025-07-17T09:05:59.5354283Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2025-07-17T09:05:59.5354338Z 2025-07-17T09:05:59.5354495Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5354616Z 2025-07-17T09:05:59.5354738Z warnings.warn(msg) 2025-07-17T09:05:59.5354791Z 2025-07-17T09:05:59.5354917Z --- Parse Warning: 94 / 136 --- 2025-07-17T09:05:59.5355454Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardingPlan in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharding_plan/api.py line=12. 2025-07-17T09:05:59.5355727Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5355787Z 2025-07-17T09:05:59.5355927Z Representation of a sharding plan, describes how to shard a module 2025-07-17T09:05:59.5356087Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2025-07-17T09:05:59.5356260Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2025-07-17T09:05:59.5356412Z layout of a module with a spec, and when to convert back to data parallel fashion. 2025-07-17T09:05:59.5356478Z 2025-07-17T09:05:59.5356537Z Args: 2025-07-17T09:05:59.5356701Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2025-07-17T09:05:59.5356809Z :class:`torch.distributed._shard.sharder.Sharder`]): 2025-07-17T09:05:59.5356972Z a dict describes how to shard a module, there're currently two ways to shard a module: 2025-07-17T09:05:59.5357125Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2025-07-17T09:05:59.5357204Z a parameter to a `ShardingSpec`. 2025-07-17T09:05:59.5357371Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2025-07-17T09:05:59.5357444Z to a `Sharder` object. 2025-07-17T09:05:59.5357643Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2025-07-17T09:05:59.5357796Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2025-07-17T09:05:59.5357945Z keyed by the name of module to ShardingSpec("" in key means the root module). 2025-07-17T09:05:59.5358014Z Default: `None` 2025-07-17T09:05:59.5358165Z return_local_tensor (List[str], optional): a list of string, each element enables 2025-07-17T09:05:59.5358308Z a module's sharded output to be returned as a Tensor from its local shards to 2025-07-17T09:05:59.5358459Z ensure further processing in a data parallel fashion. ("" in list means the 2025-07-17T09:05:59.5358523Z root module). 2025-07-17T09:05:59.5358596Z Default: None 2025-07-17T09:05:59.5358653Z Example: 2025-07-17T09:05:59.5358827Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2025-07-17T09:05:59.5358993Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2025-07-17T09:05:59.5359055Z 2025-07-17T09:05:59.5359166Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2025-07-17T09:05:59.5359251Z >>> class MyModule(nn.Module): 2025-07-17T09:05:59.5359328Z >>> def __init__(self) -> None: 2025-07-17T09:05:59.5359399Z >>> super().__init__() 2025-07-17T09:05:59.5359466Z >>> self.fc1 = nn.Linear() 2025-07-17T09:05:59.5359544Z >>> self.gelu = nn.GELU() 2025-07-17T09:05:59.5359613Z >>> self.fc2 = nn.Linear() 2025-07-17T09:05:59.5359693Z >>> self.relu = nn.Linear() 2025-07-17T09:05:59.5359748Z >>> 2025-07-17T09:05:59.5359818Z >>> def forward(self, input): 2025-07-17T09:05:59.5359927Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2025-07-17T09:05:59.5359980Z 2025-07-17T09:05:59.5360036Z 2025-07-17T09:05:59.5360122Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2025-07-17T09:05:59.5360263Z >>> sharding_plan = ShardingPlan( 2025-07-17T09:05:59.5360392Z >>> plan={ 2025-07-17T09:05:59.5360470Z >>> "fc1.weight": spec1, 2025-07-17T09:05:59.5360538Z >>> "fc2.weight": spec2 2025-07-17T09:05:59.5360602Z >>> }, 2025-07-17T09:05:59.5360672Z >>> output_plan={ 2025-07-17T09:05:59.5360744Z >>> "fc2": output_spec 2025-07-17T09:05:59.5360798Z >>> }, 2025-07-17T09:05:59.5360970Z >>> return_local_tensor=["fc2"] 2025-07-17T09:05:59.5361030Z >>> ) 2025-07-17T09:05:59.5361088Z 2025-07-17T09:05:59.5361249Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5361306Z 2025-07-17T09:05:59.5361373Z warnings.warn(msg) 2025-07-17T09:05:59.5361426Z 2025-07-17T09:05:59.5361552Z --- Parse Warning: 95 / 136 --- 2025-07-17T09:05:59.5362145Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardedTensor._init_from_local_tensor in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=835. 2025-07-17T09:05:59.5362302Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5362358Z 2025-07-17T09:05:59.5362524Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2025-07-17T09:05:59.5362602Z size and sharding spec on each rank. 2025-07-17T09:05:59.5362666Z 2025-07-17T09:05:59.5362723Z Args: 2025-07-17T09:05:59.5362861Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2025-07-17T09:05:59.5363016Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2025-07-17T09:05:59.5363134Z The specification describing how to shard the Tensor. 2025-07-17T09:05:59.5363243Z global_size (Sequence[int]): Size of the sharded tensor. 2025-07-17T09:05:59.5363400Z process_group (ProcessGroup, optional): The process group to aggregate on. 2025-07-17T09:05:59.5363467Z Default: None 2025-07-17T09:05:59.5363574Z init_rrefs (bool, optional): Whether or not to initialize 2025-07-17T09:05:59.5363702Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2025-07-17T09:05:59.5363827Z Need to initialize the RPC Framework if specified as ``True``. 2025-07-17T09:05:59.5363900Z Default: ``False``. 2025-07-17T09:05:59.5363958Z 2025-07-17T09:05:59.5364016Z Returns: 2025-07-17T09:05:59.5364161Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2025-07-17T09:05:59.5364242Z tensor stored in the current rank. 2025-07-17T09:05:59.5364295Z 2025-07-17T09:05:59.5364354Z Examples: 2025-07-17T09:05:59.5364421Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.5364516Z >>> # All tensors below are of torch.int64 type. 2025-07-17T09:05:59.5364596Z >>> # We have 2 process groups, 2 ranks. 2025-07-17T09:05:59.5364714Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2025-07-17T09:05:59.5364837Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2025-07-17T09:05:59.5364913Z >>> local_tensor 2025-07-17T09:05:59.5364983Z tensor([[1, 2, 3, 4]]) # Rank 0 2025-07-17T09:05:59.5365055Z tensor([[3, 4, 5, 6]]) # Rank 1 2025-07-17T09:05:59.5365124Z >>> sharding_dim = 0 2025-07-17T09:05:59.5365214Z >>> sharding_spec = ChunkShardingSpec( 2025-07-17T09:05:59.5365282Z dim=sharding_dim, 2025-07-17T09:05:59.5365348Z placements=[ 2025-07-17T09:05:59.5365422Z "rank:0/cuda:0", 2025-07-17T09:05:59.5365488Z "rank:1/cuda:1", 2025-07-17T09:05:59.5365556Z ], 2025-07-17T09:05:59.5365612Z ) 2025-07-17T09:05:59.5365702Z >>> st = ShardedTensor._init_from_local_tensor( 2025-07-17T09:05:59.5365844Z ... local_tensor, sharding_spec, [2, 4] 2025-07-17T09:05:59.5365953Z ... ) 2025-07-17T09:05:59.5366014Z >>> st 2025-07-17T09:05:59.5366084Z ShardedTensor( 2025-07-17T09:05:59.5366156Z ShardedTensorMetadata( 2025-07-17T09:05:59.5366224Z shards_metadata=[ 2025-07-17T09:05:59.5366489Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2025-07-17T09:05:59.5366652Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2025-07-17T09:05:59.5366708Z ], 2025-07-17T09:05:59.5366785Z size=torch.Size([2, 4]) 2025-07-17T09:05:59.5366845Z ) 2025-07-17T09:05:59.5366915Z >>> st.local_tensor() 2025-07-17T09:05:59.5366987Z tensor([1, 2, 3, 4]) # Rank 0 2025-07-17T09:05:59.5367053Z tensor([3, 4, 5, 6]) # Rank 1 2025-07-17T09:05:59.5367115Z 2025-07-17T09:05:59.5367281Z Warning: This API is experimental and subject to change. It lacks of a fully across 2025-07-17T09:05:59.5367435Z rank validations, and we only validate the local shard on the current rank. 2025-07-17T09:05:59.5367563Z We fully rely on the user to ensure local tensor is sharded based on the 2025-07-17T09:05:59.5367635Z sharding spec. 2025-07-17T09:05:59.5367689Z 2025-07-17T09:05:59.5367851Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5367909Z 2025-07-17T09:05:59.5367978Z warnings.warn(msg) 2025-07-17T09:05:59.5368034Z 2025-07-17T09:05:59.5368163Z --- Parse Warning: 96 / 136 --- 2025-07-17T09:05:59.5368731Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardedTensor.reshard in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=1076. 2025-07-17T09:05:59.5368889Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5368949Z 2025-07-17T09:05:59.5369107Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2025-07-17T09:05:59.5369173Z single local shard. 2025-07-17T09:05:59.5369229Z 2025-07-17T09:05:59.5369360Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2025-07-17T09:05:59.5369508Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2025-07-17T09:05:59.5369590Z we swap local shards directly. 2025-07-17T09:05:59.5369743Z For more generic cases, we merge different shards across different ranks and split 2025-07-17T09:05:59.5369896Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2025-07-17T09:05:59.5369950Z 2025-07-17T09:05:59.5370013Z Args: 2025-07-17T09:05:59.5370185Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2025-07-17T09:05:59.5370299Z specification describing how the tensor is sharded. 2025-07-17T09:05:59.5370356Z 2025-07-17T09:05:59.5370417Z Returns: 2025-07-17T09:05:59.5370541Z A :class:`ShardedTensor` object whose local shards are resharded. 2025-07-17T09:05:59.5370599Z 2025-07-17T09:05:59.5370659Z Examples: 2025-07-17T09:05:59.5370728Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.5370810Z >>> # We have 2 process groups, 2 ranks. 2025-07-17T09:05:59.5370929Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2025-07-17T09:05:59.5371008Z >>> tensor = torch.stack([tensor, tensor]) 2025-07-17T09:05:59.5371065Z >>> tensor 2025-07-17T09:05:59.5371155Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2025-07-17T09:05:59.5371228Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2025-07-17T09:05:59.5371308Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2025-07-17T09:05:59.5371391Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2025-07-17T09:05:59.5371698Z >>> sharding_dim = 0 2025-07-17T09:05:59.5371772Z >>> spec = ChunkShardingSpec( 2025-07-17T09:05:59.5371841Z dim=sharding_dim, 2025-07-17T09:05:59.5371903Z placements=[ 2025-07-17T09:05:59.5371975Z "rank:0/cuda:0", 2025-07-17T09:05:59.5372038Z "rank:1/cuda:1", 2025-07-17T09:05:59.5372232Z "rank:2/cuda:2", 2025-07-17T09:05:59.5372294Z "rank:3/cuda:3", 2025-07-17T09:05:59.5372357Z ], 2025-07-17T09:05:59.5372412Z ) 2025-07-17T09:05:59.5372484Z >>> current_offsets = [0] * 2 2025-07-17T09:05:59.5372561Z >>> current_offsets[0] = rank * 2 2025-07-17T09:05:59.5372640Z >>> shard_metadata = ShardMetadata( 2025-07-17T09:05:59.5372742Z shard_offsets=copy.deepcopy(current_offsets), 2025-07-17T09:05:59.5372816Z shard_sizes=tensor.size(), 2025-07-17T09:05:59.5372900Z placement=spec.placements[rank], 2025-07-17T09:05:59.5372955Z ) 2025-07-17T09:05:59.5373029Z >>> local_shards = [ 2025-07-17T09:05:59.5373090Z Shard( 2025-07-17T09:05:59.5376394Z tensor=tensor, 2025-07-17T09:05:59.5376498Z metadata=shard_metadata, 2025-07-17T09:05:59.5376566Z ) 2025-07-17T09:05:59.5376623Z ] 2025-07-17T09:05:59.5376793Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2025-07-17T09:05:59.5376863Z >>> sharding_dim = 1 2025-07-17T09:05:59.5376958Z >>> resharding_spec = ChunkShardingSpec( 2025-07-17T09:05:59.5377031Z dim=sharding_dim, 2025-07-17T09:05:59.5377098Z placements=[ 2025-07-17T09:05:59.5377169Z "rank:0/cuda:0", 2025-07-17T09:05:59.5377233Z "rank:1/cuda:1", 2025-07-17T09:05:59.5377299Z "rank:2/cuda:2", 2025-07-17T09:05:59.5377363Z "rank:3/cuda:3", 2025-07-17T09:05:59.5377424Z ], 2025-07-17T09:05:59.5377481Z ) 2025-07-17T09:05:59.5377560Z >>> st.reshard(resharding_spec) 2025-07-17T09:05:59.5377637Z >>> tensor = st.local_shards()[0].tensor 2025-07-17T09:05:59.5377701Z >>> tensor 2025-07-17T09:05:59.5377799Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2025-07-17T09:05:59.5377888Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2025-07-17T09:05:59.5377973Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2025-07-17T09:05:59.5378068Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2025-07-17T09:05:59.5378126Z 2025-07-17T09:05:59.5378288Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5378346Z 2025-07-17T09:05:59.5378416Z warnings.warn(msg) 2025-07-17T09:05:59.5378473Z 2025-07-17T09:05:59.5378629Z --- Parse Warning: 97 / 136 --- 2025-07-17T09:05:59.5379316Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=HierarchicalModelAverager in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/model_averaging/hierarchical_model_averager.py line=19. 2025-07-17T09:05:59.5379477Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5379538Z 2025-07-17T09:05:59.5379737Z Runs hierarchical model averaging (`hierarchical SGD `_). 2025-07-17T09:05:59.5379804Z 2025-07-17T09:05:59.5379990Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2025-07-17T09:05:59.5380128Z by using different periods concurrently after the warm-up stage. 2025-07-17T09:05:59.5380372Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2025-07-17T09:05:59.5380681Z that supports `post-local SGD `_, which essentially only supports 2025-07-17T09:05:59.5380922Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2025-07-17T09:05:59.5381138Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2025-07-17T09:05:59.5381420Z Similarly, the process groups within this class do not have such an intra-machine process 2025-07-17T09:05:59.5381592Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2025-07-17T09:05:59.5381649Z 2025-07-17T09:05:59.5381712Z Args: 2025-07-17T09:05:59.5381867Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2025-07-17T09:05:59.5381996Z process group size, used for initializing process groups of 2025-07-17T09:05:59.5382138Z different sizes in a hierarchy to average parameters concurrently. 2025-07-17T09:05:59.5382284Z Particularly, at each iteration, there will be at most a single 2025-07-17T09:05:59.5382427Z process group that runs averaging -- the period of such group should 2025-07-17T09:05:59.5382561Z have the largest period which the current step can be divided by. 2025-07-17T09:05:59.5382673Z For example, if the dict has three keys: 2, 4, and 8, 2025-07-17T09:05:59.5382796Z then this means totally three process groups will be created to 2025-07-17T09:05:59.5382927Z average parameters every 2, 4, and 8 iterations, respectively. 2025-07-17T09:05:59.5383044Z At the 4th iteration, only the second process group will run 2025-07-17T09:05:59.5383153Z averaging, because the first process group should be a 2025-07-17T09:05:59.5383294Z subset of the second process group, and no need to execute the first 2025-07-17T09:05:59.5383380Z process group redundantly. 2025-07-17T09:05:59.5383506Z On the other hand, the third process group can only be triggered 2025-07-17T09:05:59.5383638Z every 8 iterations, so it will not be triggered at the 4th iteration. 2025-07-17T09:05:59.5383814Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2025-07-17T09:05:59.5384071Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2025-07-17T09:05:59.5384180Z If ``None``, the default process group, which is created 2025-07-17T09:05:59.5384313Z by :func:`torch.distributed.init_process_group`, will be used. 2025-07-17T09:05:59.5384398Z (default: ``None``) 2025-07-17T09:05:59.5384450Z 2025-07-17T09:05:59.5384526Z Example:: 2025-07-17T09:05:59.5384608Z >>> # xdoctest: +SKIP('undefined rank') 2025-07-17T09:05:59.5384688Z >>> from collections import OrderedDict 2025-07-17T09:05:59.5384755Z >>> import torch 2025-07-17T09:05:59.5384845Z >>> import torch.distributed as dist 2025-07-17T09:05:59.5385009Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2025-07-17T09:05:59.5385083Z >>> PostLocalSGDState, 2025-07-17T09:05:59.5385156Z >>> post_localSGD_hook, 2025-07-17T09:05:59.5385210Z >>> ) 2025-07-17T09:05:59.5385519Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2025-07-17T09:05:59.5385590Z >>> import torch.nn as nn 2025-07-17T09:05:59.5385752Z >>> 2025-07-17T09:05:59.5385868Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2025-07-17T09:05:59.5386021Z >>> torch.cuda.set_device(rank) 2025-07-17T09:05:59.5386112Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2025-07-17T09:05:59.5386219Z >>> model = nn.parallel.DistributedDataParallel( 2025-07-17T09:05:59.5386316Z >>> module, device_ids=[rank], output_device=rank 2025-07-17T09:05:59.5386488Z >>> ) 2025-07-17T09:05:59.5386585Z >>> # Register a post-localSGD communication hook. 2025-07-17T09:05:59.5386758Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2025-07-17T09:05:59.5386837Z >>> subgroup, _ = dist.new_subgroups() 2025-07-17T09:05:59.5387021Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2025-07-17T09:05:59.5387135Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-07-17T09:05:59.5387195Z >>> 2025-07-17T09:05:59.5387361Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2025-07-17T09:05:59.5387449Z >>> # the 16 processes every 16 iterations. 2025-07-17T09:05:59.5387568Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2025-07-17T09:05:59.5387707Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2025-07-17T09:05:59.5387895Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-07-17T09:05:59.5388053Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2025-07-17T09:05:59.5388151Z >>> # After 100 steps, run model averaging at two levels. 2025-07-17T09:05:59.5388227Z >>> for step in range(0, 200): 2025-07-17T09:05:59.5388300Z >>> optimizer.zero_grad() 2025-07-17T09:05:59.5388376Z >>> loss = loss_fn(output, labels) 2025-07-17T09:05:59.5388452Z >>> loss.backward() 2025-07-17T09:05:59.5388521Z >>> optimizer.step() 2025-07-17T09:05:59.5388623Z >>> # Average parameters after ``optimizer.step()``. 2025-07-17T09:05:59.5388786Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2025-07-17T09:05:59.5388894Z >>> averager.average_parameters(model.parameters()) 2025-07-17T09:05:59.5388951Z 2025-07-17T09:05:59.5389024Z .. warning :: 2025-07-17T09:05:59.5389176Z The last group size in the dict must be the size of the provided ``process_group``, 2025-07-17T09:05:59.5389327Z which indicates model averaging at the highest level of the hierarchy. 2025-07-17T09:05:59.5389503Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2025-07-17T09:05:59.5389562Z 2025-07-17T09:05:59.5389619Z .. warning :: 2025-07-17T09:05:59.5389768Z `HierarchicalModelAverager` is experimental and subject to change. 2025-07-17T09:05:59.5389827Z 2025-07-17T09:05:59.5389984Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5390038Z 2025-07-17T09:05:59.5390107Z warnings.warn(msg) 2025-07-17T09:05:59.5390162Z 2025-07-17T09:05:59.5390293Z --- Parse Warning: 98 / 136 --- 2025-07-17T09:05:59.5390909Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PeriodicModelAverager in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/model_averaging/averagers.py line=38. 2025-07-17T09:05:59.5391079Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5391134Z 2025-07-17T09:05:59.5391253Z Averages parameters periodically after the warm-up stage. 2025-07-17T09:05:59.5391320Z 2025-07-17T09:05:59.5391476Z This can be used for running `post-local SGD `_, 2025-07-17T09:05:59.5391706Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2025-07-17T09:05:59.5391855Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2025-07-17T09:05:59.5391926Z 2025-07-17T09:05:59.5391983Z Args: 2025-07-17T09:05:59.5392094Z period (int): The number of steps per model averaging. 2025-07-17T09:05:59.5392353Z Usually the period should be greater than ``1`` to reduce the communication cost. 2025-07-17T09:05:59.5392445Z Otherwise, only DDP needs to be used. 2025-07-17T09:05:59.5392574Z warmup_steps (int): The number of warm-up steps. During this stage, 2025-07-17T09:05:59.5392656Z model averaging is skipped. 2025-07-17T09:05:59.5392780Z process_group: The process group to be used for all-reduce. 2025-07-17T09:05:59.5392885Z If ``None``, the default process group, which 2025-07-17T09:05:59.5393007Z is created by :func:`torch.distributed.init_process_group`, 2025-07-17T09:05:59.5393092Z will be used. (default: ``None``) 2025-07-17T09:05:59.5393147Z 2025-07-17T09:05:59.5393210Z Example:: 2025-07-17T09:05:59.5393265Z 2025-07-17T09:05:59.5393350Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T09:05:59.5393420Z >>> import torch 2025-07-17T09:05:59.5393499Z >>> import torch.distributed as dist 2025-07-17T09:05:59.5393688Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2025-07-17T09:05:59.5393850Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2025-07-17T09:05:59.5393924Z >>> import torch.nn as nn 2025-07-17T09:05:59.5393979Z >>> 2025-07-17T09:05:59.5394098Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2025-07-17T09:05:59.5394168Z >>> torch.cuda.set_device(rank) 2025-07-17T09:05:59.5394256Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2025-07-17T09:05:59.5394353Z >>> model = nn.parallel.DistributedDataParallel( 2025-07-17T09:05:59.5394451Z >>> module, device_ids=[rank], output_device=rank 2025-07-17T09:05:59.5394511Z >>> ) 2025-07-17T09:05:59.5394607Z >>> # Register a post-localSGD communication hook. 2025-07-17T09:05:59.5394785Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2025-07-17T09:05:59.5394904Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-07-17T09:05:59.5394961Z >>> 2025-07-17T09:05:59.5395125Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2025-07-17T09:05:59.5395222Z >>> # After 100 steps, run model averaging every 4 steps. 2025-07-17T09:05:59.5395412Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-07-17T09:05:59.5395564Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2025-07-17T09:05:59.5395649Z >>> for step in range(0, 200): 2025-07-17T09:05:59.5395720Z >>> optimizer.zero_grad() 2025-07-17T09:05:59.5395807Z >>> loss = loss_fn(output, labels) 2025-07-17T09:05:59.5395879Z >>> loss.backward() 2025-07-17T09:05:59.5395947Z >>> optimizer.step() 2025-07-17T09:05:59.5396076Z >>> # Will average model parameters globally every 4 steps. Thus, 2025-07-17T09:05:59.5396197Z >>> # inter-node communication only occurs every 4 iterations after 2025-07-17T09:05:59.5396282Z >>> # the initial ``warmup_steps`` period. 2025-07-17T09:05:59.5398852Z >>> averager.average_parameters(model.parameters()) 2025-07-17T09:05:59.5398930Z 2025-07-17T09:05:59.5399094Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5399161Z 2025-07-17T09:05:59.5399307Z warnings.warn(msg) 2025-07-17T09:05:59.5399421Z 2025-07-17T09:05:59.5399561Z --- Parse Warning: 99 / 136 --- 2025-07-17T09:05:59.5400152Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=powerSGD_hook in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py line=342. 2025-07-17T09:05:59.5400376Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5400442Z 2025-07-17T09:05:59.5400521Z Implement PowerSGD algorithm. 2025-07-17T09:05:59.5400581Z 2025-07-17T09:05:59.5400721Z This DDP communication hook implements PowerSGD gradient compression 2025-07-17T09:05:59.5400906Z algorithm described in the `paper `_. 2025-07-17T09:05:59.5401050Z Once gradient tensors are aggregated across all workers, this hook applies 2025-07-17T09:05:59.5401130Z compression as follows: 2025-07-17T09:05:59.5401185Z 2025-07-17T09:05:59.5401442Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2025-07-17T09:05:59.5401497Z 2025-07-17T09:05:59.5401745Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2025-07-17T09:05:59.5401800Z 2025-07-17T09:05:59.5402045Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2025-07-17T09:05:59.5402102Z 2025-07-17T09:05:59.5402180Z 2. Handles uncompressed tensors: 2025-07-17T09:05:59.5402235Z 2025-07-17T09:05:59.5402531Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2025-07-17T09:05:59.5402584Z 2025-07-17T09:05:59.5402786Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2025-07-17T09:05:59.5402843Z 2025-07-17T09:05:59.5402985Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2025-07-17T09:05:59.5403042Z 2025-07-17T09:05:59.5403188Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2025-07-17T09:05:59.5403376Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2025-07-17T09:05:59.5403429Z 2025-07-17T09:05:59.5403531Z 3.2. Computes each P in Ps, which is equal to MQ; 2025-07-17T09:05:59.5403583Z 2025-07-17T09:05:59.5403657Z 3.3. Allreduces Ps as a batch; 2025-07-17T09:05:59.5403712Z 2025-07-17T09:05:59.5403792Z 3.4. Orthogonalizes each P in Ps; 2025-07-17T09:05:59.5403853Z 2025-07-17T09:05:59.5403985Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2025-07-17T09:05:59.5404044Z 2025-07-17T09:05:59.5404130Z 3.6. Allreduces Qs as a batch; 2025-07-17T09:05:59.5404184Z 2025-07-17T09:05:59.5404362Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2025-07-17T09:05:59.5404419Z 2025-07-17T09:05:59.5404655Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2025-07-17T09:05:59.5404833Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2025-07-17T09:05:59.5405080Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2025-07-17T09:05:59.5405143Z 2025-07-17T09:05:59.5405204Z Args: 2025-07-17T09:05:59.5405608Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2025-07-17T09:05:59.5405826Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2025-07-17T09:05:59.5405961Z and ``min_compression_rate``. 2025-07-17T09:05:59.5406259Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2025-07-17T09:05:59.5406425Z Note that since DDP comm hook only supports single process single device mode, 2025-07-17T09:05:59.5406525Z only exactly one tensor is stored in this bucket. 2025-07-17T09:05:59.5406653Z 2025-07-17T09:05:59.5406711Z Returns: 2025-07-17T09:05:59.5406858Z Future handler of the communication, which updates the gradients in place. 2025-07-17T09:05:59.5406912Z 2025-07-17T09:05:59.5406975Z Example:: 2025-07-17T09:05:59.5407044Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.5407217Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2025-07-17T09:05:59.5407324Z start_powerSGD_iter=10, min_compression_rate=0.5) 2025-07-17T09:05:59.5407437Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2025-07-17T09:05:59.5407495Z 2025-07-17T09:05:59.5407658Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5407713Z 2025-07-17T09:05:59.5407786Z warnings.warn(msg) 2025-07-17T09:05:59.5407839Z 2025-07-17T09:05:59.5407974Z --- Parse Warning: 100 / 136 --- 2025-07-17T09:05:59.5408592Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=post_localSGD_hook in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/post_localSGD_hook.py line=72. 2025-07-17T09:05:59.5408753Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5408811Z 2025-07-17T09:05:59.5408888Z Run post-localSGD algorithm. 2025-07-17T09:05:59.5408947Z 2025-07-17T09:05:59.5409088Z This DDP communication hook is used for running post-localSGD algorithm, 2025-07-17T09:05:59.5409199Z by combining with a model averaging component (e.g., 2025-07-17T09:05:59.5409396Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2025-07-17T09:05:59.5409477Z that runs after the optimizer step. 2025-07-17T09:05:59.5409530Z 2025-07-17T09:05:59.5409592Z Args: 2025-07-17T09:05:59.5409728Z state (PostLocalSGDState): State information to run post-localSGD. 2025-07-17T09:05:59.5409899Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2025-07-17T09:05:59.5410137Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2025-07-17T09:05:59.5410294Z Note that since DDP comm hook only supports single process single device mode, 2025-07-17T09:05:59.5410393Z only exactly one tensor is stored in this bucket. 2025-07-17T09:05:59.5410462Z 2025-07-17T09:05:59.5410518Z Returns: 2025-07-17T09:05:59.5410668Z Future handler of the communication, which updates the gradients in place. 2025-07-17T09:05:59.5410724Z 2025-07-17T09:05:59.5410787Z Example:: 2025-07-17T09:05:59.5410858Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.5411013Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2025-07-17T09:05:59.5411101Z start_localSGD_iter=10) 2025-07-17T09:05:59.5411211Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2025-07-17T09:05:59.5411413Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2025-07-17T09:05:59.5411716Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2025-07-17T09:05:59.5411787Z 2025-07-17T09:05:59.5411937Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5412057Z 2025-07-17T09:05:59.5412178Z warnings.warn(msg) 2025-07-17T09:05:59.5412235Z 2025-07-17T09:05:59.5412356Z --- Parse Warning: 101 / 136 --- 2025-07-17T09:05:59.5413063Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.set_state_dict_type in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=637. 2025-07-17T09:05:59.5413218Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5413371Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2025-07-17T09:05:59.5413424Z 2025-07-17T09:05:59.5413583Z Also takes (optional) configuration for the model's and optimizer's state dict. 2025-07-17T09:05:59.5413705Z The target module does not have to be a FSDP module. If the target 2025-07-17T09:05:59.5413839Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2025-07-17T09:05:59.5413895Z 2025-07-17T09:05:59.5414023Z .. note:: This API should be called for only the top-level (root) 2025-07-17T09:05:59.5414089Z module. 2025-07-17T09:05:59.5414145Z 2025-07-17T09:05:59.5414270Z .. note:: This API enables users to transparently use the conventional 2025-07-17T09:05:59.5414393Z ``state_dict`` API to take model checkpoints in cases where the 2025-07-17T09:05:59.5414513Z root FSDP module is wrapped by another ``nn.Module``. For example, 2025-07-17T09:05:59.5414642Z the following will ensure ``state_dict`` is called on all non-FSDP 2025-07-17T09:05:59.5414786Z instances, while dispatching into `sharded_state_dict` implementation 2025-07-17T09:05:59.5414853Z for FSDP: 2025-07-17T09:05:59.5414910Z 2025-07-17T09:05:59.5414977Z Example:: 2025-07-17T09:05:59.5415035Z 2025-07-17T09:05:59.5415126Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T09:05:59.5415199Z >>> model = DDP(FSDP(...)) 2025-07-17T09:05:59.5415279Z >>> FSDP.set_state_dict_type( 2025-07-17T09:05:59.5415350Z >>> model, 2025-07-17T09:05:59.5415436Z >>> StateDictType.SHARDED_STATE_DICT, 2025-07-17T09:05:59.5415573Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2025-07-17T09:05:59.5415710Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2025-07-17T09:05:59.5415774Z >>> ) 2025-07-17T09:05:59.5415854Z >>> param_state_dict = model.state_dict() 2025-07-17T09:05:59.5415965Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2025-07-17T09:05:59.5416018Z 2025-07-17T09:05:59.5416082Z Args: 2025-07-17T09:05:59.5416169Z module (torch.nn.Module): Root module. 2025-07-17T09:05:59.5416314Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2025-07-17T09:05:59.5416453Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2025-07-17T09:05:59.5416531Z target ``state_dict_type``. 2025-07-17T09:05:59.5416678Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2025-07-17T09:05:59.5416774Z for the optimizer state dict. 2025-07-17T09:05:59.5416833Z 2025-07-17T09:05:59.5416895Z Returns: 2025-07-17T09:05:59.5417028Z A StateDictSettings that include the previous state_dict type and 2025-07-17T09:05:59.5417175Z configuration for the module. 2025-07-17T09:05:59.5417239Z 2025-07-17T09:05:59.5417392Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5417503Z 2025-07-17T09:05:59.5417568Z warnings.warn(msg) 2025-07-17T09:05:59.5417683Z 2025-07-17T09:05:59.5417799Z --- Parse Warning: 102 / 136 --- 2025-07-17T09:05:59.5418489Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.state_dict_type in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=795. 2025-07-17T09:05:59.5418645Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5418792Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2025-07-17T09:05:59.5418845Z 2025-07-17T09:05:59.5419033Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2025-07-17T09:05:59.5419121Z :meth:`set_state_dict_type` for the detail. 2025-07-17T09:05:59.5419182Z 2025-07-17T09:05:59.5419247Z Example:: 2025-07-17T09:05:59.5419302Z 2025-07-17T09:05:59.5419391Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T09:05:59.5419473Z >>> model = DDP(FSDP(...)) 2025-07-17T09:05:59.5419553Z >>> with FSDP.state_dict_type( 2025-07-17T09:05:59.5419623Z >>> model, 2025-07-17T09:05:59.5419706Z >>> StateDictType.SHARDED_STATE_DICT, 2025-07-17T09:05:59.5419770Z >>> ): 2025-07-17T09:05:59.5419849Z >>> checkpoint = model.state_dict() 2025-07-17T09:05:59.5419908Z 2025-07-17T09:05:59.5419968Z Args: 2025-07-17T09:05:59.5420048Z module (torch.nn.Module): Root module. 2025-07-17T09:05:59.5420191Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2025-07-17T09:05:59.5420331Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2025-07-17T09:05:59.5420438Z configuration for the target ``state_dict_type``. 2025-07-17T09:05:59.5420585Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2025-07-17T09:05:59.5420706Z ``state_dict`` configuration for the target ``state_dict_type``. 2025-07-17T09:05:59.5420766Z 2025-07-17T09:05:59.5420915Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5420976Z 2025-07-17T09:05:59.5421047Z warnings.warn(msg) 2025-07-17T09:05:59.5421108Z 2025-07-17T09:05:59.5421229Z --- Parse Warning: 103 / 136 --- 2025-07-17T09:05:59.5421864Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.optim_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1808. 2025-07-17T09:05:59.5422011Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5422069Z 2025-07-17T09:05:59.5422210Z Transform the state-dict of an optimizer corresponding to a sharded model. 2025-07-17T09:05:59.5422266Z 2025-07-17T09:05:59.5422386Z The given state-dict can be transformed to one of three types: 2025-07-17T09:05:59.5422564Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2025-07-17T09:05:59.5422624Z 2025-07-17T09:05:59.5422766Z For full optimizer state_dict, all states are unflattened and not sharded. 2025-07-17T09:05:59.5422907Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2025-07-17T09:05:59.5422968Z avoid OOM. 2025-07-17T09:05:59.5423026Z 2025-07-17T09:05:59.5423222Z For sharded optimizer state_dict, all states are unflattened but sharded. 2025-07-17T09:05:59.5423357Z CPU only can be specified via :meth:`state_dict_type` to further save 2025-07-17T09:05:59.5423416Z memory. 2025-07-17T09:05:59.5423526Z 2025-07-17T09:05:59.5423658Z For local state_dict, no transformation will be performed. But a state 2025-07-17T09:05:59.5423851Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2025-07-17T09:05:59.5423926Z nature (this is not supported yet). 2025-07-17T09:05:59.5423983Z 2025-07-17T09:05:59.5424045Z Example:: 2025-07-17T09:05:59.5424105Z 2025-07-17T09:05:59.5424246Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T09:05:59.5424387Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-07-17T09:05:59.5424484Z >>> from torch.distributed.fsdp import StateDictType 2025-07-17T09:05:59.5424599Z >>> from torch.distributed.fsdp import FullStateDictConfig 2025-07-17T09:05:59.5424727Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2025-07-17T09:05:59.5424795Z >>> # Save a checkpoint 2025-07-17T09:05:59.5424861Z >>> model, optim = ... 2025-07-17T09:05:59.5424934Z >>> FSDP.set_state_dict_type( 2025-07-17T09:05:59.5424996Z >>> model, 2025-07-17T09:05:59.5425076Z >>> StateDictType.FULL_STATE_DICT, 2025-07-17T09:05:59.5425162Z >>> FullStateDictConfig(rank0_only=False), 2025-07-17T09:05:59.5425254Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-07-17T09:05:59.5425388Z >>> ) 2025-07-17T09:05:59.5425466Z >>> state_dict = model.state_dict() 2025-07-17T09:05:59.5425571Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2025-07-17T09:05:59.5425661Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2025-07-17T09:05:59.5425730Z >>> # Load a checkpoint 2025-07-17T09:05:59.5425797Z >>> model, optim = ... 2025-07-17T09:05:59.5425902Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2025-07-17T09:05:59.5425971Z >>> FSDP.set_state_dict_type( 2025-07-17T09:05:59.5426034Z >>> model, 2025-07-17T09:05:59.5426113Z >>> StateDictType.FULL_STATE_DICT, 2025-07-17T09:05:59.5426194Z >>> FullStateDictConfig(rank0_only=False), 2025-07-17T09:05:59.5426283Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-07-17T09:05:59.5426341Z >>> ) 2025-07-17T09:05:59.5426422Z >>> model.load_state_dict(state_dict) 2025-07-17T09:05:59.5426515Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2025-07-17T09:05:59.5426592Z >>> model, optim, optim_state_dict 2025-07-17T09:05:59.5426647Z >>> ) 2025-07-17T09:05:59.5426734Z >>> optim.load_state_dict(optim_state_dict) 2025-07-17T09:05:59.5426789Z 2025-07-17T09:05:59.5426851Z Args: 2025-07-17T09:05:59.5426969Z model (torch.nn.Module): Root module (which may or may not be a 2025-07-17T09:05:59.5427096Z :class:`FullyShardedDataParallel` instance) whose parameters 2025-07-17T09:05:59.5427182Z were passed into the optimizer ``optim``. 2025-07-17T09:05:59.5427303Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2025-07-17T09:05:59.5427368Z parameters. 2025-07-17T09:05:59.5427499Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2025-07-17T09:05:59.5427622Z transform. If the value is None, optim.state_dict() will be used. ( 2025-07-17T09:05:59.5427693Z Default: ``None``) 2025-07-17T09:05:59.5427836Z group (dist.ProcessGroup): Model's process group across which parameters 2025-07-17T09:05:59.5427955Z are sharded or ``None`` if using the default process group. ( 2025-07-17T09:05:59.5428019Z Default: ``None``) 2025-07-17T09:05:59.5428071Z 2025-07-17T09:05:59.5428136Z Returns: 2025-07-17T09:05:59.5428321Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2025-07-17T09:05:59.5428428Z ``model``. The sharding of the optimizer state is based on 2025-07-17T09:05:59.5428493Z ``state_dict_type``. 2025-07-17T09:05:59.5428613Z 2025-07-17T09:05:59.5428761Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5428892Z 2025-07-17T09:05:59.5428956Z warnings.warn(msg) 2025-07-17T09:05:59.5429011Z 2025-07-17T09:05:59.5429129Z --- Parse Warning: 104 / 136 --- 2025-07-17T09:05:59.5429856Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.optim_state_dict_to_load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1906. 2025-07-17T09:05:59.5430009Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5430069Z 2025-07-17T09:05:59.5430275Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2025-07-17T09:05:59.5430331Z 2025-07-17T09:05:59.5430434Z Given a ``optim_state_dict`` that is transformed through 2025-07-17T09:05:59.5430562Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2025-07-17T09:05:59.5430690Z state_dict that can be loaded to ``optim`` which is the optimizer for 2025-07-17T09:05:59.5430816Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2025-07-17T09:05:59.5430874Z 2025-07-17T09:05:59.5430956Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T09:05:59.5431103Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-07-17T09:05:59.5431198Z >>> from torch.distributed.fsdp import StateDictType 2025-07-17T09:05:59.5431309Z >>> from torch.distributed.fsdp import FullStateDictConfig 2025-07-17T09:05:59.5431434Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2025-07-17T09:05:59.5431509Z >>> # Save a checkpoint 2025-07-17T09:05:59.5431573Z >>> model, optim = ... 2025-07-17T09:05:59.5431649Z >>> FSDP.set_state_dict_type( 2025-07-17T09:05:59.5431710Z >>> model, 2025-07-17T09:05:59.5431799Z >>> StateDictType.FULL_STATE_DICT, 2025-07-17T09:05:59.5431881Z >>> FullStateDictConfig(rank0_only=False), 2025-07-17T09:05:59.5431975Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-07-17T09:05:59.5432033Z >>> ) 2025-07-17T09:05:59.5432116Z >>> state_dict = model.state_dict() 2025-07-17T09:05:59.5432195Z >>> original_osd = optim.state_dict() 2025-07-17T09:05:59.5432280Z >>> optim_state_dict = FSDP.optim_state_dict( 2025-07-17T09:05:59.5432341Z >>> model, 2025-07-17T09:05:59.5432400Z >>> optim, 2025-07-17T09:05:59.5432475Z >>> optim_state_dict=original_osd 2025-07-17T09:05:59.5432532Z >>> ) 2025-07-17T09:05:59.5432621Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2025-07-17T09:05:59.5432687Z >>> # Load a checkpoint 2025-07-17T09:05:59.5432752Z >>> model, optim = ... 2025-07-17T09:05:59.5432849Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2025-07-17T09:05:59.5432930Z >>> FSDP.set_state_dict_type( 2025-07-17T09:05:59.5432986Z >>> model, 2025-07-17T09:05:59.5433059Z >>> StateDictType.FULL_STATE_DICT, 2025-07-17T09:05:59.5433138Z >>> FullStateDictConfig(rank0_only=False), 2025-07-17T09:05:59.5433228Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-07-17T09:05:59.5433283Z >>> ) 2025-07-17T09:05:59.5433360Z >>> model.load_state_dict(state_dict) 2025-07-17T09:05:59.5433458Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2025-07-17T09:05:59.5433538Z >>> model, optim, optim_state_dict 2025-07-17T09:05:59.5433594Z >>> ) 2025-07-17T09:05:59.5433741Z >>> optim.load_state_dict(optim_state_dict) 2025-07-17T09:05:59.5433803Z 2025-07-17T09:05:59.5433862Z Args: 2025-07-17T09:05:59.5433989Z model (torch.nn.Module): Root module (which may or may not be a 2025-07-17T09:05:59.5434162Z :class:`FullyShardedDataParallel` instance) whose parameters 2025-07-17T09:05:59.5434301Z were passed into the optimizer ``optim``. 2025-07-17T09:05:59.5434414Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2025-07-17T09:05:59.5434484Z parameters. 2025-07-17T09:05:59.5434616Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2025-07-17T09:05:59.5434801Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2025-07-17T09:05:59.5434923Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2025-07-17T09:05:59.5435044Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2025-07-17T09:05:59.5435164Z load_directly (bool): If this is set to True, this API will also 2025-07-17T09:05:59.5435290Z call optim.load_state_dict(result) before returning the result. 2025-07-17T09:05:59.5435421Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2025-07-17T09:05:59.5435495Z (Default: ``False``) 2025-07-17T09:05:59.5435633Z group (dist.ProcessGroup): Model's process group across which parameters 2025-07-17T09:05:59.5435748Z are sharded or ``None`` if using the default process group. ( 2025-07-17T09:05:59.5435817Z Default: ``None``) 2025-07-17T09:05:59.5435883Z 2025-07-17T09:05:59.5436034Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5436094Z 2025-07-17T09:05:59.5436168Z warnings.warn(msg) 2025-07-17T09:05:59.5436230Z 2025-07-17T09:05:59.5436360Z --- Parse Warning: 105 / 136 --- 2025-07-17T09:05:59.5436864Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MixedPrecision in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/api.py line=114. 2025-07-17T09:05:59.5437021Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5437076Z 2025-07-17T09:05:59.5437188Z This configures FSDP-native mixed precision training. 2025-07-17T09:05:59.5437242Z 2025-07-17T09:05:59.5437303Z Attributes: 2025-07-17T09:05:59.5437439Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2025-07-17T09:05:59.5437569Z parameters during forward and backward and thus the dtype for 2025-07-17T09:05:59.5437699Z forward and backward computation. Outside forward and backward, the 2025-07-17T09:05:59.5437817Z *sharded* parameters are kept in full precision (e.g. for the 2025-07-17T09:05:59.5437939Z optimizer step), and for model checkpointing, the parameters are 2025-07-17T09:05:59.5438049Z always saved in full precision. (Default: ``None``) 2025-07-17T09:05:59.5438175Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2025-07-17T09:05:59.5438307Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2025-07-17T09:05:59.5438418Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2025-07-17T09:05:59.5438544Z the ``param_dtype`` value, still running gradient reduction in low 2025-07-17T09:05:59.5438671Z precision. This is permitted to differ from ``param_dtype``, e.g. 2025-07-17T09:05:59.5438800Z to force gradient reduction to run in full precision. (Default: 2025-07-17T09:05:59.5438860Z ``None``) 2025-07-17T09:05:59.5438983Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2025-07-17T09:05:59.5439100Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2025-07-17T09:05:59.5439281Z ``buffer_dtype`` in the first forward pass and keeps them in that 2025-07-17T09:05:59.5439406Z dtype thereafter. For model checkpointing, the buffers are saved 2025-07-17T09:05:59.5439576Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2025-07-17T09:05:59.5439682Z ``None``) 2025-07-17T09:05:59.5439801Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2025-07-17T09:05:59.5439930Z gradients to full precision after the backward pass in preparation 2025-07-17T09:05:59.5440057Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2025-07-17T09:05:59.5440230Z in the dtype used for gradient reduction, which can save memory if 2025-07-17T09:05:59.5440360Z using a custom optimizer that supports running in low precision. 2025-07-17T09:05:59.5440429Z (Default: ``False``) 2025-07-17T09:05:59.5440557Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2025-07-17T09:05:59.5440688Z its forward args and kwargs to ``param_dtype``. This is to ensure 2025-07-17T09:05:59.5440815Z that parameter and input dtypes match for forward computation, as 2025-07-17T09:05:59.5440946Z required by many ops. This may need to be set to ``True`` when only 2025-07-17T09:05:59.5441079Z applying mixed precision to some but not all FSDP modules, in which 2025-07-17T09:05:59.5441207Z case a mixed-precision FSDP submodule needs to recast its inputs. 2025-07-17T09:05:59.5441271Z (Default: ``False``) 2025-07-17T09:05:59.5441404Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2025-07-17T09:05:59.5441521Z casts its forward args and kwargs to ``param_dtype``, overriding 2025-07-17T09:05:59.5441638Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2025-07-17T09:05:59.5441733Z this does not do anything. (Default: ``True``) 2025-07-17T09:05:59.5441872Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2025-07-17T09:05:59.5441989Z module classes to ignore for mixed precision when using an 2025-07-17T09:05:59.5442105Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2025-07-17T09:05:59.5442232Z applied to them separately with mixed precision disabled (meaning 2025-07-17T09:05:59.5442354Z that the final FSDP construction would deviate from the specified 2025-07-17T09:05:59.5442464Z policy). If ``auto_wrap_policy`` is not specified, then this does 2025-07-17T09:05:59.5442586Z not do anything. This API is experimental and subject to change. 2025-07-17T09:05:59.5442660Z (Default: ``(_BatchNorm,)``) 2025-07-17T09:05:59.5442716Z 2025-07-17T09:05:59.5442822Z .. note:: This API is experimental and subject to change. 2025-07-17T09:05:59.5442881Z 2025-07-17T09:05:59.5443009Z .. note:: Only floating point tensors are cast to their specified dtypes. 2025-07-17T09:05:59.5443065Z 2025-07-17T09:05:59.5443175Z .. note:: In ``summon_full_params``, parameters are forced to full 2025-07-17T09:05:59.5443255Z precision, but buffers are not. 2025-07-17T09:05:59.5443311Z 2025-07-17T09:05:59.5443437Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2025-07-17T09:05:59.5443559Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2025-07-17T09:05:59.5443693Z Disabling FSDP's mixed precision for those norm modules only means that 2025-07-17T09:05:59.5443817Z the affine parameters are kept in ``float32``. However, this incurs 2025-07-17T09:05:59.5443952Z separate all-gathers and reduce-scatters for those norm modules, which 2025-07-17T09:05:59.5444079Z may be inefficient, so if the workload permits, the user should prefer 2025-07-17T09:05:59.5444172Z to still apply mixed precision to those modules. 2025-07-17T09:05:59.5444227Z 2025-07-17T09:05:59.5444404Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2025-07-17T09:05:59.5444534Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2025-07-17T09:05:59.5444718Z modules will have FSDP applied to them separately with mixed precision 2025-07-17T09:05:59.5444882Z disabled. See the ``_module_classes_to_ignore`` argument. 2025-07-17T09:05:59.5444941Z 2025-07-17T09:05:59.5445065Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2025-07-17T09:05:59.5445191Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2025-07-17T09:05:59.5445351Z its ``cast_root_forward_inputs`` takes precedence over its 2025-07-17T09:05:59.5445463Z ``cast_forward_inputs``. For non-root FSDP instances, their 2025-07-17T09:05:59.5445597Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2025-07-17T09:05:59.5445726Z sufficient for the typical case where each FSDP instance has the same 2025-07-17T09:05:59.5445859Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2025-07-17T09:05:59.5445972Z ``param_dtype`` at the beginning of the model's forward pass. 2025-07-17T09:05:59.5446033Z 2025-07-17T09:05:59.5446157Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2025-07-17T09:05:59.5446300Z configurations, we recommend setting individual ``cast_forward_inputs`` 2025-07-17T09:05:59.5446419Z values to configure casting inputs or not before each instance's 2025-07-17T09:05:59.5446538Z forward. In such a case, since the casts happen before each FSDP 2025-07-17T09:05:59.5446665Z instance's forward, a parent FSDP instance should have its non-FSDP 2025-07-17T09:05:59.5446797Z submodules run before its FSDP submodules to avoid the activation dtype 2025-07-17T09:05:59.5446918Z being changed due to a different ``MixedPrecision`` configuration. 2025-07-17T09:05:59.5446974Z 2025-07-17T09:05:59.5447035Z Example:: 2025-07-17T09:05:59.5447090Z 2025-07-17T09:05:59.5447178Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T09:05:59.5447286Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2025-07-17T09:05:59.5447360Z >>> model[1] = FSDP( 2025-07-17T09:05:59.5447424Z >>> model[1], 2025-07-17T09:05:59.5447611Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2025-07-17T09:05:59.5447669Z >>> ) 2025-07-17T09:05:59.5447734Z >>> model = FSDP( 2025-07-17T09:05:59.5447797Z >>> model, 2025-07-17T09:05:59.5447981Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2025-07-17T09:05:59.5448039Z >>> ) 2025-07-17T09:05:59.5448097Z 2025-07-17T09:05:59.5448221Z The above shows a working example. On the other hand, if ``model[1]`` 2025-07-17T09:05:59.5448346Z were replaced with ``model[0]``, meaning that the submodule using 2025-07-17T09:05:59.5448475Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2025-07-17T09:05:59.5448606Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2025-07-17T09:05:59.5448664Z ones. 2025-07-17T09:05:59.5448720Z 2025-07-17T09:05:59.5448775Z 2025-07-17T09:05:59.5448925Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5448982Z 2025-07-17T09:05:59.5449046Z warnings.warn(msg) 2025-07-17T09:05:59.5449101Z 2025-07-17T09:05:59.5449229Z --- Parse Warning: 106 / 136 --- 2025-07-17T09:05:59.5449751Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullStateDictConfig in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/api.py line=295. 2025-07-17T09:05:59.5449972Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5450038Z 2025-07-17T09:05:59.5450163Z ``FullStateDictConfig`` is a config class meant to be used with 2025-07-17T09:05:59.5450405Z ``StateDictType.FULL_STATE_DICT``. We recommend enabling both 2025-07-17T09:05:59.5450527Z ``offload_to_cpu=True`` and ``rank0_only=True`` when saving full state 2025-07-17T09:05:59.5450662Z dicts to save GPU memory and CPU memory, respectively. This config class 2025-07-17T09:05:59.5450777Z is meant to be used via the :func:`state_dict_type` context manager as 2025-07-17T09:05:59.5450897Z follows: 2025-07-17T09:05:59.5450951Z 2025-07-17T09:05:59.5451049Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T09:05:59.5451190Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-07-17T09:05:59.5451274Z >>> fsdp = FSDP(model, auto_wrap_policy=...) 2025-07-17T09:05:59.5451397Z >>> cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True) 2025-07-17T09:05:59.5451538Z >>> with FSDP.state_dict_type(fsdp, StateDictType.FULL_STATE_DICT, cfg): 2025-07-17T09:05:59.5451612Z >>> state = fsdp.state_dict() 2025-07-17T09:05:59.5451748Z >>> # `state` will be empty on non rank 0 and contain CPU tensors on rank 0. 2025-07-17T09:05:59.5451889Z >>> # To reload checkpoint for inference, finetuning, transfer learning, etc: 2025-07-17T09:05:59.5452037Z >>> model = model_fn() # Initialize model in preparation for wrapping with FSDP 2025-07-17T09:05:59.5452108Z >>> if dist.get_rank() == 0: 2025-07-17T09:05:59.5452222Z >>> # Load checkpoint only on rank 0 to avoid memory redundancy 2025-07-17T09:05:59.5452315Z >>> state_dict = torch.load("my_checkpoint.pt") 2025-07-17T09:05:59.5452395Z >>> model.load_state_dict(state_dict) 2025-07-17T09:05:59.5452532Z >>> # All ranks initialize FSDP module as usual. `sync_module_states` argument 2025-07-17T09:05:59.5452675Z >>> # communicates loaded checkpoint states from rank 0 to rest of the world. 2025-07-17T09:05:59.5452743Z >>> fsdp = FSDP( 2025-07-17T09:05:59.5452808Z ... model, 2025-07-17T09:05:59.5452898Z ... device_id=torch.cuda.current_device(), 2025-07-17T09:05:59.5452967Z ... auto_wrap_policy=..., 2025-07-17T09:05:59.5453041Z ... sync_module_states=True, 2025-07-17T09:05:59.5453098Z ... ) 2025-07-17T09:05:59.5453226Z >>> # After this point, all ranks have FSDP model with loaded checkpoint. 2025-07-17T09:05:59.5453279Z 2025-07-17T09:05:59.5453339Z Attributes: 2025-07-17T09:05:59.5453455Z rank0_only (bool): If ``True``, then only rank 0 saves the full state 2025-07-17T09:05:59.5453575Z dict, and nonzero ranks save an empty dict. If ``False``, then all 2025-07-17T09:05:59.5453672Z ranks save the full state dict. (Default: ``False``) 2025-07-17T09:05:59.5453730Z 2025-07-17T09:05:59.5453876Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5453931Z 2025-07-17T09:05:59.5454005Z warnings.warn(msg) 2025-07-17T09:05:59.5454066Z 2025-07-17T09:05:59.5454192Z --- Parse Warning: 107 / 136 --- 2025-07-17T09:05:59.5454795Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_server_process_global_profile in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/server_process_global_profiler.py line=19. 2025-07-17T09:05:59.5454953Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5455007Z 2025-07-17T09:05:59.5455136Z It has the same API as ``torch.autograd.profiler.profile`` class, 2025-07-17T09:05:59.5455301Z except that it enables profiling on all threads running RPC server request callbacks. 2025-07-17T09:05:59.5455418Z 2025-07-17T09:05:59.5455581Z Context manager that manages autograd profiler state and holds a summary of results. 2025-07-17T09:05:59.5455716Z Under the hood it just records events of functions being executed in C++ and 2025-07-17T09:05:59.5455952Z exposes those events to Python. You can wrap any code into it and it will 2025-07-17T09:05:59.5456038Z only report runtime of PyTorch functions. 2025-07-17T09:05:59.5456198Z Note: profiler is thread local and is automatically propagated into the async tasks 2025-07-17T09:05:59.5456260Z 2025-07-17T09:05:59.5456317Z Args: 2025-07-17T09:05:59.5456541Z enabled (bool, optional): Setting this to False makes this context manager a no-op. 2025-07-17T09:05:59.5456612Z Default: ``True``. 2025-07-17T09:05:59.5456670Z 2025-07-17T09:05:59.5456843Z use_cuda (bool, optional): Enables timing of CUDA events as well using the cudaEvent API. 2025-07-17T09:05:59.5456972Z Adds approximately 4us of overhead to each tensor operation. 2025-07-17T09:05:59.5457036Z Default: ``False`` 2025-07-17T09:05:59.5457095Z 2025-07-17T09:05:59.5457226Z record_shapes (bool, optional): If shapes recording is set, information 2025-07-17T09:05:59.5457365Z about input dimensions will be collected. This allows one to see which 2025-07-17T09:05:59.5457491Z dimensions have been used under the hood and further group by them 2025-07-17T09:05:59.5457627Z using prof.key_averages(group_by_input_shape=True). Please note that 2025-07-17T09:05:59.5457762Z shape recording might skew your profiling data. It is recommended to 2025-07-17T09:05:59.5457905Z use separate runs with and without shape recording to validate the timing. 2025-07-17T09:05:59.5458037Z Most likely the skew will be negligible for bottom most events (in a case 2025-07-17T09:05:59.5458165Z of nested function calls). But for higher level functions the total 2025-07-17T09:05:59.5458288Z self cpu time might be artificially increased because of the shape 2025-07-17T09:05:59.5458348Z collection. 2025-07-17T09:05:59.5458408Z 2025-07-17T09:05:59.5458564Z profile_memory (bool, optional): Whether to report memory usage, default: ``False`` 2025-07-17T09:05:59.5458621Z 2025-07-17T09:05:59.5458684Z .. warning:: 2025-07-17T09:05:59.5458810Z Enabling memory profiling incurs additional profiler overhead 2025-07-17T09:05:59.5458864Z 2025-07-17T09:05:59.5458937Z .. warning:: 2025-07-17T09:05:59.5459111Z Due to some CUDA multiprocessing limitations (see :ref:`multiprocessing-cuda-note`), 2025-07-17T09:05:59.5459236Z one cannot use the profiler with ``use_cuda = True`` to benchmark 2025-07-17T09:05:59.5459375Z DataLoaders with ``num_workers > 0``. If you wish to benchmark data loading, 2025-07-17T09:05:59.5459479Z please use ``use_cuda = False`` or ``num_workers = 0``. 2025-07-17T09:05:59.5459535Z 2025-07-17T09:05:59.5459595Z Example: 2025-07-17T09:05:59.5459661Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.5459724Z >>> # On worker 0: 2025-07-17T09:05:59.5459787Z >>> import torch 2025-07-17T09:05:59.5459871Z >>> import torch.distributed.rpc as rpc 2025-07-17T09:05:59.5459965Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-07-17T09:05:59.5460047Z >>> x, y = torch.tensor(1), torch.tensor(2) 2025-07-17T09:05:59.5460135Z >>> outer_profile_rref = rpc.remote( 2025-07-17T09:05:59.5460239Z ... dst_worker_name, rpc._server_process_global_profile 2025-07-17T09:05:59.5460309Z ... ) 2025-07-17T09:05:59.5460395Z >>> outer_profile_rref.rpc_sync().__enter__() 2025-07-17T09:05:59.5460495Z >>> rpc.rpc_sync(dst_worker_name, torch.add, (x, y)) 2025-07-17T09:05:59.5460568Z >>> inner_profile_rref = rpc.remote( 2025-07-17T09:05:59.5460662Z ... dst_worker_name, rpc._server_process_global_profile 2025-07-17T09:05:59.5460778Z ... ) 2025-07-17T09:05:59.5460869Z >>> inner_profile_rref.rpc_sync().__enter__() 2025-07-17T09:05:59.5460957Z >>> rpc.rpc_sync(dst_worker_name, torch.sub, (x, y)) 2025-07-17T09:05:59.5461118Z >>> inner_profile_rref.rpc_sync().__exit__(None, None, None) 2025-07-17T09:05:59.5461276Z >>> outer_profile_rref.rpc_sync().__exit__(None, None, None) 2025-07-17T09:05:59.5461388Z >>> print(inner_profile_rref.rpc_sync().key_averages()) 2025-07-17T09:05:59.5461543Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-07-17T09:05:59.5461786Z Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls 2025-07-17T09:05:59.5461926Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-07-17T09:05:59.5462050Z sub 85.06% 76.275us 100.00% 89.667us 89.667us 1 2025-07-17T09:05:59.5462160Z empty 14.94% 13.392us 14.94% 13.392us 13.392us 1 2025-07-17T09:05:59.5462300Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-07-17T09:05:59.5462375Z Self CPU time total: 89.667us 2025-07-17T09:05:59.5462474Z >>> print(outer_profile_rref.rpc_sync().key_averages()) 2025-07-17T09:05:59.5462604Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-07-17T09:05:59.5462778Z Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls 2025-07-17T09:05:59.5462916Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-07-17T09:05:59.5463027Z sub 35.65% 76.275us 41.91% 89.667us 89.667us 1 2025-07-17T09:05:59.5463135Z empty 12.67% 27.101us 12.67% 27.101us 13.551us 2 2025-07-17T09:05:59.5463248Z add 51.68% 110.550us 58.09% 124.259us 124.259us 1 2025-07-17T09:05:59.5463382Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-07-17T09:05:59.5463457Z Self CPU time total: 213.926us 2025-07-17T09:05:59.5463522Z >>> rpc.shutdown() 2025-07-17T09:05:59.5463577Z 2025-07-17T09:05:59.5463640Z >>> # On worker 1: 2025-07-17T09:05:59.5463726Z >>> import torch.distributed.rpc as rpc 2025-07-17T09:05:59.5463821Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-07-17T09:05:59.5463922Z >>> # wait for worker 0 to finish work, and then shutdown. 2025-07-17T09:05:59.5463987Z >>> rpc.shutdown() 2025-07-17T09:05:59.5464043Z 2025-07-17T09:05:59.5464196Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5464252Z 2025-07-17T09:05:59.5464320Z warnings.warn(msg) 2025-07-17T09:05:59.5464372Z 2025-07-17T09:05:59.5464504Z --- Parse Warning: 108 / 136 --- 2025-07-17T09:05:59.5465090Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=TensorPipeRpcBackendOptions.set_device_map in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/options.py line=113. 2025-07-17T09:05:59.5465255Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5465378Z 2025-07-17T09:05:59.5465502Z Set device mapping between each RPC caller and callee pair. This 2025-07-17T09:05:59.5465615Z function can be called multiple times to incrementally add 2025-07-17T09:05:59.5465882Z device placement configurations. 2025-07-17T09:05:59.5465938Z 2025-07-17T09:05:59.5465998Z Args: 2025-07-17T09:05:59.5466066Z to (str): Callee name. 2025-07-17T09:05:59.5466193Z device_map (Dict of int, str, or torch.device): Device placement 2025-07-17T09:05:59.5466425Z mappings from this worker to the callee. This map must be 2025-07-17T09:05:59.5466488Z invertible. 2025-07-17T09:05:59.5466548Z 2025-07-17T09:05:59.5466609Z Example: 2025-07-17T09:05:59.5466691Z >>> # xdoctest: +SKIP("distributed") 2025-07-17T09:05:59.5466754Z >>> # both workers 2025-07-17T09:05:59.5466820Z >>> def add(x, y): 2025-07-17T09:05:59.5466974Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2025-07-17T09:05:59.5467051Z >>> return x + y, (x + y).to(2) 2025-07-17T09:05:59.5467108Z >>> 2025-07-17T09:05:59.5467176Z >>> # on worker 0 2025-07-17T09:05:59.5467265Z >>> options = TensorPipeRpcBackendOptions( 2025-07-17T09:05:59.5467347Z >>> num_worker_threads=8, 2025-07-17T09:05:59.5467426Z >>> device_maps={"worker1": {0: 1}} 2025-07-17T09:05:59.5467512Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2025-07-17T09:05:59.5467574Z >>> ) 2025-07-17T09:05:59.5467663Z >>> options.set_device_map("worker1", {1: 2}) 2025-07-17T09:05:59.5467751Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2025-07-17T09:05:59.5467805Z >>> 2025-07-17T09:05:59.5467875Z >>> rpc.init_rpc( 2025-07-17T09:05:59.5467935Z >>> "worker0", 2025-07-17T09:05:59.5467998Z >>> rank=0, 2025-07-17T09:05:59.5468062Z >>> world_size=2, 2025-07-17T09:05:59.5468151Z >>> backend=rpc.BackendType.TENSORPIPE, 2025-07-17T09:05:59.5468225Z >>> rpc_backend_options=options 2025-07-17T09:05:59.5468287Z >>> ) 2025-07-17T09:05:59.5468340Z >>> 2025-07-17T09:05:59.5468403Z >>> x = torch.ones(2) 2025-07-17T09:05:59.5468507Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2025-07-17T09:05:59.5468627Z >>> # The first argument will be moved to cuda:1 on worker1. When 2025-07-17T09:05:59.5468738Z >>> # sending the return value back, it will follow the invert of 2025-07-17T09:05:59.5468854Z >>> # the device map, and hence will be moved back to cuda:0 and 2025-07-17T09:05:59.5468922Z >>> # cuda:1 on worker0 2025-07-17T09:05:59.5469015Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2025-07-17T09:05:59.5469113Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2025-07-17T09:05:59.5469167Z 2025-07-17T09:05:59.5469327Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5469383Z 2025-07-17T09:05:59.5469456Z warnings.warn(msg) 2025-07-17T09:05:59.5469512Z 2025-07-17T09:05:59.5469649Z --- Parse Warning: 109 / 136 --- 2025-07-17T09:05:59.5470158Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=async_execution in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/functions.py line=6. 2025-07-17T09:05:59.5470321Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5470378Z 2025-07-17T09:05:59.5470525Z A decorator for a function indicating that the return value of the function 2025-07-17T09:05:59.5470653Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2025-07-17T09:05:59.5470796Z function can run asynchronously on the RPC callee. More specifically, the 2025-07-17T09:05:59.5470935Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2025-07-17T09:05:59.5471082Z function and installs subsequent processing steps as a callback to that 2025-07-17T09:05:59.5471217Z :class:`~torch.futures.Future`. The installed callback will read the value 2025-07-17T09:05:59.5471424Z from the :class:`~torch.futures.Future` when completed and send the 2025-07-17T09:05:59.5471538Z value back as the RPC response. That also means the returned 2025-07-17T09:05:59.5471678Z :class:`~torch.futures.Future` only exists on the callee side and is never 2025-07-17T09:05:59.5471916Z sent through RPC. This decorator is useful when the wrapped function's 2025-07-17T09:05:59.5472040Z (``fn``) execution needs to pause and resume due to, e.g., containing 2025-07-17T09:05:59.5472175Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2025-07-17T09:05:59.5472231Z 2025-07-17T09:05:59.5472411Z .. note:: To enable asynchronous execution, applications must pass the 2025-07-17T09:05:59.5472556Z function object returned by this decorator to RPC APIs. If RPC detected 2025-07-17T09:05:59.5472688Z attributes installed by this decorator, it knows that this function 2025-07-17T09:05:59.5472810Z returns a ``Future`` object and will handle that accordingly. 2025-07-17T09:05:59.5472939Z However, this does not mean this decorator has to be outmost one when 2025-07-17T09:05:59.5473078Z defining a function. For example, when combined with ``@staticmethod`` 2025-07-17T09:05:59.5473203Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2025-07-17T09:05:59.5473336Z inner decorator to allow the target function be recognized as a static 2025-07-17T09:05:59.5473471Z or class function. This target function can still execute asynchronously 2025-07-17T09:05:59.5473603Z because, when accessed, the static or class method preserves attributes 2025-07-17T09:05:59.5473711Z installed by ``@rpc.functions.async_execution``. 2025-07-17T09:05:59.5473767Z 2025-07-17T09:05:59.5473825Z 2025-07-17T09:05:59.5473885Z Example:: 2025-07-17T09:05:59.5474012Z The returned :class:`~torch.futures.Future` object can come from 2025-07-17T09:05:59.5474096Z :meth:`~torch.distributed.rpc.rpc_async`, 2025-07-17T09:05:59.5474237Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2025-07-17T09:05:59.5474352Z constructor. The example below shows directly using the 2025-07-17T09:05:59.5474442Z :class:`~torch.futures.Future` returned by 2025-07-17T09:05:59.5474528Z :meth:`~torch.futures.Future.then`. 2025-07-17T09:05:59.5474592Z 2025-07-17T09:05:59.5474675Z >>> from torch.distributed import rpc 2025-07-17T09:05:59.5474740Z >>> 2025-07-17T09:05:59.5474819Z >>> # omitting setup and shutdown RPC 2025-07-17T09:05:59.5474886Z >>> 2025-07-17T09:05:59.5474953Z >>> # On all workers 2025-07-17T09:05:59.5475033Z >>> @rpc.functions.async_execution 2025-07-17T09:05:59.5475121Z >>> def async_add_chained(to, x, y, z): 2025-07-17T09:05:59.5475238Z >>> # This function runs on "worker1" and returns immediately when 2025-07-17T09:05:59.5475360Z >>> # the callback is installed through the `then(cb)` API. In the 2025-07-17T09:05:59.5475476Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2025-07-17T09:05:59.5475585Z >>> # When the return value of that `rpc_async` arrives at 2025-07-17T09:05:59.5475697Z >>> # "worker1", "worker1" will run the lambda function accordingly 2025-07-17T09:05:59.5475810Z >>> # and set the value for the previously returned `Future`, which 2025-07-17T09:05:59.5475920Z >>> # will then trigger RPC to send the result back to "worker0". 2025-07-17T09:05:59.5476026Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-07-17T09:05:59.5476104Z >>> lambda fut: fut.wait() + z 2025-07-17T09:05:59.5476166Z >>> ) 2025-07-17T09:05:59.5476224Z >>> 2025-07-17T09:05:59.5476287Z >>> # On worker0 2025-07-17T09:05:59.5476354Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.5476426Z >>> ret = rpc.rpc_sync( 2025-07-17T09:05:59.5476548Z >>> "worker1", 2025-07-17T09:05:59.5476620Z >>> async_add_chained, 2025-07-17T09:05:59.5476705Z >>> args=("worker2", torch.ones(2), 1, 1) 2025-07-17T09:05:59.5476821Z >>> ) 2025-07-17T09:05:59.5476901Z >>> print(ret) # prints tensor([3., 3.]) 2025-07-17T09:05:59.5477008Z 2025-07-17T09:05:59.5477146Z When combined with TorchScript decorators, this decorator must be the 2025-07-17T09:05:59.5477210Z outmost one. 2025-07-17T09:05:59.5477272Z 2025-07-17T09:05:59.5477347Z >>> from torch import Tensor 2025-07-17T09:05:59.5477425Z >>> from torch.futures import Future 2025-07-17T09:05:59.5477561Z >>> from torch.distributed import rpc 2025-07-17T09:05:59.5477619Z >>> 2025-07-17T09:05:59.5477691Z >>> # omitting setup and shutdown RPC 2025-07-17T09:05:59.5477749Z >>> 2025-07-17T09:05:59.5477813Z >>> # On all workers 2025-07-17T09:05:59.5477887Z >>> @torch.jit.script 2025-07-17T09:05:59.5477988Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2025-07-17T09:05:59.5478060Z >>> return x + y 2025-07-17T09:05:59.5478115Z >>> 2025-07-17T09:05:59.5478192Z >>> @rpc.functions.async_execution 2025-07-17T09:05:59.5478260Z >>> @torch.jit.script 2025-07-17T09:05:59.5478375Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2025-07-17T09:05:59.5478475Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2025-07-17T09:05:59.5478533Z >>> 2025-07-17T09:05:59.5478604Z >>> # On worker0 2025-07-17T09:05:59.5478672Z >>> ret = rpc.rpc_sync( 2025-07-17T09:05:59.5478740Z >>> "worker1", 2025-07-17T09:05:59.5478803Z >>> async_add, 2025-07-17T09:05:59.5478885Z >>> args=("worker2", torch.ones(2), 1) 2025-07-17T09:05:59.5478941Z >>> ) 2025-07-17T09:05:59.5479022Z >>> print(ret) # prints tensor([2., 2.]) 2025-07-17T09:05:59.5479075Z 2025-07-17T09:05:59.5479208Z When combined with static or class method, this decorator must be the 2025-07-17T09:05:59.5479271Z inner one. 2025-07-17T09:05:59.5479326Z 2025-07-17T09:05:59.5479404Z >>> from torch.distributed import rpc 2025-07-17T09:05:59.5479463Z >>> 2025-07-17T09:05:59.5479546Z >>> # omitting setup and shutdown RPC 2025-07-17T09:05:59.5479600Z >>> 2025-07-17T09:05:59.5479669Z >>> # On all workers 2025-07-17T09:05:59.5479743Z >>> class AsyncExecutionClass: 2025-07-17T09:05:59.5479799Z >>> 2025-07-17T09:05:59.5479861Z >>> @staticmethod 2025-07-17T09:05:59.5479946Z >>> @rpc.functions.async_execution 2025-07-17T09:05:59.5480021Z >>> def static_async_add(to, x, y, z): 2025-07-17T09:05:59.5480129Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-07-17T09:05:59.5480199Z >>> lambda fut: fut.wait() + z 2025-07-17T09:05:59.5480261Z >>> ) 2025-07-17T09:05:59.5480316Z >>> 2025-07-17T09:05:59.5480379Z >>> @classmethod 2025-07-17T09:05:59.5480457Z >>> @rpc.functions.async_execution 2025-07-17T09:05:59.5480542Z >>> def class_async_add(cls, to, x, y, z): 2025-07-17T09:05:59.5480631Z >>> ret_fut = torch.futures.Future() 2025-07-17T09:05:59.5480725Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-07-17T09:05:59.5480827Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2025-07-17T09:05:59.5480885Z >>> ) 2025-07-17T09:05:59.5480958Z >>> return ret_fut 2025-07-17T09:05:59.5481012Z >>> 2025-07-17T09:05:59.5481099Z >>> @rpc.functions.async_execution 2025-07-17T09:05:59.5481181Z >>> def bound_async_add(self, to, x, y, z): 2025-07-17T09:05:59.5481291Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-07-17T09:05:59.5481360Z >>> lambda fut: fut.wait() + z 2025-07-17T09:05:59.5481485Z >>> ) 2025-07-17T09:05:59.5481544Z >>> 2025-07-17T09:05:59.5481611Z >>> # On worker0 2025-07-17T09:05:59.5481677Z >>> ret = rpc.rpc_sync( 2025-07-17T09:05:59.5481798Z >>> "worker1", 2025-07-17T09:05:59.5481940Z >>> AsyncExecutionClass.static_async_add, 2025-07-17T09:05:59.5482013Z >>> args=("worker2", torch.ones(2), 1, 2) 2025-07-17T09:05:59.5482075Z >>> ) 2025-07-17T09:05:59.5482150Z >>> print(ret) # prints tensor([4., 4.]) 2025-07-17T09:05:59.5482208Z >>> 2025-07-17T09:05:59.5482272Z >>> ret = rpc.rpc_sync( 2025-07-17T09:05:59.5482389Z >>> "worker1", 2025-07-17T09:05:59.5482474Z >>> AsyncExecutionClass.class_async_add, 2025-07-17T09:05:59.5482556Z >>> args=("worker2", torch.ones(2), 1, 2) 2025-07-17T09:05:59.5482613Z >>> ) 2025-07-17T09:05:59.5482692Z >>> print(ret) # prints tensor([4., 4.]) 2025-07-17T09:05:59.5482745Z 2025-07-17T09:05:59.5482860Z This decorator also works with RRef helpers, i.e., . 2025-07-17T09:05:59.5482949Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2025-07-17T09:05:59.5483051Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2025-07-17T09:05:59.5483141Z :meth:`torch.distributed.rpc.RRef.remote`. 2025-07-17T09:05:59.5483196Z 2025-07-17T09:05:59.5483275Z >>> from torch.distributed import rpc 2025-07-17T09:05:59.5483330Z >>> 2025-07-17T09:05:59.5483424Z >>> # reuse the AsyncExecutionClass class above 2025-07-17T09:05:59.5483520Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-07-17T09:05:59.5483662Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2025-07-17T09:05:59.5483736Z >>> print(ret) # prints tensor([4., 4.]) 2025-07-17T09:05:59.5483801Z >>> 2025-07-17T09:05:59.5483893Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-07-17T09:05:59.5484041Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2025-07-17T09:05:59.5484111Z >>> print(ret) # prints tensor([4., 4.]) 2025-07-17T09:05:59.5484174Z >>> 2025-07-17T09:05:59.5484261Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-07-17T09:05:59.5484406Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2025-07-17T09:05:59.5484480Z >>> print(ret) # prints tensor([4., 4.]) 2025-07-17T09:05:59.5484542Z 2025-07-17T09:05:59.5484694Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5484755Z 2025-07-17T09:05:59.5484820Z warnings.warn(msg) 2025-07-17T09:05:59.5484875Z 2025-07-17T09:05:59.5485012Z --- Parse Warning: 110 / 136 --- 2025-07-17T09:05:59.5485594Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=construct_and_record_rdzv_event in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/elastic/events/__init__.py line=94. 2025-07-17T09:05:59.5485753Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5485811Z 2025-07-17T09:05:59.5485944Z Initialize rendezvous event object and record its operations. 2025-07-17T09:05:59.5485997Z 2025-07-17T09:05:59.5486062Z Args: 2025-07-17T09:05:59.5486145Z run_id (str): The run id of the rendezvous. 2025-07-17T09:05:59.5486243Z message (str): The message describing the event. 2025-07-17T09:05:59.5486394Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2025-07-17T09:05:59.5486516Z name (str): Event name. (E.g. Current action being performed). 2025-07-17T09:05:59.5486591Z hostname (str): Hostname of the node. 2025-07-17T09:05:59.5486686Z pid (Optional[int]): The process id of the node. 2025-07-17T09:05:59.5486892Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2025-07-17T09:05:59.5487060Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2025-07-17T09:05:59.5487208Z rank (Optional[int]): The rank of the node, if known. 2025-07-17T09:05:59.5487321Z Returns: 2025-07-17T09:05:59.5487378Z None 2025-07-17T09:05:59.5487441Z Example: 2025-07-17T09:05:59.5487526Z >>> # See DynamicRendezvousHandler class 2025-07-17T09:05:59.5487594Z >>> def _record( 2025-07-17T09:05:59.5487654Z ... self, 2025-07-17T09:05:59.5487719Z ... message: str, 2025-07-17T09:05:59.5487892Z ... node_state: NodeState = NodeState.RUNNING, 2025-07-17T09:05:59.5487969Z ... rank: Optional[int] = None, 2025-07-17T09:05:59.5488041Z ... ) -> None: 2025-07-17T09:05:59.5488116Z ... construct_and_record_rdzv_event( 2025-07-17T09:05:59.5488231Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2025-07-17T09:05:59.5488307Z ... run_id=self._settings.run_id, 2025-07-17T09:05:59.5488379Z ... message=message, 2025-07-17T09:05:59.5488454Z ... node_state=node_state, 2025-07-17T09:05:59.5488538Z ... hostname=self._this_node.addr, 2025-07-17T09:05:59.5488614Z ... pid=self._this_node.pid, 2025-07-17T09:05:59.5488698Z ... local_id=self._this_node.local_id, 2025-07-17T09:05:59.5488764Z ... rank=rank, 2025-07-17T09:05:59.5488822Z ... ) 2025-07-17T09:05:59.5488878Z 2025-07-17T09:05:59.5489034Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5489087Z 2025-07-17T09:05:59.5489152Z warnings.warn(msg) 2025-07-17T09:05:59.5489212Z 2025-07-17T09:05:59.5489332Z --- Parse Warning: 111 / 136 --- 2025-07-17T09:05:59.5489890Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_RemoteModule.__init__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=129. 2025-07-17T09:05:59.5490042Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5490105Z 2025-07-17T09:05:59.5490242Z RemoteModule instance can only be created after RPC initialization. 2025-07-17T09:05:59.5490303Z 2025-07-17T09:05:59.5490423Z It creates a user-specified module on a specified remote node. 2025-07-17T09:05:59.5490569Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2025-07-17T09:05:59.5490643Z executed on the remote node. 2025-07-17T09:05:59.5490786Z It takes care of autograd recording to ensure the backward pass propagates 2025-07-17T09:05:59.5490882Z gradients back to the corresponding remote module. 2025-07-17T09:05:59.5491093Z It can be shared across processors using `RPC framework `__, 2025-07-17T09:05:59.5491214Z without incurring any overheads of copying the actual module, 2025-07-17T09:05:59.5491344Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2025-07-17T09:05:59.5491420Z pointing to the remote module. 2025-07-17T09:05:59.5491480Z 2025-07-17T09:05:59.5491603Z The arguments of ``forward_async`` and ``forward`` are the same as 2025-07-17T09:05:59.5491726Z the ``forward`` method of the module returned by the ``module_cls``. 2025-07-17T09:05:59.5491783Z 2025-07-17T09:05:59.5491973Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2025-07-17T09:05:59.5492029Z 2025-07-17T09:05:59.5492185Z Particularly, to create a hybrid model, typically the local modules should be 2025-07-17T09:05:59.5492401Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2025-07-17T09:05:59.5492528Z Hybrid Example: 2025-07-17T09:05:59.5492617Z >>> class HybridModel(nn.Module): 2025-07-17T09:05:59.5492694Z >>> def __init__(self) -> None: 2025-07-17T09:05:59.5492820Z >>> nn.Module.__init__(self) 2025-07-17T09:05:59.5492961Z >>> self.remote_embedding = RemoteModule(...) 2025-07-17T09:05:59.5493046Z >>> self.local_linear = nn.Linear(...) 2025-07-17T09:05:59.5493099Z 2025-07-17T09:05:59.5493222Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2025-07-17T09:05:59.5493426Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2025-07-17T09:05:59.5493557Z the generated ``RemoteModule`` will have 2 methods in signature of 2025-07-17T09:05:59.5493642Z ``def forward(input: Tensor) -> Tensor:`` and 2025-07-17T09:05:59.5493752Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2025-07-17T09:05:59.5493808Z 2025-07-17T09:05:59.5493883Z .. note:: 2025-07-17T09:05:59.5493987Z If the remote module is placed on a cuda device, 2025-07-17T09:05:59.5494142Z any input CPU tensors will be automatically moved to the same cuda device, 2025-07-17T09:05:59.5494378Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2025-07-17T09:05:59.5494441Z 2025-07-17T09:05:59.5494501Z Args: 2025-07-17T09:05:59.5494680Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-07-17T09:05:59.5494866Z The device can be a local device or a remote device specified by one of the following remote 2025-07-17T09:05:59.5494936Z formats: 2025-07-17T09:05:59.5494993Z 2025-07-17T09:05:59.5495090Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2025-07-17T09:05:59.5495197Z 2. "/" (ex: "trainer0/cuda:0"). 2025-07-17T09:05:59.5495253Z 2025-07-17T09:05:59.5495408Z In addition, the device field can be optional and the default value is "cpu". 2025-07-17T09:05:59.5495486Z module_cls (nn.Module): For example, 2025-07-17T09:05:59.5495564Z >>> class MyModule(nn.Module): 2025-07-17T09:05:59.5495635Z >>> def forward(input): 2025-07-17T09:05:59.5495713Z >>> return input + 1 2025-07-17T09:05:59.5495774Z >>> 2025-07-17T09:05:59.5495853Z >>> module_cls = MyModule 2025-07-17T09:05:59.5495978Z args (Sequence, optional): args to be passed to ``module_cls``. 2025-07-17T09:05:59.5496100Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2025-07-17T09:05:59.5496265Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2025-07-17T09:05:59.5496415Z to be created. The type object should be decorated by @torch.jit.interface. 2025-07-17T09:05:59.5496551Z If not provided, the generated RemoteModule is not torchscript-able. 2025-07-17T09:05:59.5496706Z Warning, this is an experimental API and susceptible to frequent changes. 2025-07-17T09:05:59.5496763Z 2025-07-17T09:05:59.5496826Z Returns: 2025-07-17T09:05:59.5496974Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-07-17T09:05:59.5497124Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2025-07-17T09:05:59.5497290Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-07-17T09:05:59.5497395Z on the user-provided module on the remote side. 2025-07-17T09:05:59.5497455Z 2025-07-17T09:05:59.5497519Z Example:: 2025-07-17T09:05:59.5497627Z Run the following code in two different processes: 2025-07-17T09:05:59.5497683Z 2025-07-17T09:05:59.5497766Z >>> # xdoctest: +SKIP("distributed") 2025-07-17T09:05:59.5497829Z >>> # On worker 0: 2025-07-17T09:05:59.5497902Z >>> import torch 2025-07-17T09:05:59.5498048Z >>> import torch.distributed.rpc as rpc 2025-07-17T09:05:59.5498128Z >>> from torch import nn, Tensor 2025-07-17T09:05:59.5498265Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-07-17T09:05:59.5498434Z >>> 2025-07-17T09:05:59.5498527Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-07-17T09:05:59.5498611Z >>> remote_linear_module = RemoteModule( 2025-07-17T09:05:59.5498698Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-07-17T09:05:59.5498758Z >>> ) 2025-07-17T09:05:59.5498828Z >>> input = torch.randn(128, 20) 2025-07-17T09:05:59.5498979Z >>> ret_fut = remote_linear_module.forward_async(input) 2025-07-17T09:05:59.5499053Z >>> ret = ret_fut.wait() 2025-07-17T09:05:59.5499119Z >>> rpc.shutdown() 2025-07-17T09:05:59.5499182Z 2025-07-17T09:05:59.5499241Z >>> # On worker 1: 2025-07-17T09:05:59.5499310Z >>> import torch 2025-07-17T09:05:59.5499392Z >>> import torch.distributed.rpc as rpc 2025-07-17T09:05:59.5499459Z >>> 2025-07-17T09:05:59.5499547Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-07-17T09:05:59.5499621Z >>> rpc.shutdown() 2025-07-17T09:05:59.5499676Z 2025-07-17T09:05:59.5499826Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5499885Z 2025-07-17T09:05:59.5499954Z warnings.warn(msg) 2025-07-17T09:05:59.5500013Z 2025-07-17T09:05:59.5500142Z --- Parse Warning: 112 / 136 --- 2025-07-17T09:05:59.5500730Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_RemoteModule.init_from_module_rref in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=506. 2025-07-17T09:05:59.5500894Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5500951Z 2025-07-17T09:05:59.5501142Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2025-07-17T09:05:59.5501204Z 2025-07-17T09:05:59.5501390Z This alternate initialization method can be particularly useful if we want to create multiple 2025-07-17T09:05:59.5501583Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2025-07-17T09:05:59.5501637Z 2025-07-17T09:05:59.5501809Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2025-07-17T09:05:59.5501920Z which is not supported. The recommended way is as follows: 2025-07-17T09:05:59.5501978Z 2025-07-17T09:05:59.5502057Z 1. the sender creates a RemoteModule; 2025-07-17T09:05:59.5502155Z 2. the sender sends its ``module_rref`` over RPC; 2025-07-17T09:05:59.5502348Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2025-07-17T09:05:59.5502412Z 2025-07-17T09:05:59.5502475Z Example:: 2025-07-17T09:05:59.5502575Z Run the following code in two different processes: 2025-07-17T09:05:59.5502629Z 2025-07-17T09:05:59.5502710Z >>> # xdoctest: +SKIP("distributed") 2025-07-17T09:05:59.5502774Z >>> # On worker 0: 2025-07-17T09:05:59.5502837Z >>> import torch 2025-07-17T09:05:59.5502919Z >>> import torch.distributed.rpc as rpc 2025-07-17T09:05:59.5502991Z >>> from torch import nn, Tensor 2025-07-17T09:05:59.5503127Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-07-17T09:05:59.5503182Z >>> 2025-07-17T09:05:59.5503274Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-07-17T09:05:59.5503347Z >>> remote_module = RemoteModule( 2025-07-17T09:05:59.5503435Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-07-17T09:05:59.5503492Z >>> ) 2025-07-17T09:05:59.5503549Z >>> 2025-07-17T09:05:59.5503688Z >>> remote_module1 = rpc.rpc_sync( 2025-07-17T09:05:59.5503760Z >>> "worker1/cpu", 2025-07-17T09:05:59.5503840Z >>> RemoteModule.init_from_module_rref, 2025-07-17T09:05:59.5504000Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2025-07-17T09:05:59.5504105Z >>> ) 2025-07-17T09:05:59.5504174Z >>> rpc.shutdown() 2025-07-17T09:05:59.5504229Z 2025-07-17T09:05:59.5504299Z >>> # On worker 1: 2025-07-17T09:05:59.5504362Z >>> import torch 2025-07-17T09:05:59.5504443Z >>> import torch.distributed.rpc as rpc 2025-07-17T09:05:59.5504507Z >>> 2025-07-17T09:05:59.5504652Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-07-17T09:05:59.5504720Z >>> rpc.shutdown() 2025-07-17T09:05:59.5504777Z 2025-07-17T09:05:59.5504841Z Args: 2025-07-17T09:05:59.5505018Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-07-17T09:05:59.5505202Z The device can be a local device or a remote device specified by one of the following remote 2025-07-17T09:05:59.5505265Z formats: 2025-07-17T09:05:59.5505384Z 2025-07-17T09:05:59.5505479Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2025-07-17T09:05:59.5505587Z 2. "/" (ex: "trainer0/cuda:0"). 2025-07-17T09:05:59.5505642Z 2025-07-17T09:05:59.5505800Z In addition, the device field can be optional and the default value is "cpu". 2025-07-17T09:05:59.5505948Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2025-07-17T09:05:59.5506029Z the created remote module. 2025-07-17T09:05:59.5506194Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2025-07-17T09:05:59.5506338Z to be created. The type object should be decorated by @torch.jit.interface. 2025-07-17T09:05:59.5506475Z If not provided, the generated RemoteModule is not torchscript-able. 2025-07-17T09:05:59.5506625Z Warning, this is an experimental API and susceptible to frequent changes. 2025-07-17T09:05:59.5506683Z 2025-07-17T09:05:59.5506741Z Returns: 2025-07-17T09:05:59.5506887Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-07-17T09:05:59.5507026Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2025-07-17T09:05:59.5507187Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-07-17T09:05:59.5507280Z on the user-provided module on the remote side. 2025-07-17T09:05:59.5507337Z 2025-07-17T09:05:59.5507481Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5507537Z 2025-07-17T09:05:59.5507608Z warnings.warn(msg) 2025-07-17T09:05:59.5507672Z 2025-07-17T09:05:59.5507801Z --- Parse Warning: 113 / 136 --- 2025-07-17T09:05:59.5508344Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RemoteModule in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=598. 2025-07-17T09:05:59.5508503Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5508559Z 2025-07-17T09:05:59.5508694Z A RemoteModule instance can only be created after RPC initialization. 2025-07-17T09:05:59.5508751Z 2025-07-17T09:05:59.5508875Z It creates a user-specified module on a specified remote node. 2025-07-17T09:05:59.5509023Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2025-07-17T09:05:59.5509098Z executed on the remote node. 2025-07-17T09:05:59.5509242Z It takes care of autograd recording to ensure the backward pass propagates 2025-07-17T09:05:59.5509345Z gradients back to the corresponding remote module. 2025-07-17T09:05:59.5509477Z 2025-07-17T09:05:59.5509613Z It generates two methods ``forward_async`` and ``forward`` based on the 2025-07-17T09:05:59.5509740Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2025-07-17T09:05:59.5510035Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2025-07-17T09:05:59.5510155Z and ``forward`` are the same as the ``forward`` method of the module 2025-07-17T09:05:59.5510239Z returned by the ``module_cls``. 2025-07-17T09:05:59.5510292Z 2025-07-17T09:05:59.5510411Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2025-07-17T09:05:59.5510618Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2025-07-17T09:05:59.5510766Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2025-07-17T09:05:59.5510821Z 2025-07-17T09:05:59.5510909Z | ``def forward(input: Tensor) -> Tensor:`` 2025-07-17T09:05:59.5511018Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2025-07-17T09:05:59.5511080Z 2025-07-17T09:05:59.5511138Z Args: 2025-07-17T09:05:59.5511319Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-07-17T09:05:59.5511521Z The format should be "/", where the device field can be parsed as torch.device type. 2025-07-17T09:05:59.5511615Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2025-07-17T09:05:59.5511757Z In addition, the device field can be optional and the default value is "cpu". 2025-07-17T09:05:59.5511902Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2025-07-17T09:05:59.5511955Z 2025-07-17T09:05:59.5512037Z >>> class MyModule(nn.Module): 2025-07-17T09:05:59.5512106Z >>> def forward(input): 2025-07-17T09:05:59.5512189Z >>> return input + 1 2025-07-17T09:05:59.5512248Z >>> 2025-07-17T09:05:59.5512320Z >>> module_cls = MyModule 2025-07-17T09:05:59.5512379Z 2025-07-17T09:05:59.5512504Z args (Sequence, optional): args to be passed to ``module_cls``. 2025-07-17T09:05:59.5512630Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2025-07-17T09:05:59.5512688Z 2025-07-17T09:05:59.5512751Z Returns: 2025-07-17T09:05:59.5512894Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-07-17T09:05:59.5513031Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2025-07-17T09:05:59.5513191Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-07-17T09:05:59.5513288Z on the user-provided module on the remote side. 2025-07-17T09:05:59.5513346Z 2025-07-17T09:05:59.5513414Z Example:: 2025-07-17T09:05:59.5513512Z Run the following code in two different processes: 2025-07-17T09:05:59.5513578Z 2025-07-17T09:05:59.5513656Z >>> # xdoctest: +SKIP("distributed") 2025-07-17T09:05:59.5513725Z >>> # On worker 0: 2025-07-17T09:05:59.5513788Z >>> import torch 2025-07-17T09:05:59.5513872Z >>> import torch.distributed.rpc as rpc 2025-07-17T09:05:59.5513959Z >>> from torch import nn, Tensor 2025-07-17T09:05:59.5514104Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-07-17T09:05:59.5514167Z >>> 2025-07-17T09:05:59.5514258Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-07-17T09:05:59.5514348Z >>> remote_linear_module = RemoteModule( 2025-07-17T09:05:59.5514437Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-07-17T09:05:59.5514495Z >>> ) 2025-07-17T09:05:59.5514568Z >>> input = torch.randn(128, 20) 2025-07-17T09:05:59.5514670Z >>> ret_fut = remote_linear_module.forward_async(input) 2025-07-17T09:05:59.5514741Z >>> ret = ret_fut.wait() 2025-07-17T09:05:59.5514878Z >>> rpc.shutdown() 2025-07-17T09:05:59.5514933Z 2025-07-17T09:05:59.5514998Z >>> # On worker 1: 2025-07-17T09:05:59.5515062Z >>> import torch 2025-07-17T09:05:59.5515200Z >>> import torch.distributed.rpc as rpc 2025-07-17T09:05:59.5515305Z >>> 2025-07-17T09:05:59.5515390Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-07-17T09:05:59.5515454Z >>> rpc.shutdown() 2025-07-17T09:05:59.5515508Z 2025-07-17T09:05:59.5515634Z Furthermore, a more practical example that is combined with 2025-07-17T09:05:59.5515958Z `DistributedDataParallel `__ (DDP) 2025-07-17T09:05:59.5516154Z can be found in this `tutorial `__. 2025-07-17T09:05:59.5516211Z 2025-07-17T09:05:59.5516370Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5516426Z 2025-07-17T09:05:59.5516497Z warnings.warn(msg) 2025-07-17T09:05:59.5516550Z 2025-07-17T09:05:59.5516685Z --- Parse Warning: 114 / 136 --- 2025-07-17T09:05:59.5517253Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assoc_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=245. 2025-07-17T09:05:59.5517424Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5517550Z Return a new dict with new, potentially nested, key value pair 2025-07-17T09:05:59.5517610Z 2025-07-17T09:05:59.5517676Z >>> purchase = { 2025-07-17T09:05:59.5517748Z ... "name": "Alice", 2025-07-17T09:05:59.5517860Z ... "order": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-07-17T09:05:59.5517951Z ... "credit card": "5555-1234-1234-1234", 2025-07-17T09:05:59.5518011Z ... } 2025-07-17T09:05:59.5518138Z >>> assoc_in(purchase, ["order", "costs"], [0.25, 1.00]) # doctest: +SKIP 2025-07-17T09:05:59.5518222Z {'credit card': '5555-1234-1234-1234', 2025-07-17T09:05:59.5518286Z 'name': 'Alice', 2025-07-17T09:05:59.5518399Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2025-07-17T09:05:59.5518459Z 2025-07-17T09:05:59.5518620Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5518674Z 2025-07-17T09:05:59.5518746Z warnings.warn(msg) 2025-07-17T09:05:59.5518804Z 2025-07-17T09:05:59.5518927Z --- Parse Warning: 115 / 136 --- 2025-07-17T09:05:59.5519497Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=update_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=261. 2025-07-17T09:05:59.5519670Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5519769Z Update value in a (potentially) nested dictionary 2025-07-17T09:05:59.5519828Z 2025-07-17T09:05:59.5519889Z inputs: 2025-07-17T09:05:59.5519976Z d - dictionary on which to operate 2025-07-17T09:05:59.5520106Z keys - list or tuple giving the location of the value to be changed in d 2025-07-17T09:05:59.5520192Z func - function to operate on that value 2025-07-17T09:05:59.5520251Z 2025-07-17T09:05:59.5520381Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2025-07-17T09:05:59.5520520Z original dictionary with v replaced by func(v), but does not mutate the 2025-07-17T09:05:59.5520591Z original dictionary. 2025-07-17T09:05:59.5520651Z 2025-07-17T09:05:59.5520777Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2025-07-17T09:05:59.5520974Z specified by the keys, with the innermost value set to func(default). 2025-07-17T09:05:59.5521032Z 2025-07-17T09:05:59.5521111Z >>> inc = lambda x: x + 1 2025-07-17T09:05:59.5521188Z >>> update_in({"a": 0}, ["a"], inc) 2025-07-17T09:05:59.5521304Z {'a': 1} 2025-07-17T09:05:59.5521416Z 2025-07-17T09:05:59.5521492Z >>> transaction = { 2025-07-17T09:05:59.5521559Z ... "name": "Alice", 2025-07-17T09:05:59.5521679Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-07-17T09:05:59.5521755Z ... "credit card": "5555-1234-1234-1234", 2025-07-17T09:05:59.5521818Z ... } 2025-07-17T09:05:59.5522003Z >>> update_in(transaction, ["purchase", "costs"], sum) # doctest: +SKIP 2025-07-17T09:05:59.5522086Z {'credit card': '5555-1234-1234-1234', 2025-07-17T09:05:59.5522152Z 'name': 'Alice', 2025-07-17T09:05:59.5522260Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2025-07-17T09:05:59.5522321Z 2025-07-17T09:05:59.5522401Z >>> # updating a value when k0 is not in d 2025-07-17T09:05:59.5522490Z >>> update_in({}, [1, 2, 3], str, default="bar") 2025-07-17T09:05:59.5522553Z {1: {2: {3: 'bar'}}} 2025-07-17T09:05:59.5522634Z >>> update_in({1: "foo"}, [2, 3, 4], inc, 0) 2025-07-17T09:05:59.5522699Z {1: 'foo', 2: {3: {4: 1}}} 2025-07-17T09:05:59.5522758Z 2025-07-17T09:05:59.5522908Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5522973Z 2025-07-17T09:05:59.5523046Z warnings.warn(msg) 2025-07-17T09:05:59.5523109Z 2025-07-17T09:05:59.5523234Z --- Parse Warning: 116 / 136 --- 2025-07-17T09:05:59.5523792Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=get_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=320. 2025-07-17T09:05:59.5523945Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5524061Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2025-07-17T09:05:59.5524120Z 2025-07-17T09:05:59.5524239Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2025-07-17T09:05:59.5524364Z ``no_default`` is specified, then it raises KeyError or IndexError. 2025-07-17T09:05:59.5524427Z 2025-07-17T09:05:59.5524554Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2025-07-17T09:05:59.5524642Z structures such as dictionaries and lists. 2025-07-17T09:05:59.5524710Z 2025-07-17T09:05:59.5524776Z >>> transaction = { 2025-07-17T09:05:59.5524852Z ... "name": "Alice", 2025-07-17T09:05:59.5524965Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-07-17T09:05:59.5525043Z ... "credit card": "5555-1234-1234-1234", 2025-07-17T09:05:59.5525098Z ... } 2025-07-17T09:05:59.5525196Z >>> get_in(["purchase", "items", 0], transaction) 2025-07-17T09:05:59.5525256Z 'Apple' 2025-07-17T09:05:59.5525340Z >>> get_in(["name"], transaction) 2025-07-17T09:05:59.5525400Z 'Alice' 2025-07-17T09:05:59.5525490Z >>> get_in(["purchase", "total"], transaction) 2025-07-17T09:05:59.5525587Z >>> get_in(["purchase", "items", "apple"], transaction) 2025-07-17T09:05:59.5525678Z >>> get_in(["purchase", "items", 10], transaction) 2025-07-17T09:05:59.5525767Z >>> get_in(["purchase", "total"], transaction, 0) 2025-07-17T09:05:59.5525831Z 0 2025-07-17T09:05:59.5525906Z >>> get_in(["y"], {}, no_default=True) 2025-07-17T09:05:59.5525990Z Traceback (most recent call last): 2025-07-17T09:05:59.5526050Z ... 2025-07-17T09:05:59.5526115Z KeyError: 'y' 2025-07-17T09:05:59.5526181Z 2025-07-17T09:05:59.5526240Z See Also: 2025-07-17T09:05:59.5526376Z itertoolz.get 2025-07-17T09:05:59.5526448Z operator.getitem 2025-07-17T09:05:59.5526509Z 2025-07-17T09:05:59.5526656Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5526774Z 2025-07-17T09:05:59.5526889Z warnings.warn(msg) 2025-07-17T09:05:59.5526944Z 2025-07-17T09:05:59.5527060Z --- Parse Warning: 117 / 136 --- 2025-07-17T09:05:59.5527676Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=groupby in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=373. 2025-07-17T09:05:59.5527831Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5527913Z Group a collection by a key function 2025-07-17T09:05:59.5527969Z 2025-07-17T09:05:59.5528079Z >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"] 2025-07-17T09:05:59.5528161Z >>> groupby(len, names) # doctest: +SKIP 2025-07-17T09:05:59.5528263Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2025-07-17T09:05:59.5528332Z 2025-07-17T09:05:59.5528405Z >>> iseven = lambda x: x % 2 == 0 2025-07-17T09:05:59.5528517Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2025-07-17T09:05:59.5528590Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2025-07-17T09:05:59.5528650Z 2025-07-17T09:05:59.5528744Z Non-callable keys imply grouping on a member. 2025-07-17T09:05:59.5528808Z 2025-07-17T09:05:59.5528873Z >>> groupby( 2025-07-17T09:05:59.5528938Z ... "gender", 2025-07-17T09:05:59.5528995Z ... [ 2025-07-17T09:05:59.5529076Z ... {"name": "Alice", "gender": "F"}, 2025-07-17T09:05:59.5529157Z ... {"name": "Bob", "gender": "M"}, 2025-07-17T09:05:59.5529242Z ... {"name": "Charlie", "gender": "M"}, 2025-07-17T09:05:59.5529302Z ... ], 2025-07-17T09:05:59.5529376Z ... ) # doctest:+SKIP 2025-07-17T09:05:59.5529452Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2025-07-17T09:05:59.5529526Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2025-07-17T09:05:59.5529607Z {'gender': 'M', 'name': 'Charlie'}]} 2025-07-17T09:05:59.5529662Z 2025-07-17T09:05:59.5529760Z Not to be confused with ``itertools.groupby`` 2025-07-17T09:05:59.5529822Z 2025-07-17T09:05:59.5529880Z See Also: 2025-07-17T09:05:59.5529935Z countby 2025-07-17T09:05:59.5529992Z 2025-07-17T09:05:59.5530141Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5530206Z 2025-07-17T09:05:59.5534478Z warnings.warn(msg) 2025-07-17T09:05:59.5534549Z 2025-07-17T09:05:59.5534717Z --- Parse Warning: 118 / 136 --- 2025-07-17T09:05:59.5535255Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Conv1d in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py line=354. 2025-07-17T09:05:59.5535424Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5535563Z Applies a 1D convolution over a quantized input signal composed of 2025-07-17T09:05:59.5535645Z several quantized input planes. 2025-07-17T09:05:59.5535701Z 2025-07-17T09:05:59.5535844Z For details on input arguments, parameters, and implementation see 2025-07-17T09:05:59.5535922Z :class:`~torch.nn.Conv1d`. 2025-07-17T09:05:59.5535983Z 2025-07-17T09:05:59.5536049Z .. note:: 2025-07-17T09:05:59.5536186Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2025-07-17T09:05:59.5536244Z 2025-07-17T09:05:59.5536311Z .. note:: 2025-07-17T09:05:59.5536425Z Only `torch.quint8` is supported for the input data type. 2025-07-17T09:05:59.5536586Z 2025-07-17T09:05:59.5536644Z 2025-07-17T09:05:59.5536715Z Attributes: 2025-07-17T09:05:59.5536849Z weight (Tensor): packed tensor derived from the learnable weight 2025-07-17T09:05:59.5537009Z parameter. 2025-07-17T09:05:59.5537162Z scale (Tensor): scalar for the output scale 2025-07-17T09:05:59.5537271Z zero_point (Tensor): scalar for the output zero point 2025-07-17T09:05:59.5537329Z 2025-07-17T09:05:59.5537427Z See :class:`~torch.nn.Conv1d` for other attributes. 2025-07-17T09:05:59.5537507Z 2025-07-17T09:05:59.5537635Z Examples:: 2025-07-17T09:05:59.5537697Z 2025-07-17T09:05:59.5537791Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2025-07-17T09:05:59.5537880Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2025-07-17T09:05:59.5537956Z >>> input = torch.randn(20, 16, 100) 2025-07-17T09:05:59.5538040Z >>> # quantize input to quint8 2025-07-17T09:05:59.5538112Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.5538255Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2025-07-17T09:05:59.5538346Z ... dtype=torch.quint8) 2025-07-17T09:05:59.5538421Z >>> output = m(q_input) 2025-07-17T09:05:59.5538474Z 2025-07-17T09:05:59.5538535Z 2025-07-17T09:05:59.5538688Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5538752Z 2025-07-17T09:05:59.5538824Z warnings.warn(msg) 2025-07-17T09:05:59.5538882Z 2025-07-17T09:05:59.5539017Z --- Parse Warning: 119 / 136 --- 2025-07-17T09:05:59.5539506Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=LSTM in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/rnn.py line=12. 2025-07-17T09:05:59.5539671Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5539764Z A quantized long short-term memory (LSTM). 2025-07-17T09:05:59.5539826Z 2025-07-17T09:05:59.5539994Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2025-07-17T09:05:59.5540051Z 2025-07-17T09:05:59.5540114Z Attributes: 2025-07-17T09:05:59.5540197Z layers : instances of the `_LSTMLayer` 2025-07-17T09:05:59.5540252Z 2025-07-17T09:05:59.5540313Z .. note:: 2025-07-17T09:05:59.5540440Z To access the weights and biases, you need to access them per layer. 2025-07-17T09:05:59.5540561Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2025-07-17T09:05:59.5540618Z 2025-07-17T09:05:59.5540684Z Examples:: 2025-07-17T09:05:59.5540751Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.5540832Z >>> custom_module_config = { 2025-07-17T09:05:59.5540928Z ... 'float_to_observed_custom_module_class': { 2025-07-17T09:05:59.5541012Z ... nn.LSTM: nn.quantizable.LSTM, 2025-07-17T09:05:59.5541078Z ... }, 2025-07-17T09:05:59.5541170Z ... 'observed_to_quantized_custom_module_class': { 2025-07-17T09:05:59.5541268Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2025-07-17T09:05:59.5541326Z ... } 2025-07-17T09:05:59.5541386Z ... } 2025-07-17T09:05:59.5541526Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2025-07-17T09:05:59.5541657Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2025-07-17T09:05:59.5541716Z 2025-07-17T09:05:59.5541874Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5541927Z 2025-07-17T09:05:59.5541993Z warnings.warn(msg) 2025-07-17T09:05:59.5542046Z 2025-07-17T09:05:59.5542233Z --- Parse Warning: 120 / 136 --- 2025-07-17T09:05:59.5542802Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReport in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report.py line=24. 2025-07-17T09:05:59.5543073Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5543129Z 2025-07-17T09:05:59.5543315Z The ModelReport class aims to provide users an easy way to diagnose issues that they run into 2025-07-17T09:05:59.5543549Z with their models. The class works with all traceable GraphModules to help diagnose issues, 2025-07-17T09:05:59.5543732Z though the requirements on the type of model more-so depends on the specific report the user 2025-07-17T09:05:59.5543904Z is trying to generate. With respect to the reports, the ModelReport class is initialized with 2025-07-17T09:05:59.5544073Z a set of Detector classes, each of which generate reports on quantization configuration 2025-07-17T09:05:59.5544141Z issues a use might have. 2025-07-17T09:05:59.5544201Z 2025-07-17T09:05:59.5544292Z Currently supports generating reports on: 2025-07-17T09:05:59.5544438Z - Suggestions for per-channel vs. per-tensor quantization (nn.Module) 2025-07-17T09:05:59.5544603Z - Suggestions for dynamic vs static quantization for linear layers (Graph Modules) 2025-07-17T09:05:59.5544777Z - Suggestions for input-weight equalization for linear and conv layers (Graph Modules) 2025-07-17T09:05:59.5544914Z - Suggestions for outlier detection for all layers (Graph Modules) 2025-07-17T09:05:59.5544967Z 2025-07-17T09:05:59.5545202Z The ModelReport class has the primary functionality of inserting observers (primarily the ModelReportObserver) 2025-07-17T09:05:59.5545483Z where needed for each detector to gather the information it needs, and then after calibration, the ModelReport 2025-07-17T09:05:59.5545703Z class compiles the report generated by each Detector class into a single report to return to the user. It also 2025-07-17T09:05:59.5545834Z has the capability to remove all the observers it inserted as well. 2025-07-17T09:05:59.5545895Z 2025-07-17T09:05:59.5546068Z * :attr:`_model` The model we wish to generate the report for. Must be a traceable GraphModule 2025-07-17T09:05:59.5546132Z 2025-07-17T09:05:59.5546351Z * :attr:`_desired_report_detectors` The set of Detectors representing desired reports from the ModelReport class 2025-07-17T09:05:59.5546541Z Make sure that these are all unique types of detectors [do not have more than 1 of the same class] 2025-07-17T09:05:59.5546598Z 2025-07-17T09:05:59.5546765Z * :attr:`_desired_detector_names` The set of detector names of the _desired_report_detectors. 2025-07-17T09:05:59.5546904Z This set is generated by calling the get_detector_name() of each detector 2025-07-17T09:05:59.5546970Z 2025-07-17T09:05:59.5547165Z * :attr:`_detector_name_to_observer_fqns` The mapping from each detector to fqns of observers of interest 2025-07-17T09:05:59.5547350Z The purpose of this is to keep track of what observers were inserted for each detector, so that they 2025-07-17T09:05:59.5547432Z can be removed at the end if desired 2025-07-17T09:05:59.5547493Z 2025-07-17T09:05:59.5547675Z * :attr:`_prepared_flag` A boolean flag that keeps track of whether we have prepared the model or not 2025-07-17T09:05:59.5547823Z This is to ensure we only insert observers once with the ModelReport instance 2025-07-17T09:05:59.5547878Z 2025-07-17T09:05:59.5548034Z * :attr:`_removed_observers` A boolean to track if we have removed observers already 2025-07-17T09:05:59.5548207Z The purpose is to ensure we don't attempt to remove observers twice with the same ModelReport 2025-07-17T09:05:59.5548474Z instance. This also allows the functionality where we can generate the report multiple times 2025-07-17T09:05:59.5548577Z as long as we haven't removed the observers yet. 2025-07-17T09:05:59.5548639Z 2025-07-17T09:05:59.5548701Z Note: 2025-07-17T09:05:59.5548946Z This class was initially designed to work with the Fx Graph Mode workflow in mind. However, 2025-07-17T09:05:59.5549195Z full functionality is available as long as there is a traceable GraphModule that is being used. 2025-07-17T09:05:59.5549368Z One method to get a traceable GraphModule without going through the Fx workflow is to use 2025-07-17T09:05:59.5549452Z the QuantizationTracer class. 2025-07-17T09:05:59.5549507Z 2025-07-17T09:05:59.5549646Z General Flow for Fx workflow: 2025-07-17T09:05:59.5549877Z 1.) Initialize ModelReport object with reports of interest by passing in initialized detector objects and model 2025-07-17T09:05:59.5549958Z 2.) Prepare your model with prepare_fx 2025-07-17T09:05:59.5550107Z 3.) Call model_report.prepare_detailed_calibration to add relevant observers 2025-07-17T09:05:59.5550181Z 4.) Calibrate your model with data 2025-07-17T09:05:59.5550390Z 5.) Call model_report.generate_report on your model to generate report and optionally remove added observers 2025-07-17T09:05:59.5550456Z Optional 2025-07-17T09:05:59.5550617Z 6.) Call model_report.generate_visualizer to get a ModelReportVisualizer instance 2025-07-17T09:05:59.5550767Z 7.) To help in parsing report information and debugging, view report info as a: 2025-07-17T09:05:59.5550832Z - Table 2025-07-17T09:05:59.5550898Z - Histogram 2025-07-17T09:05:59.5550963Z - Line plot 2025-07-17T09:05:59.5551151Z 8.) Call model_report.generate_qconfigs to generate the qconfigs based on the report suggestions 2025-07-17T09:05:59.5551210Z 2025-07-17T09:05:59.5551291Z Example (with QuantizationTracer): 2025-07-17T09:05:59.5551362Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.5551448Z >>> # get the necessary qconfig 2025-07-17T09:05:59.5551528Z >>> config = PrepareCustomConfig() 2025-07-17T09:05:59.5551627Z >>> skipped_module_names, skipped_module_classes = ( 2025-07-17T09:05:59.5551739Z ... get_skipped_module_name_and_classes(config, False) 2025-07-17T09:05:59.5551796Z ... ) 2025-07-17T09:05:59.5551850Z 2025-07-17T09:05:59.5551935Z >>> # initialize our model and get GraphModule 2025-07-17T09:05:59.5552011Z >>> model = SomeModel() 2025-07-17T09:05:59.5552166Z >>> tracer = QuantizationTracer(skipped_module_names, skipped_module_classes) 2025-07-17T09:05:59.5552283Z >>> graph_module = GraphModule(model, tracer.trace(model)) 2025-07-17T09:05:59.5552336Z 2025-07-17T09:05:59.5552436Z >>> # get our set of detectors and ModelReport instance 2025-07-17T09:05:59.5552501Z >>> detector_set = set( 2025-07-17T09:05:59.5552560Z ... [ 2025-07-17T09:05:59.5552653Z ... DynamicStaticDetector(tolerance=0.5), 2025-07-17T09:05:59.5552772Z ... InputWeightEqualizationDetector(ratio_threshold=0.7), 2025-07-17T09:05:59.5552831Z ... ] 2025-07-17T09:05:59.5552890Z ... ) 2025-07-17T09:05:59.5553017Z >>> tracer_reporter = ModelReport(graph_module, tracer_detector_set) 2025-07-17T09:05:59.5553071Z 2025-07-17T09:05:59.5553176Z >>> # now we insert the observers and calibrate the model 2025-07-17T09:05:59.5553323Z >>> tracer_model_with_observers = tracer_reporter.prepare_detailed_calibration() 2025-07-17T09:05:59.5553421Z >>> for i in range(num_callibration_batches): 2025-07-17T09:05:59.5553515Z >>> example_input = get_callibration_input() 2025-07-17T09:05:59.5553608Z >>> tracer_model_with_observers(example_input) 2025-07-17T09:05:59.5553663Z 2025-07-17T09:05:59.5553819Z >>> # finally we generate the reports and optionally remove the observers we inserted 2025-07-17T09:05:59.5553975Z >>> reports = tracer_reporter.generate_model_report( 2025-07-17T09:05:59.5554060Z ... remove_inserted_observers=True 2025-07-17T09:05:59.5554114Z ... ) 2025-07-17T09:05:59.5554169Z 2025-07-17T09:05:59.5554360Z >>> # Optional: we can generate the qconfig mapping based on the suggestions 2025-07-17T09:05:59.5554518Z >>> qconfigs = model_report.generate_qconfig_mapping() 2025-07-17T09:05:59.5554571Z 2025-07-17T09:05:59.5554716Z >>> # Optional: we can generate the equalization mapping based on the suggestions 2025-07-17T09:05:59.5554829Z >>> qconfigs = model_report.generate_equalization_mapping() 2025-07-17T09:05:59.5554885Z 2025-07-17T09:05:59.5555223Z >>> # Optional: we get a ModelReportVisualizer instance to do any visualizations desired 2025-07-17T09:05:59.5555358Z >>> model_report_visualizer = tracer_reporter.generate_visualizer() 2025-07-17T09:05:59.5555415Z 2025-07-17T09:05:59.5555472Z 2025-07-17T09:05:59.5555633Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5555688Z 2025-07-17T09:05:59.5555757Z warnings.warn(msg) 2025-07-17T09:05:59.5555809Z 2025-07-17T09:05:59.5555949Z --- Parse Warning: 121 / 136 --- 2025-07-17T09:05:59.5556636Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_filtered_tables in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=301. 2025-07-17T09:05:59.5556804Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5556857Z 2025-07-17T09:05:59.5557016Z Takes in optional filter values and generates two tables with desired information. 2025-07-17T09:05:59.5557074Z 2025-07-17T09:05:59.5557211Z The generated tables are presented in both a list-of-lists format 2025-07-17T09:05:59.5557268Z 2025-07-17T09:05:59.5557407Z The reason for the two tables are that they handle different things: 2025-07-17T09:05:59.5557513Z 1.) the first table handles all tensor level information 2025-07-17T09:05:59.5557659Z 2.) the second table handles and displays all channel based information 2025-07-17T09:05:59.5557713Z 2025-07-17T09:05:59.5557897Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2025-07-17T09:05:59.5558088Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2025-07-17T09:05:59.5558293Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2025-07-17T09:05:59.5558347Z 2025-07-17T09:05:59.5558414Z Tensor table columns: 2025-07-17T09:05:59.5558530Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2025-07-17T09:05:59.5558638Z ---- --------- --------- --------- --------- --------- 2025-07-17T09:05:59.5558704Z 2025-07-17T09:05:59.5558780Z Per-Channel table columns: 2025-07-17T09:05:59.5558914Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2025-07-17T09:05:59.5559023Z ---- --------- ------- --------- --------- --------- --------- 2025-07-17T09:05:59.5559086Z 2025-07-17T09:05:59.5559146Z Args: 2025-07-17T09:05:59.5559309Z feature_filter (str, optional): Filters the features presented to only those that 2025-07-17T09:05:59.5559386Z contain this filter substring 2025-07-17T09:05:59.5559491Z Default = "", results in all the features being printed 2025-07-17T09:05:59.5559646Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-07-17T09:05:59.5559801Z Default = "", results in all the modules in the reports to be visible in the table 2025-07-17T09:05:59.5559854Z 2025-07-17T09:05:59.5559933Z Returns a dictionary with two keys: 2025-07-17T09:05:59.5560126Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2025-07-17T09:05:59.5560221Z "tensor_level_info", "channel_level_info" 2025-07-17T09:05:59.5560350Z Each key maps to a tuple with: 2025-07-17T09:05:59.5560490Z A list of the headers of each table 2025-07-17T09:05:59.5560603Z A list of lists containing the table information row by row 2025-07-17T09:05:59.5560719Z The 0th index row will contain the headers of the columns 2025-07-17T09:05:59.5560808Z The rest of the rows will contain data 2025-07-17T09:05:59.5560866Z 2025-07-17T09:05:59.5560982Z Example Use: 2025-07-17T09:05:59.5561070Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T09:05:59.5561176Z >>> mod_report_visualizer.generate_filtered_tables( 2025-07-17T09:05:59.5561296Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-07-17T09:05:59.5561470Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2025-07-17T09:05:59.5561523Z 2025-07-17T09:05:59.5561678Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5561737Z 2025-07-17T09:05:59.5561814Z warnings.warn(msg) 2025-07-17T09:05:59.5561871Z 2025-07-17T09:05:59.5562006Z --- Parse Warning: 122 / 136 --- 2025-07-17T09:05:59.5562699Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_table_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=399. 2025-07-17T09:05:59.5562857Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5562910Z 2025-07-17T09:05:59.5563082Z Takes in optional filter values and prints out formatted tables of the information. 2025-07-17T09:05:59.5563138Z 2025-07-17T09:05:59.5563343Z The reason for the two tables printed out instead of one large one are that they handle different things: 2025-07-17T09:05:59.5563451Z 1.) the first table handles all tensor level information 2025-07-17T09:05:59.5563586Z 2.) the second table handles and displays all channel based information 2025-07-17T09:05:59.5563640Z 2025-07-17T09:05:59.5563823Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2025-07-17T09:05:59.5564007Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2025-07-17T09:05:59.5564216Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2025-07-17T09:05:59.5564268Z 2025-07-17T09:05:59.5564337Z Tensor table columns: 2025-07-17T09:05:59.5564452Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2025-07-17T09:05:59.5564549Z ---- --------- --------- --------- --------- --------- 2025-07-17T09:05:59.5564602Z 2025-07-17T09:05:59.5564670Z Per-Channel table columns: 2025-07-17T09:05:59.5564730Z 2025-07-17T09:05:59.5564855Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2025-07-17T09:05:59.5564971Z ---- --------- ------- --------- --------- --------- --------- 2025-07-17T09:05:59.5565026Z 2025-07-17T09:05:59.5565085Z Args: 2025-07-17T09:05:59.5565240Z feature_filter (str, optional): Filters the features presented to only those that 2025-07-17T09:05:59.5565320Z contain this filter substring 2025-07-17T09:05:59.5565423Z Default = "", results in all the features being printed 2025-07-17T09:05:59.5565579Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-07-17T09:05:59.5565793Z Default = "", results in all the modules in the reports to be visible in the table 2025-07-17T09:05:59.5565850Z 2025-07-17T09:05:59.5565916Z Example Use: 2025-07-17T09:05:59.5566005Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T09:05:59.5566171Z >>> mod_report_visualizer.generate_table_visualization( 2025-07-17T09:05:59.5566340Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-07-17T09:05:59.5566395Z ... ) 2025-07-17T09:05:59.5566520Z >>> # prints out neatly formatted table with per_channel_min info 2025-07-17T09:05:59.5566606Z >>> # for all modules in block 1 of the model 2025-07-17T09:05:59.5566659Z 2025-07-17T09:05:59.5566867Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5566925Z 2025-07-17T09:05:59.5567000Z warnings.warn(msg) 2025-07-17T09:05:59.5567058Z 2025-07-17T09:05:59.5567177Z --- Parse Warning: 123 / 136 --- 2025-07-17T09:05:59.5567862Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_plot_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=564. 2025-07-17T09:05:59.5568022Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5568094Z 2025-07-17T09:05:59.5568237Z Takes in a feature and optional module_filter and plots of the desired data. 2025-07-17T09:05:59.5568290Z 2025-07-17T09:05:59.5568458Z For per channel features, it averages the value across the channels and plots a point 2025-07-17T09:05:59.5568613Z per module. The reason for this is that for models with hundreds of channels, it can 2025-07-17T09:05:59.5568774Z be hard to differentiate one channel line from another, and so the point of generating 2025-07-17T09:05:59.5568930Z a single average point per module is to give a sense of general trends that encourage 2025-07-17T09:05:59.5569003Z further deep dives. 2025-07-17T09:05:59.5569062Z 2025-07-17T09:05:59.5569119Z Note: 2025-07-17T09:05:59.5569284Z Only features in the report that have tensor value data are plottable by this class 2025-07-17T09:05:59.5569394Z When the tensor information is plotted, it will plot: 2025-07-17T09:05:59.5569497Z idx as the x val, feature value as the y_val 2025-07-17T09:05:59.5569603Z When the channel information is plotted, it will plot: 2025-07-17T09:05:59.5569768Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2025-07-17T09:05:59.5569905Z The reason for this is that we want to be able to compare values across the 2025-07-17T09:05:59.5570049Z channels for same layer, and it will be hard if values are staggered by idx 2025-07-17T09:05:59.5570154Z This means each module is represented by only 1 x value 2025-07-17T09:05:59.5570211Z Args: 2025-07-17T09:05:59.5570346Z feature_filter (str): Filters the features presented to only those that 2025-07-17T09:05:59.5570426Z contain this filter substring 2025-07-17T09:05:59.5570574Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-07-17T09:05:59.5570724Z Default = "", results in all the modules in the reports to be visible in the table 2025-07-17T09:05:59.5570778Z 2025-07-17T09:05:59.5570838Z Example Use: 2025-07-17T09:05:59.5570922Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T09:05:59.5571023Z >>> mod_report_visualizer.generate_plot_visualization( 2025-07-17T09:05:59.5571140Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-07-17T09:05:59.5571198Z ... ) 2025-07-17T09:05:59.5571316Z >>> # outputs line plot of per_channel_min information for all 2025-07-17T09:05:59.5571428Z >>> # modules in block1 of model each channel gets it's own line, 2025-07-17T09:05:59.5571603Z >>> # and it's plotted across the in-order modules on the x-axis 2025-07-17T09:05:59.5571659Z 2025-07-17T09:05:59.5571810Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5571967Z 2025-07-17T09:05:59.5572038Z warnings.warn(msg) 2025-07-17T09:05:59.5572090Z 2025-07-17T09:05:59.5572208Z --- Parse Warning: 124 / 136 --- 2025-07-17T09:05:59.5572959Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_histogram_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=643. 2025-07-17T09:05:59.5573120Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5573175Z 2025-07-17T09:05:59.5573341Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2025-07-17T09:05:59.5573395Z 2025-07-17T09:05:59.5573455Z Note: 2025-07-17T09:05:59.5573612Z Only features in the report that have tensor value data can be viewed as a histogram 2025-07-17T09:05:59.5573769Z If you want to plot a histogram from all the channel values of a specific feature for 2025-07-17T09:05:59.5573914Z a specific model, make sure to specify both the model and the feature properly 2025-07-17T09:05:59.5574062Z in the filters and you should be able to see a distribution of the channel data 2025-07-17T09:05:59.5574117Z 2025-07-17T09:05:59.5574173Z Args: 2025-07-17T09:05:59.5574329Z feature_filter (str, optional): Filters the features presented to only those that 2025-07-17T09:05:59.5574399Z contain this filter substring 2025-07-17T09:05:59.5574503Z Default = "", results in all the features being printed 2025-07-17T09:05:59.5574652Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-07-17T09:05:59.5574796Z Default = "", results in all the modules in the reports to be visible in the table 2025-07-17T09:05:59.5574935Z num_bins (int, optional): The number of bins to create the histogram with 2025-07-17T09:05:59.5575051Z Default = 10, the values will be split into 10 equal sized bins 2025-07-17T09:05:59.5575103Z 2025-07-17T09:05:59.5575172Z Example Use: 2025-07-17T09:05:59.5575238Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.5575416Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2025-07-17T09:05:59.5575536Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-07-17T09:05:59.5575600Z ... ) 2025-07-17T09:05:59.5575756Z # outputs histogram of per_channel_min information for all modules in block1 of model 2025-07-17T09:05:59.5575909Z information is gathered across all channels for all modules in block 1 for the 2025-07-17T09:05:59.5576038Z per_channel_min and is displayed in a histogram of equally sized bins 2025-07-17T09:05:59.5576095Z 2025-07-17T09:05:59.5576250Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5576305Z 2025-07-17T09:05:59.5576369Z warnings.warn(msg) 2025-07-17T09:05:59.5576425Z 2025-07-17T09:05:59.5576542Z --- Parse Warning: 125 / 136 --- 2025-07-17T09:05:59.5577109Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DTypeConfig in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/backend_config/backend_config.py line=181. 2025-07-17T09:05:59.5577259Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5577314Z 2025-07-17T09:05:59.5577461Z Config object that specifies the supported data types passed as arguments to 2025-07-17T09:05:59.5577672Z quantize ops in the reference model spec, for input and output activations, 2025-07-17T09:05:59.5577735Z weights, and biases. 2025-07-17T09:05:59.5577790Z 2025-07-17T09:05:59.5577941Z For example, consider the following reference model: 2025-07-17T09:05:59.5578045Z 2025-07-17T09:05:59.5578148Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2025-07-17T09:05:59.5578202Z 2025-07-17T09:05:59.5578330Z The pattern in the square brackets refers to the reference pattern of 2025-07-17T09:05:59.5578473Z statically quantized linear. Setting the input dtype as `torch.quint8` 2025-07-17T09:05:59.5578682Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2025-07-17T09:05:59.5578821Z to the first quantize op (quant1). Similarly, setting the output dtype as 2025-07-17T09:05:59.5578951Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2025-07-17T09:05:59.5579027Z the second quantize op (quant2). 2025-07-17T09:05:59.5579081Z 2025-07-17T09:05:59.5579215Z Note that the dtype here does not refer to the interface dtypes of the 2025-07-17T09:05:59.5579338Z op. For example, the "input dtype" here is not the dtype of the input 2025-07-17T09:05:59.5579468Z tensor passed to the quantized linear op. Though it can still be the 2025-07-17T09:05:59.5579597Z same as the interface dtype, this is not always the case, e.g. the 2025-07-17T09:05:59.5579724Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2025-07-17T09:05:59.5579856Z specified in the DTypeConfig would still be quint8. The semantics of 2025-07-17T09:05:59.5579976Z dtypes here are the same as the semantics of the dtypes specified in 2025-07-17T09:05:59.5580043Z the observers. 2025-07-17T09:05:59.5580096Z 2025-07-17T09:05:59.5580225Z These dtypes are matched against the ones specified in the user's 2025-07-17T09:05:59.5580349Z QConfig. If there is a match, and the QConfig satisfies the constraints 2025-07-17T09:05:59.5580482Z specified in the DTypeConfig (if any), then we will quantize the given 2025-07-17T09:05:59.5580611Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2025-07-17T09:05:59.5580689Z the pattern will not be quantized. 2025-07-17T09:05:59.5580748Z 2025-07-17T09:05:59.5580828Z Example usage:: 2025-07-17T09:05:59.5580880Z 2025-07-17T09:05:59.5580954Z >>> # xdoctest: +SKIP(failing) 2025-07-17T09:05:59.5581026Z >>> dtype_config1 = DTypeConfig( 2025-07-17T09:05:59.5581098Z ... input_dtype=torch.quint8, 2025-07-17T09:05:59.5581168Z ... output_dtype=torch.quint8, 2025-07-17T09:05:59.5581239Z ... weight_dtype=torch.qint8, 2025-07-17T09:05:59.5581310Z ... bias_dtype=torch.float) 2025-07-17T09:05:59.5581363Z 2025-07-17T09:05:59.5581433Z >>> dtype_config2 = DTypeConfig( 2025-07-17T09:05:59.5581518Z ... input_dtype=DTypeWithConstraints( 2025-07-17T09:05:59.5581587Z ... dtype=torch.quint8, 2025-07-17T09:05:59.5581660Z ... quant_min_lower_bound=0, 2025-07-17T09:05:59.5581737Z ... quant_max_upper_bound=255, 2025-07-17T09:05:59.5581795Z ... ), 2025-07-17T09:05:59.5581879Z ... output_dtype=DTypeWithConstraints( 2025-07-17T09:05:59.5581944Z ... dtype=torch.quint8, 2025-07-17T09:05:59.5582017Z ... quant_min_lower_bound=0, 2025-07-17T09:05:59.5582092Z ... quant_max_upper_bound=255, 2025-07-17T09:05:59.5582154Z ... ), 2025-07-17T09:05:59.5582235Z ... weight_dtype=DTypeWithConstraints( 2025-07-17T09:05:59.5582308Z ... dtype=torch.qint8, 2025-07-17T09:05:59.5582386Z ... quant_min_lower_bound=-128, 2025-07-17T09:05:59.5582455Z ... quant_max_upper_bound=127, 2025-07-17T09:05:59.5582511Z ... ), 2025-07-17T09:05:59.5582577Z ... bias_dtype=torch.float) 2025-07-17T09:05:59.5582694Z 2025-07-17T09:05:59.5582768Z >>> dtype_config1.input_dtype 2025-07-17T09:05:59.5582831Z torch.quint8 2025-07-17T09:05:59.5582883Z 2025-07-17T09:05:59.5583011Z >>> dtype_config2.input_dtype 2025-07-17T09:05:59.5583071Z torch.quint8 2025-07-17T09:05:59.5583177Z 2025-07-17T09:05:59.5583268Z >>> dtype_config2.input_dtype_with_constraints 2025-07-17T09:05:59.5583582Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2025-07-17T09:05:59.5583634Z 2025-07-17T09:05:59.5583842Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5583896Z 2025-07-17T09:05:59.5583961Z warnings.warn(msg) 2025-07-17T09:05:59.5584016Z 2025-07-17T09:05:59.5584135Z --- Parse Warning: 126 / 136 --- 2025-07-17T09:05:59.5584785Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ActivationSparsifier in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py line=16. 2025-07-17T09:05:59.5584943Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5584998Z 2025-07-17T09:05:59.5585156Z The Activation sparsifier class aims to sparsify/prune activations in a neural 2025-07-17T09:05:59.5585373Z network. The idea is to attach the sparsifier to a layer (or layers) and it 2025-07-17T09:05:59.5585519Z zeroes out the activations based on the mask_fn (or sparsification function) 2025-07-17T09:05:59.5585585Z input by the user. 2025-07-17T09:05:59.5585720Z The mask_fn is applied once all the inputs are aggregated and reduced i.e. 2025-07-17T09:05:59.5585826Z mask = mask_fn(reduce_fn(aggregate_fn(activations))) 2025-07-17T09:05:59.5585882Z 2025-07-17T09:05:59.5585948Z Note:: 2025-07-17T09:05:59.5586135Z The sparsification mask is computed on the input **before it goes through the attached layer**. 2025-07-17T09:05:59.5586189Z 2025-07-17T09:05:59.5586245Z Args: 2025-07-17T09:05:59.5586311Z model (nn.Module): 2025-07-17T09:05:59.5586442Z The model whose layers will be sparsified. The layers that needs to be 2025-07-17T09:05:59.5586587Z sparsified should be added separately using the register_layer() function 2025-07-17T09:05:59.5586662Z aggregate_fn (Optional, Callable): 2025-07-17T09:05:59.5586815Z default aggregate_fn that is used if not specified while registering the layer. 2025-07-17T09:05:59.5586923Z specifies how inputs should be aggregated over time. 2025-07-17T09:05:59.5587095Z The aggregate_fn should usually take 2 torch tensors and return the aggregated tensor. 2025-07-17T09:05:59.5587152Z Example 2025-07-17T09:05:59.5587262Z def add_agg_fn(tensor1, tensor2): return tensor1 + tensor2 2025-07-17T09:05:59.5587338Z reduce_fn (Optional, Callable): 2025-07-17T09:05:59.5587489Z default reduce_fn that is used if not specified while registering the layer. 2025-07-17T09:05:59.5587645Z reduce_fn will be called on the aggregated tensor i.e. the tensor obtained after 2025-07-17T09:05:59.5587725Z calling agg_fn() on all inputs. 2025-07-17T09:05:59.5587788Z Example 2025-07-17T09:05:59.5587903Z def mean_reduce_fn(agg_tensor): return agg_tensor.mean(dim=0) 2025-07-17T09:05:59.5587986Z mask_fn (Optional, Callable): 2025-07-17T09:05:59.5588164Z default mask_fn that is used to create the sparsification mask using the tensor obtained after 2025-07-17T09:05:59.5588318Z calling the reduce_fn(). This is used by default if a custom one is passed in the 2025-07-17T09:05:59.5588389Z register_layer(). 2025-07-17T09:05:59.5588670Z Note that the mask_fn() definition should contain the sparse arguments that is passed in sparse_config 2025-07-17T09:05:59.5588740Z arguments. 2025-07-17T09:05:59.5588880Z features (Optional, list): 2025-07-17T09:05:59.5589041Z default selected features to sparsify. 2025-07-17T09:05:59.5589210Z If this is non-empty, then the mask_fn will be applied for each feature of the input. 2025-07-17T09:05:59.5589275Z For example, 2025-07-17T09:05:59.5589488Z mask = [mask_fn(reduce_fn(aggregated_fn(input[feature])) for feature in features] 2025-07-17T09:05:59.5589564Z feature_dim (Optional, int): 2025-07-17T09:05:59.5589727Z default dimension of input features. Again, features along this dim will be chosen 2025-07-17T09:05:59.5589797Z for sparsification. 2025-07-17T09:05:59.5589867Z sparse_config (Dict): 2025-07-17T09:05:59.5590001Z Default configuration for the mask_fn. This config will be passed 2025-07-17T09:05:59.5590075Z with the mask_fn() 2025-07-17T09:05:59.5590135Z 2025-07-17T09:05:59.5590201Z Example: 2025-07-17T09:05:59.5590269Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.5590338Z >>> model = SomeModel() 2025-07-17T09:05:59.5590488Z >>> act_sparsifier = ActivationSparsifier(...) # init activation sparsifier 2025-07-17T09:05:59.5590556Z >>> # Initialize aggregate_fn 2025-07-17T09:05:59.5590628Z >>> def agg_fn(x, y): 2025-07-17T09:05:59.5590694Z >>> return x + y 2025-07-17T09:05:59.5590756Z >>> 2025-07-17T09:05:59.5590826Z >>> # Initialize reduce_fn 2025-07-17T09:05:59.5590891Z >>> def reduce_fn(x): 2025-07-17T09:05:59.5590963Z >>> return torch.mean(x, dim=0) 2025-07-17T09:05:59.5591019Z >>> 2025-07-17T09:05:59.5591085Z >>> # Initialize mask_fn 2025-07-17T09:05:59.5591153Z >>> def mask_fn(data): 2025-07-17T09:05:59.5591245Z >>> return torch.eye(data.shape).to(data.device) 2025-07-17T09:05:59.5591306Z >>> 2025-07-17T09:05:59.5591365Z >>> 2025-07-17T09:05:59.5591446Z >>> act_sparsifier.register_layer( 2025-07-17T09:05:59.5591520Z ... model.some_layer, 2025-07-17T09:05:59.5591592Z ... aggregate_fn=agg_fn, 2025-07-17T09:05:59.5591665Z ... reduce_fn=reduce_fn, 2025-07-17T09:05:59.5591729Z ... mask_fn=mask_fn, 2025-07-17T09:05:59.5591789Z ... ) 2025-07-17T09:05:59.5591842Z >>> 2025-07-17T09:05:59.5591913Z >>> # start training process 2025-07-17T09:05:59.5591974Z >>> for _ in [...]: 2025-07-17T09:05:59.5592042Z >>> # epoch starts 2025-07-17T09:05:59.5592152Z >>> # model.forward(), compute_loss() and model.backwards() 2025-07-17T09:05:59.5592217Z >>> # epoch ends 2025-07-17T09:05:59.5592288Z >>> act_sparsifier.step() 2025-07-17T09:05:59.5592358Z >>> # end training process 2025-07-17T09:05:59.5592425Z >>> sparsifier.squash_mask() 2025-07-17T09:05:59.5592487Z 2025-07-17T09:05:59.5592641Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5592694Z 2025-07-17T09:05:59.5592762Z warnings.warn(msg) 2025-07-17T09:05:59.5592817Z 2025-07-17T09:05:59.5592957Z --- Parse Warning: 127 / 136 --- 2025-07-17T09:05:59.5593614Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BaseDataScheduler.get_schedule_param in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py line=91. 2025-07-17T09:05:59.5593770Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5593825Z 2025-07-17T09:05:59.5594015Z Abstract method that needs to be implemented by the child class. 2025-07-17T09:05:59.5594166Z The expected return type should is a dictionary of name to schedule_param value 2025-07-17T09:05:59.5594328Z The returned values will be updated in sparsifier when the scheduler step() function 2025-07-17T09:05:59.5594490Z is called. 2025-07-17T09:05:59.5594549Z 2025-07-17T09:05:59.5594607Z Example: 2025-07-17T09:05:59.5594692Z >>> def get_schedule_param(self): 2025-07-17T09:05:59.5594758Z ... new_param = {} 2025-07-17T09:05:59.5594859Z ... for name in self.sparsifier.data_groups.keys(): 2025-07-17T09:05:59.5594980Z ... new_param[name] = ( 2025-07-17T09:05:59.5595111Z ... self.sparsifier.data_groups[name][self.schedule_param] * 0.5 2025-07-17T09:05:59.5595169Z ... ) 2025-07-17T09:05:59.5595240Z ... return new_param 2025-07-17T09:05:59.5595293Z 2025-07-17T09:05:59.5595496Z When the step() function is called, the value in self.sparsifier.data_groups[name][self.schedule_param] 2025-07-17T09:05:59.5595559Z would be halved 2025-07-17T09:05:59.5595614Z 2025-07-17T09:05:59.5595764Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5595822Z 2025-07-17T09:05:59.5595890Z warnings.warn(msg) 2025-07-17T09:05:59.5595942Z 2025-07-17T09:05:59.5596068Z --- Parse Warning: 128 / 136 --- 2025-07-17T09:05:59.5596639Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BaseSparsifier.squash_mask in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py line=229. 2025-07-17T09:05:59.5596790Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5596899Z Squashes the sparse masks into the appropriate tensors. 2025-07-17T09:05:59.5596958Z 2025-07-17T09:05:59.5597078Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2025-07-17T09:05:59.5597194Z the module will have a `sparse_params` dict attached to it. 2025-07-17T09:05:59.5597251Z 2025-07-17T09:05:59.5597312Z Args: 2025-07-17T09:05:59.5597431Z params_to_keep: List of keys to save in the module or a dict 2025-07-17T09:05:59.5597530Z representing the modules and keys that will have 2025-07-17T09:05:59.5597615Z sparsity parameters saved 2025-07-17T09:05:59.5597744Z params_to_keep_per_layer: Dict to specify the params that should be 2025-07-17T09:05:59.5597842Z saved for specific layers. The keys in the dict 2025-07-17T09:05:59.5597940Z should be the module fqn, while the values should 2025-07-17T09:05:59.5598037Z be a list of strings with the names of the variables 2025-07-17T09:05:59.5598125Z to save in the `sparse_params` 2025-07-17T09:05:59.5598178Z 2025-07-17T09:05:59.5598239Z Examples: 2025-07-17T09:05:59.5598325Z >>> # xdoctest: +SKIP("locals are undefined") 2025-07-17T09:05:59.5598404Z >>> # Don't save any sparse params 2025-07-17T09:05:59.5598488Z >>> sparsifier.squash_mask() 2025-07-17T09:05:59.5598572Z >>> hasattr(model.submodule1, "sparse_params") 2025-07-17T09:05:59.5598633Z False 2025-07-17T09:05:59.5598688Z 2025-07-17T09:05:59.5598763Z >>> # Keep sparse params per layer 2025-07-17T09:05:59.5598840Z >>> sparsifier.squash_mask( 2025-07-17T09:05:59.5598919Z ... params_to_keep_per_layer={ 2025-07-17T09:05:59.5599008Z ... "submodule1.linear1": ("foo", "bar"), 2025-07-17T09:05:59.5599093Z ... "submodule2.linear42": ("baz",), 2025-07-17T09:05:59.5599215Z ... } 2025-07-17T09:05:59.5599274Z ... ) 2025-07-17T09:05:59.5599371Z >>> print(model.submodule1.linear1.sparse_params) 2025-07-17T09:05:59.5599489Z {'foo': 42, 'bar': 24} 2025-07-17T09:05:59.5599663Z >>> print(model.submodule2.linear42.sparse_params) 2025-07-17T09:05:59.5599727Z {'baz': 0.1} 2025-07-17T09:05:59.5599788Z 2025-07-17T09:05:59.5599874Z >>> # Keep sparse params for all layers 2025-07-17T09:05:59.5599989Z >>> sparsifier.squash_mask(params_to_keep=("foo", "bar")) 2025-07-17T09:05:59.5600131Z >>> print(model.submodule1.linear1.sparse_params) 2025-07-17T09:05:59.5600201Z {'foo': 42, 'bar': 24} 2025-07-17T09:05:59.5600295Z >>> print(model.submodule2.linear42.sparse_params) 2025-07-17T09:05:59.5600362Z {'foo': 42, 'bar': 24} 2025-07-17T09:05:59.5600415Z 2025-07-17T09:05:59.5600538Z >>> # Keep some sparse params for all layers, and specific ones for 2025-07-17T09:05:59.5600604Z >>> # some other layers 2025-07-17T09:05:59.5600679Z >>> sparsifier.squash_mask( 2025-07-17T09:05:59.5600759Z ... params_to_keep=("foo", "bar"), 2025-07-17T09:05:59.5600878Z ... params_to_keep_per_layer={"submodule2.linear42": ("baz",)}, 2025-07-17T09:05:59.5600934Z ... ) 2025-07-17T09:05:59.5601026Z >>> print(model.submodule1.linear1.sparse_params) 2025-07-17T09:05:59.5601089Z {'foo': 42, 'bar': 24} 2025-07-17T09:05:59.5601183Z >>> print(model.submodule2.linear42.sparse_params) 2025-07-17T09:05:59.5601255Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2025-07-17T09:05:59.5601313Z 2025-07-17T09:05:59.5601459Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5601515Z 2025-07-17T09:05:59.5601582Z warnings.warn(msg) 2025-07-17T09:05:59.5601636Z 2025-07-17T09:05:59.5601762Z --- Parse Warning: 129 / 136 --- 2025-07-17T09:05:59.5602242Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=AveragedModel in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py line=120. 2025-07-17T09:05:59.5602399Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5602613Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2025-07-17T09:05:59.5602671Z 2025-07-17T09:05:59.5602812Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2025-07-17T09:05:59.5602943Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2025-07-17T09:05:59.5603072Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2025-07-17T09:05:59.5603137Z (UAI 2018). 2025-07-17T09:05:59.5603196Z 2025-07-17T09:05:59.5603333Z Exponential Moving Average is a variation of `Polyak averaging`_, 2025-07-17T09:05:59.5603478Z but using exponential weights instead of equal weights across iterations. 2025-07-17T09:05:59.5603536Z 2025-07-17T09:05:59.5603677Z AveragedModel class creates a copy of the provided module :attr:`model` 2025-07-17T09:05:59.5603808Z on the device :attr:`device` and allows to compute running averages of the 2025-07-17T09:05:59.5603882Z parameters of the :attr:`model`. 2025-07-17T09:05:59.5603936Z 2025-07-17T09:05:59.5603992Z Args: 2025-07-17T09:05:59.5604093Z model (torch.nn.Module): model to use with SWA/EMA 2025-07-17T09:05:59.5604233Z device (torch.device, optional): if provided, the averaged model will be 2025-07-17T09:05:59.5604310Z stored on the :attr:`device` 2025-07-17T09:05:59.5604503Z avg_fn (function, optional): the averaging function used to update 2025-07-17T09:05:59.5604629Z parameters; the function must take in the current value of the 2025-07-17T09:05:59.5604766Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2025-07-17T09:05:59.5604982Z parameter, and the number of models already averaged; if None, 2025-07-17T09:05:59.5605092Z an equally weighted average is used (default: None) 2025-07-17T09:05:59.5605222Z multi_avg_fn (function, optional): the averaging function used to update 2025-07-17T09:05:59.5605436Z parameters inplace; the function must take in the current values of the 2025-07-17T09:05:59.5605598Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2025-07-17T09:05:59.5605738Z parameters as a list, and the number of models already averaged; if None, 2025-07-17T09:05:59.5605837Z an equally weighted average is used (default: None) 2025-07-17T09:05:59.5605971Z use_buffers (bool): if ``True``, it will compute running averages for 2025-07-17T09:05:59.5606108Z both the parameters and the buffers of the model. (default: ``False``) 2025-07-17T09:05:59.5606166Z 2025-07-17T09:05:59.5606227Z Example: 2025-07-17T09:05:59.5606316Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T09:05:59.5606401Z >>> loader, optimizer, model, loss_fn = ... 2025-07-17T09:05:59.5606509Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2025-07-17T09:05:59.5606643Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2025-07-17T09:05:59.5606721Z >>> T_max=300) 2025-07-17T09:05:59.5606785Z >>> swa_start = 160 2025-07-17T09:05:59.5606878Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2025-07-17T09:05:59.5606945Z >>> for i in range(300): 2025-07-17T09:05:59.5607023Z >>> for input, target in loader: 2025-07-17T09:05:59.5607121Z >>> optimizer.zero_grad() 2025-07-17T09:05:59.5607213Z >>> loss_fn(model(input), target).backward() 2025-07-17T09:05:59.5607292Z >>> optimizer.step() 2025-07-17T09:05:59.5607365Z >>> if i > swa_start: 2025-07-17T09:05:59.5607450Z >>> swa_model.update_parameters(model) 2025-07-17T09:05:59.5607524Z >>> swa_scheduler.step() 2025-07-17T09:05:59.5607590Z >>> else: 2025-07-17T09:05:59.5607660Z >>> scheduler.step() 2025-07-17T09:05:59.5607724Z >>> 2025-07-17T09:05:59.5607820Z >>> # Update bn statistics for the swa_model at the end 2025-07-17T09:05:59.5607922Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2025-07-17T09:05:59.5607977Z 2025-07-17T09:05:59.5608156Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2025-07-17T09:05:59.5608280Z If no averaging function is provided, the default is to compute 2025-07-17T09:05:59.5608382Z equally-weighted average of the weights (SWA). 2025-07-17T09:05:59.5608436Z 2025-07-17T09:05:59.5608496Z Example: 2025-07-17T09:05:59.5608576Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T09:05:59.5608707Z >>> # Compute exponential moving averages of the weights and buffers 2025-07-17T09:05:59.5608808Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2025-07-17T09:05:59.5608945Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2025-07-17T09:05:59.5608998Z 2025-07-17T09:05:59.5609067Z .. note:: 2025-07-17T09:05:59.5609201Z When using SWA/EMA with models containing Batch Normalization you may 2025-07-17T09:05:59.5609327Z need to update the activation statistics for Batch Normalization. 2025-07-17T09:05:59.5609529Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2025-07-17T09:05:59.5609665Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2025-07-17T09:05:59.5609871Z statistics in a post-training step by passing data through the model. The 2025-07-17T09:05:59.5610065Z second does it during the parameter update phase by averaging all buffers. 2025-07-17T09:05:59.5610209Z Empirical evidence has shown that updating the statistics in normalization 2025-07-17T09:05:59.5610395Z layers increases accuracy, but you may wish to empirically test which 2025-07-17T09:05:59.5610498Z approach yields the best results in your problem. 2025-07-17T09:05:59.5610554Z 2025-07-17T09:05:59.5610614Z .. note:: 2025-07-17T09:05:59.5610766Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2025-07-17T09:05:59.5610824Z 2025-07-17T09:05:59.5610885Z .. note:: 2025-07-17T09:05:59.5611005Z When :meth:`update_parameters` is called for the first time (i.e. 2025-07-17T09:05:59.5611116Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2025-07-17T09:05:59.5611241Z to the parameters of :class:`AveragedModel`. For every subsequent 2025-07-17T09:05:59.5611352Z call of :meth:`update_parameters` the function `avg_fn` is used 2025-07-17T09:05:59.5611429Z to update the parameters. 2025-07-17T09:05:59.5611482Z 2025-07-17T09:05:59.5611616Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2025-07-17T09:05:59.5611702Z https://arxiv.org/abs/1803.05407 2025-07-17T09:05:59.5611845Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2025-07-17T09:05:59.5611906Z Average: 2025-07-17T09:05:59.5611984Z https://arxiv.org/abs/1806.05594 2025-07-17T09:05:59.5612105Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2025-07-17T09:05:59.5612179Z https://arxiv.org/abs/1904.11943 2025-07-17T09:05:59.5612320Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2025-07-17T09:05:59.5612392Z Generalizes Well: 2025-07-17T09:05:59.5612469Z https://arxiv.org/abs/2001.02312 2025-07-17T09:05:59.5612532Z .. _Polyak averaging: 2025-07-17T09:05:59.5612641Z https://paperswithcode.com/method/polyak-averaging 2025-07-17T09:05:59.5612708Z 2025-07-17T09:05:59.5612864Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5612920Z 2025-07-17T09:05:59.5612994Z warnings.warn(msg) 2025-07-17T09:05:59.5613046Z 2025-07-17T09:05:59.5613181Z --- Parse Warning: 130 / 136 --- 2025-07-17T09:05:59.5613647Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SWALR in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py line=375. 2025-07-17T09:05:59.5613812Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5613944Z Anneals the learning rate in each parameter group to a fixed value. 2025-07-17T09:05:59.5614007Z 2025-07-17T09:05:59.5614146Z This learning rate scheduler is meant to be used with Stochastic Weight 2025-07-17T09:05:59.5614277Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2025-07-17T09:05:59.5614333Z 2025-07-17T09:05:59.5614395Z Args: 2025-07-17T09:05:59.5614505Z optimizer (torch.optim.Optimizer): wrapped optimizer 2025-07-17T09:05:59.5614630Z swa_lrs (float or list): the learning rate value for all param groups 2025-07-17T09:05:59.5614734Z together or separately for each group. 2025-07-17T09:05:59.5614867Z annealing_epochs (int): number of epochs in the annealing phase 2025-07-17T09:05:59.5614996Z (default: 10) 2025-07-17T09:05:59.5615127Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2025-07-17T09:05:59.5615313Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2025-07-17T09:05:59.5615430Z (default: "cos") 2025-07-17T09:05:59.5615554Z last_epoch (int): the index of the last epoch (default: -1) 2025-07-17T09:05:59.5615606Z 2025-07-17T09:05:59.5615720Z The :class:`SWALR` scheduler can be used together with other 2025-07-17T09:05:59.5615904Z schedulers to switch to a constant learning rate late in the training 2025-07-17T09:05:59.5615982Z as in the example below. 2025-07-17T09:05:59.5616036Z 2025-07-17T09:05:59.5616102Z Example: 2025-07-17T09:05:59.5616190Z >>> # xdoctest: +SKIP("Undefined variables") 2025-07-17T09:05:59.5616270Z >>> loader, optimizer, model = ... 2025-07-17T09:05:59.5616346Z >>> lr_lambda = lambda epoch: 0.9 2025-07-17T09:05:59.5616483Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2025-07-17T09:05:59.5616556Z >>> lr_lambda=lr_lambda) 2025-07-17T09:05:59.5616670Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2025-07-17T09:05:59.5616782Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2025-07-17T09:05:59.5616851Z >>> swa_start = 160 2025-07-17T09:05:59.5616917Z >>> for i in range(300): 2025-07-17T09:05:59.5616992Z >>> for input, target in loader: 2025-07-17T09:05:59.5617075Z >>> optimizer.zero_grad() 2025-07-17T09:05:59.5617160Z >>> loss_fn(model(input), target).backward() 2025-07-17T09:05:59.5617233Z >>> optimizer.step() 2025-07-17T09:05:59.5617304Z >>> if i > swa_start: 2025-07-17T09:05:59.5617382Z >>> swa_scheduler.step() 2025-07-17T09:05:59.5617445Z >>> else: 2025-07-17T09:05:59.5617520Z >>> scheduler.step() 2025-07-17T09:05:59.5617572Z 2025-07-17T09:05:59.5617709Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2025-07-17T09:05:59.5617790Z https://arxiv.org/abs/1803.05407 2025-07-17T09:05:59.5617848Z 2025-07-17T09:05:59.5617992Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5618051Z 2025-07-17T09:05:59.5618114Z warnings.warn(msg) 2025-07-17T09:05:59.5618166Z 2025-07-17T09:05:59.5618297Z --- Parse Warning: 131 / 136 --- 2025-07-17T09:05:59.5618810Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Optimizer.load_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/optimizer.py line=867. 2025-07-17T09:05:59.5618979Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5619049Z Load the optimizer state. 2025-07-17T09:05:59.5619111Z 2025-07-17T09:05:59.5619168Z Args: 2025-07-17T09:05:59.5619296Z state_dict (dict): optimizer state. Should be an object returned 2025-07-17T09:05:59.5619379Z from a call to :meth:`state_dict`. 2025-07-17T09:05:59.5619440Z 2025-07-17T09:05:59.5619508Z .. warning:: 2025-07-17T09:05:59.5619720Z Make sure this method is called after initializing :class:`torch.optim.lr_scheduler.LRScheduler`, 2025-07-17T09:05:59.5619854Z as calling it beforehand will overwrite the loaded learning rates. 2025-07-17T09:05:59.5619912Z 2025-07-17T09:05:59.5619970Z .. note:: 2025-07-17T09:05:59.5620142Z The names of the parameters (if they exist under the "param_names" key of each param group 2025-07-17T09:05:59.5620322Z in :meth:`state_dict`) will not affect the loading process. 2025-07-17T09:05:59.5620520Z To use the parameters' names for custom cases (such as when the parameters in the loaded state dict 2025-07-17T09:05:59.5620702Z differ from those initialized in the optimizer), 2025-07-17T09:05:59.5620938Z a custom ``register_load_state_dict_pre_hook`` should be implemented to adapt the loaded dict 2025-07-17T09:05:59.5621008Z accordingly. 2025-07-17T09:05:59.5621183Z If ``param_names`` exist in loaded state dict ``param_groups`` they will be saved and override 2025-07-17T09:05:59.5621417Z the current names, if present, in the optimizer state. If they do not exist in loaded state dict, 2025-07-17T09:05:59.5621532Z the optimizer ``param_names`` will remain unchanged. 2025-07-17T09:05:59.5621584Z 2025-07-17T09:05:59.5621648Z Example: 2025-07-17T09:05:59.5621720Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.5621803Z >>> model = torch.nn.Linear(10, 10) 2025-07-17T09:05:59.5621909Z >>> optim = torch.optim.SGD(model.parameters(), lr=3e-4) 2025-07-17T09:05:59.5622010Z >>> scheduler1 = torch.optim.lr_scheduler.LinearLR( 2025-07-17T09:05:59.5622084Z ... optim, 2025-07-17T09:05:59.5622153Z ... start_factor=0.1, 2025-07-17T09:05:59.5622221Z ... end_factor=1, 2025-07-17T09:05:59.5622288Z ... total_iters=20, 2025-07-17T09:05:59.5622350Z ... ) 2025-07-17T09:05:59.5622470Z >>> scheduler2 = torch.optim.lr_scheduler.CosineAnnealingLR( 2025-07-17T09:05:59.5622534Z ... optim, 2025-07-17T09:05:59.5622599Z ... T_max=80, 2025-07-17T09:05:59.5622669Z ... eta_min=3e-5, 2025-07-17T09:05:59.5622728Z ... ) 2025-07-17T09:05:59.5622830Z >>> lr = torch.optim.lr_scheduler.SequentialLR( 2025-07-17T09:05:59.5622895Z ... optim, 2025-07-17T09:05:59.5622982Z ... schedulers=[scheduler1, scheduler2], 2025-07-17T09:05:59.5623052Z ... milestones=[20], 2025-07-17T09:05:59.5623115Z ... ) 2025-07-17T09:05:59.5623210Z >>> lr.load_state_dict(torch.load("./save_seq.pt")) 2025-07-17T09:05:59.5623345Z >>> # now load the optimizer checkpoint after loading the LRScheduler 2025-07-17T09:05:59.5623454Z >>> optim.load_state_dict(torch.load("./save_optim.pt")) 2025-07-17T09:05:59.5623509Z 2025-07-17T09:05:59.5623565Z 2025-07-17T09:05:59.5623717Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5623779Z 2025-07-17T09:05:59.5623844Z warnings.warn(msg) 2025-07-17T09:05:59.5623909Z 2025-07-17T09:05:59.5624032Z --- Parse Warning: 132 / 136 --- 2025-07-17T09:05:59.5624522Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SequentialLR in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py line=808. 2025-07-17T09:05:59.5624673Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5624866Z Contains a list of schedulers expected to be called sequentially during the optimization process. 2025-07-17T09:05:59.5624918Z 2025-07-17T09:05:59.5625131Z Specifically, the schedulers will be called according to the milestone points, which should provide exact 2025-07-17T09:05:59.5625262Z intervals by which each scheduler should be called at a given epoch. 2025-07-17T09:05:59.5625384Z 2025-07-17T09:05:59.5625440Z Args: 2025-07-17T09:05:59.5625535Z optimizer (Optimizer): Wrapped optimizer. 2025-07-17T09:05:59.5625630Z schedulers (list): List of chained schedulers. 2025-07-17T09:05:59.5625841Z milestones (list): List of integers that reflects milestone points. 2025-07-17T09:05:59.5625947Z last_epoch (int): The index of last epoch. Default: -1. 2025-07-17T09:05:59.5626007Z 2025-07-17T09:05:59.5626134Z Example: 2025-07-17T09:05:59.5626200Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.5626360Z >>> # Assuming optimizer uses lr = 0.05 for all groups 2025-07-17T09:05:59.5626432Z >>> # lr = 0.005 if epoch == 0 2025-07-17T09:05:59.5626502Z >>> # lr = 0.005 if epoch == 1 2025-07-17T09:05:59.5626567Z >>> # lr = 0.005 if epoch == 2 2025-07-17T09:05:59.5626626Z >>> # ... 2025-07-17T09:05:59.5626764Z >>> # lr = 0.05 if epoch == 20 2025-07-17T09:05:59.5626836Z >>> # lr = 0.045 if epoch == 21 2025-07-17T09:05:59.5626901Z >>> # lr = 0.0405 if epoch == 22 2025-07-17T09:05:59.5627027Z >>> scheduler1 = ConstantLR(optimizer, factor=0.1, total_iters=20) 2025-07-17T09:05:59.5627125Z >>> scheduler2 = ExponentialLR(optimizer, gamma=0.9) 2025-07-17T09:05:59.5627205Z >>> scheduler = SequentialLR( 2025-07-17T09:05:59.5627272Z ... optimizer, 2025-07-17T09:05:59.5627365Z ... schedulers=[scheduler1, scheduler2], 2025-07-17T09:05:59.5627434Z ... milestones=[20], 2025-07-17T09:05:59.5627493Z ... ) 2025-07-17T09:05:59.5627565Z >>> for epoch in range(100): 2025-07-17T09:05:59.5627633Z >>> train(...) 2025-07-17T09:05:59.5627695Z >>> validate(...) 2025-07-17T09:05:59.5627764Z >>> scheduler.step() 2025-07-17T09:05:59.5627823Z 2025-07-17T09:05:59.5627934Z .. image:: ../scripts/lr_scheduler_images/SequentialLR.png 2025-07-17T09:05:59.5627999Z 2025-07-17T09:05:59.5628154Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5628214Z 2025-07-17T09:05:59.5628281Z warnings.warn(msg) 2025-07-17T09:05:59.5628346Z 2025-07-17T09:05:59.5628473Z --- Parse Warning: 133 / 136 --- 2025-07-17T09:05:59.5628991Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ReduceLROnPlateau in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py line=1233. 2025-07-17T09:05:59.5629145Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-07-17T09:05:59.5629266Z Reduce learning rate when a metric has stopped improving. 2025-07-17T09:05:59.5629322Z 2025-07-17T09:05:59.5629453Z Models often benefit from reducing the learning rate by a factor 2025-07-17T09:05:59.5629570Z of 2-10 once learning stagnates. This scheduler reads a metrics 2025-07-17T09:05:59.5629694Z quantity and if no improvement is seen for a 'patience' number 2025-07-17T09:05:59.5629777Z of epochs, the learning rate is reduced. 2025-07-17T09:05:59.5629838Z 2025-07-17T09:05:59.5629897Z Args: 2025-07-17T09:05:59.5629988Z optimizer (Optimizer): Wrapped optimizer. 2025-07-17T09:05:59.5630094Z mode (str): One of `min`, `max`. In `min` mode, lr will 2025-07-17T09:05:59.5630202Z be reduced when the quantity monitored has stopped 2025-07-17T09:05:59.5630308Z decreasing; in `max` mode it will be reduced when the 2025-07-17T09:05:59.5630429Z quantity monitored has stopped increasing. Default: 'min'. 2025-07-17T09:05:59.5630549Z factor (float): Factor by which the learning rate will be 2025-07-17T09:05:59.5630641Z reduced. new_lr = lr * factor. Default: 0.1. 2025-07-17T09:05:59.5630781Z patience (int): The number of allowed epochs with no improvement after 2025-07-17T09:05:59.5630869Z which the learning rate will be reduced. 2025-07-17T09:05:59.5631085Z For example, consider the case of having no patience (`patience = 0`). 2025-07-17T09:05:59.5631303Z In the first epoch, a baseline is established and is always considered good as there's no previous baseline. 2025-07-17T09:05:59.5631485Z In the second epoch, if the performance is worse than the baseline, 2025-07-17T09:05:59.5631636Z we have what is considered an intolerable epoch. 2025-07-17T09:05:59.5631803Z Since the count of intolerable epochs (1) is greater than the patience level (0), 2025-07-17T09:05:59.5631910Z the learning rate is reduced at the end of this epoch. 2025-07-17T09:05:59.5632156Z From the third epoch onwards, the learning rate continues to be reduced at the end of each epoch 2025-07-17T09:05:59.5632343Z if the performance is worse than the baseline. If the performance improves or remains the same, 2025-07-17T09:05:59.5632429Z the learning rate is not adjusted. 2025-07-17T09:05:59.5632496Z Default: 10. 2025-07-17T09:05:59.5632624Z threshold (float): Threshold for measuring the new optimum, 2025-07-17T09:05:59.5632730Z to only focus on significant changes. Default: 1e-4. 2025-07-17T09:05:59.5632848Z threshold_mode (str): One of `rel`, `abs`. In `rel` mode, 2025-07-17T09:05:59.5632945Z dynamic_threshold = best * ( 1 + threshold ) in 'max' 2025-07-17T09:05:59.5633043Z mode or best * ( 1 - threshold ) in `min` mode. 2025-07-17T09:05:59.5633140Z In `abs` mode, dynamic_threshold = best + threshold in 2025-07-17T09:05:59.5633257Z `max` mode or best - threshold in `min` mode. Default: 'rel'. 2025-07-17T09:05:59.5633363Z cooldown (int): Number of epochs to wait before resuming 2025-07-17T09:05:59.5633476Z normal operation after lr has been reduced. Default: 0. 2025-07-17T09:05:59.5633577Z min_lr (float or list): A scalar or a list of scalars. A 2025-07-17T09:05:59.5633676Z lower bound on the learning rate of all param groups 2025-07-17T09:05:59.5633768Z or each group respectively. Default: 0. 2025-07-17T09:05:59.5633881Z eps (float): Minimal decay applied to lr. If the difference 2025-07-17T09:05:59.5633998Z between new and old lr is smaller than eps, the update is 2025-07-17T09:05:59.5634079Z ignored. Default: 1e-8. 2025-07-17T09:05:59.5634137Z 2025-07-17T09:05:59.5634198Z Example: 2025-07-17T09:05:59.5634275Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.5634421Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) 2025-07-17T09:05:59.5634528Z >>> scheduler = ReduceLROnPlateau(optimizer, "min") 2025-07-17T09:05:59.5634596Z >>> for epoch in range(10): 2025-07-17T09:05:59.5634668Z >>> train(...) 2025-07-17T09:05:59.5634747Z >>> val_loss = validate(...) 2025-07-17T09:05:59.5634854Z >>> # Note that step should be called after validate() 2025-07-17T09:05:59.5634928Z >>> scheduler.step(val_loss) 2025-07-17T09:05:59.5634993Z 2025-07-17T09:05:59.5635114Z .. image:: ../scripts/lr_scheduler_images/ReduceLROnPlateau.png 2025-07-17T09:05:59.5635175Z 2025-07-17T09:05:59.5635418Z Original Error: IndentationError('unexpected indent', ('', 8, 4, ' scheduler.step(val_loss)\n', 8, -1)) 2025-07-17T09:05:59.5635474Z 2025-07-17T09:05:59.5635547Z scheduler.step(val_loss) 2025-07-17T09:05:59.5635605Z ^ 2025-07-17T09:05:59.5635670Z warnings.warn(msg) 2025-07-17T09:05:59.5635725Z 2025-07-17T09:05:59.5635861Z --- Parse Warning: 134 / 136 --- 2025-07-17T09:05:59.5636343Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CyclicLR in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py line=1430. 2025-07-17T09:05:59.5636569Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5636767Z Sets the learning rate of each parameter group according to cyclical learning rate policy (CLR). 2025-07-17T09:05:59.5636882Z 2025-07-17T09:05:59.5637105Z The policy cycles the learning rate between two boundaries with a constant frequency, 2025-07-17T09:05:59.5637267Z as detailed in the paper `Cyclical Learning Rates for Training Neural Networks`_. 2025-07-17T09:05:59.5637411Z The distance between the two boundaries can be scaled on a per-iteration 2025-07-17T09:05:59.5637485Z or per-cycle basis. 2025-07-17T09:05:59.5637594Z 2025-07-17T09:05:59.5637746Z Cyclical learning rate policy changes the learning rate after every batch. 2025-07-17T09:05:59.5637865Z `step` should be called after a batch has been used for training. 2025-07-17T09:05:59.5637921Z 2025-07-17T09:05:59.5638050Z This class has three built-in policies, as put forth in the paper: 2025-07-17T09:05:59.5638109Z 2025-07-17T09:05:59.5638235Z * "triangular": A basic triangular cycle without amplitude scaling. 2025-07-17T09:05:59.5638418Z * "triangular2": A basic triangular cycle that scales initial amplitude by half each cycle. 2025-07-17T09:05:59.5638607Z * "exp_range": A cycle that scales initial amplitude by :math:`\text{gamma}^{\text{cycle iterations}}` 2025-07-17T09:05:59.5638685Z at each cycle iteration. 2025-07-17T09:05:59.5638740Z 2025-07-17T09:05:59.5638894Z This implementation was adapted from the github repo: `bckenstler/CLR`_ 2025-07-17T09:05:59.5638953Z 2025-07-17T09:05:59.5639010Z Args: 2025-07-17T09:05:59.5639100Z optimizer (Optimizer): Wrapped optimizer. 2025-07-17T09:05:59.5639222Z base_lr (float or list): Initial learning rate which is the 2025-07-17T09:05:59.5639331Z lower boundary in the cycle for each parameter group. 2025-07-17T09:05:59.5639448Z max_lr (float or list): Upper learning rate boundaries in the cycle 2025-07-17T09:05:59.5639536Z for each parameter group. Functionally, 2025-07-17T09:05:59.5639638Z it defines the cycle amplitude (max_lr - base_lr). 2025-07-17T09:05:59.5639734Z The lr at any cycle is the sum of base_lr 2025-07-17T09:05:59.5639825Z and some scaling of the amplitude; therefore 2025-07-17T09:05:59.5639924Z max_lr may not actually be reached depending on 2025-07-17T09:05:59.5639990Z scaling function. 2025-07-17T09:05:59.5640103Z step_size_up (int): Number of training iterations in the 2025-07-17T09:05:59.5640184Z increasing half of a cycle. Default: 2000 2025-07-17T09:05:59.5640297Z step_size_down (int): Number of training iterations in the 2025-07-17T09:05:59.5640397Z decreasing half of a cycle. If step_size_down is None, 2025-07-17T09:05:59.5640487Z it is set to step_size_up. Default: None 2025-07-17T09:05:59.5640585Z mode (str): One of {triangular, triangular2, exp_range}. 2025-07-17T09:05:59.5640682Z Values correspond to policies detailed above. 2025-07-17T09:05:59.5640780Z If scale_fn is not None, this argument is ignored. 2025-07-17T09:05:59.5640855Z Default: 'triangular' 2025-07-17T09:05:59.5640961Z gamma (float): Constant in 'exp_range' scaling function: 2025-07-17T09:05:59.5641038Z gamma**(cycle iterations) 2025-07-17T09:05:59.5641105Z Default: 1.0 2025-07-17T09:05:59.5641226Z scale_fn (function): Custom scaling policy defined by a single 2025-07-17T09:05:59.5641306Z argument lambda function, where 2025-07-17T09:05:59.5641387Z 0 <= scale_fn(x) <= 1 for all x >= 0. 2025-07-17T09:05:59.5641464Z If specified, then 'mode' is ignored. 2025-07-17T09:05:59.5641614Z Default: None 2025-07-17T09:05:59.5641709Z scale_mode (str): {'cycle', 'iterations'}. 2025-07-17T09:05:59.5641800Z Defines whether scale_fn is evaluated on 2025-07-17T09:05:59.5641940Z cycle number or cycle iterations (training 2025-07-17T09:05:59.5642202Z iterations since start of cycle). 2025-07-17T09:05:59.5642271Z Default: 'cycle' 2025-07-17T09:05:59.5642391Z cycle_momentum (bool): If ``True``, momentum is cycled inversely 2025-07-17T09:05:59.5642510Z to learning rate between 'base_momentum' and 'max_momentum'. 2025-07-17T09:05:59.5642627Z Default: True 2025-07-17T09:05:59.5642768Z base_momentum (float or list): Lower momentum boundaries in the cycle 2025-07-17T09:05:59.5642899Z for each parameter group. Note that momentum is cycled inversely 2025-07-17T09:05:59.5643007Z to learning rate; at the peak of a cycle, momentum is 2025-07-17T09:05:59.5643102Z 'base_momentum' and learning rate is 'max_lr'. 2025-07-17T09:05:59.5643173Z Default: 0.8 2025-07-17T09:05:59.5643301Z max_momentum (float or list): Upper momentum boundaries in the cycle 2025-07-17T09:05:59.5643398Z for each parameter group. Functionally, 2025-07-17T09:05:59.5643516Z it defines the cycle amplitude (max_momentum - base_momentum). 2025-07-17T09:05:59.5643635Z The momentum at any cycle is the difference of max_momentum 2025-07-17T09:05:59.5643724Z and some scaling of the amplitude; therefore 2025-07-17T09:05:59.5643839Z base_momentum may not actually be reached depending on 2025-07-17T09:05:59.5643949Z scaling function. Note that momentum is cycled inversely 2025-07-17T09:05:59.5644083Z to learning rate; at the start of a cycle, momentum is 'max_momentum' 2025-07-17T09:05:59.5644163Z and learning rate is 'base_lr' 2025-07-17T09:05:59.5644235Z Default: 0.9 2025-07-17T09:05:59.5644371Z last_epoch (int): The index of the last batch. This parameter is used when 2025-07-17T09:05:59.5644512Z resuming a training job. Since `step()` should be invoked after each 2025-07-17T09:05:59.5644646Z batch instead of after each epoch, this number represents the total 2025-07-17T09:05:59.5644782Z number of *batches* computed, not the total number of epochs computed. 2025-07-17T09:05:59.5644906Z When last_epoch=-1, the schedule is started from the beginning. 2025-07-17T09:05:59.5644974Z Default: -1 2025-07-17T09:05:59.5645037Z 2025-07-17T09:05:59.5645096Z Example: 2025-07-17T09:05:59.5645172Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.5645309Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) 2025-07-17T09:05:59.5645408Z >>> scheduler = torch.optim.lr_scheduler.CyclicLR( 2025-07-17T09:05:59.5645472Z ... optimizer, 2025-07-17T09:05:59.5645539Z ... base_lr=0.01, 2025-07-17T09:05:59.5645619Z ... max_lr=0.1, 2025-07-17T09:05:59.5645700Z ... step_size_up=10, 2025-07-17T09:05:59.5645761Z ... ) 2025-07-17T09:05:59.5645866Z >>> data_loader = torch.utils.data.DataLoader(...) 2025-07-17T09:05:59.5645940Z >>> for epoch in range(10): 2025-07-17T09:05:59.5646020Z >>> for batch in data_loader: 2025-07-17T09:05:59.5646092Z >>> train_batch(...) 2025-07-17T09:05:59.5646166Z >>> scheduler.step() 2025-07-17T09:05:59.5646220Z 2025-07-17T09:05:59.5646320Z .. image:: ../scripts/lr_scheduler_images/CyclicLR.png 2025-07-17T09:05:59.5646375Z 2025-07-17T09:05:59.5646556Z .. _Cyclical Learning Rates for Training Neural Networks: https://arxiv.org/abs/1506.01186 2025-07-17T09:05:59.5646721Z .. _bckenstler/CLR: https://github.com/bckenstler/CLR 2025-07-17T09:05:59.5646780Z 2025-07-17T09:05:59.5646938Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5647048Z 2025-07-17T09:05:59.5647119Z warnings.warn(msg) 2025-07-17T09:05:59.5647229Z 2025-07-17T09:05:59.5647356Z --- Parse Warning: 135 / 136 --- 2025-07-17T09:05:59.5647898Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CosineAnnealingWarmRestarts in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py line=1713. 2025-07-17T09:05:59.5648115Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5648262Z Set the learning rate of each parameter group using a cosine annealing schedule. 2025-07-17T09:05:59.5648320Z 2025-07-17T09:05:59.5648430Z The :math:`\eta_{max}` is set to the initial lr, :math:`T_{cur}` 2025-07-17T09:05:59.5648576Z is the number of epochs since the last restart and :math:`T_{i}` is the number 2025-07-17T09:05:59.5648668Z of epochs between two warm restarts in SGDR: 2025-07-17T09:05:59.5648731Z 2025-07-17T09:05:59.5648792Z .. math:: 2025-07-17T09:05:59.5648907Z \eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 + 2025-07-17T09:05:59.5648999Z \cos\left(\frac{T_{cur}}{T_{i}}\pi\right)\right) 2025-07-17T09:05:59.5649061Z 2025-07-17T09:05:59.5649167Z When :math:`T_{cur}=T_{i}`, set :math:`\eta_t = \eta_{min}`. 2025-07-17T09:05:59.5649280Z When :math:`T_{cur}=0` after restart, set :math:`\eta_t=\eta_{max}`. 2025-07-17T09:05:59.5649338Z 2025-07-17T09:05:59.5649411Z It has been proposed in 2025-07-17T09:05:59.5649520Z `SGDR: Stochastic Gradient Descent with Warm Restarts`_. 2025-07-17T09:05:59.5649573Z 2025-07-17T09:05:59.5649633Z Args: 2025-07-17T09:05:59.5649725Z optimizer (Optimizer): Wrapped optimizer. 2025-07-17T09:05:59.5649831Z T_0 (int): Number of iterations until the first restart. 2025-07-17T09:05:59.5649999Z T_mult (int, optional): A factor by which :math:`T_{i}` increases after a restart. Default: 1. 2025-07-17T09:05:59.5650125Z eta_min (float, optional): Minimum learning rate. Default: 0. 2025-07-17T09:05:59.5650254Z last_epoch (int, optional): The index of the last epoch. Default: -1. 2025-07-17T09:05:59.5650309Z 2025-07-17T09:05:59.5650416Z .. _SGDR\: Stochastic Gradient Descent with Warm Restarts: 2025-07-17T09:05:59.5650496Z https://arxiv.org/abs/1608.03983 2025-07-17T09:05:59.5650557Z 2025-07-17T09:05:59.5650617Z Example: 2025-07-17T09:05:59.5650687Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.5650808Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.05) 2025-07-17T09:05:59.5650950Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts( 2025-07-17T09:05:59.5651026Z ... optimizer, T_0=20 2025-07-17T09:05:59.5651088Z ... ) 2025-07-17T09:05:59.5651160Z >>> for epoch in range(100): 2025-07-17T09:05:59.5651236Z >>> train(...) 2025-07-17T09:05:59.5651301Z >>> validate(...) 2025-07-17T09:05:59.5651370Z >>> scheduler.step() 2025-07-17T09:05:59.5651423Z 2025-07-17T09:05:59.5651564Z .. image:: ../scripts/lr_scheduler_images/CosineAnnealingWarmRestarts.png 2025-07-17T09:05:59.5651621Z 2025-07-17T09:05:59.5651774Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5651827Z 2025-07-17T09:05:59.5651896Z warnings.warn(msg) 2025-07-17T09:05:59.5651953Z 2025-07-17T09:05:59.5652080Z --- Parse Warning: 136 / 136 --- 2025-07-17T09:05:59.5652623Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=OneCycleLR in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py line=1863. 2025-07-17T09:05:59.5652786Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T09:05:59.5653022Z Sets the learning rate of each parameter group according to the 1cycle learning rate policy. 2025-07-17T09:05:59.5653140Z 2025-07-17T09:05:59.5653318Z The 1cycle policy anneals the learning rate from an initial learning rate to some maximum 2025-07-17T09:05:59.5653489Z learning rate and then from that maximum learning rate to some minimum learning rate much 2025-07-17T09:05:59.5653625Z lower than the initial learning rate. 2025-07-17T09:05:59.5653762Z This policy was initially described in the paper `Super-Convergence: 2025-07-17T09:05:59.5653893Z Very Fast Training of Neural Networks Using Large Learning Rates`_. 2025-07-17T09:05:59.5653947Z 2025-07-17T09:05:59.5654098Z The 1cycle learning rate policy changes the learning rate after every batch. 2025-07-17T09:05:59.5654216Z `step` should be called after a batch has been used for training. 2025-07-17T09:05:59.5654275Z 2025-07-17T09:05:59.5654349Z This scheduler is not chainable. 2025-07-17T09:05:59.5654413Z 2025-07-17T09:05:59.5654555Z Note also that the total number of steps in the cycle can be determined in one 2025-07-17T09:05:59.5654647Z of two ways (listed in order of precedence): 2025-07-17T09:05:59.5654700Z 2025-07-17T09:05:59.5654798Z #. A value for total_steps is explicitly provided. 2025-07-17T09:05:59.5654915Z #. A number of epochs (epochs) and a number of steps per epoch 2025-07-17T09:05:59.5654994Z (steps_per_epoch) are provided. 2025-07-17T09:05:59.5655096Z In this case, the number of total steps is inferred by 2025-07-17T09:05:59.5655179Z total_steps = epochs * steps_per_epoch 2025-07-17T09:05:59.5655234Z 2025-07-17T09:05:59.5655383Z You must either provide a value for total_steps or provide a value for both 2025-07-17T09:05:59.5655455Z epochs and steps_per_epoch. 2025-07-17T09:05:59.5655521Z 2025-07-17T09:05:59.5655703Z The default behaviour of this scheduler follows the fastai implementation of 1cycle, which 2025-07-17T09:05:59.5655878Z claims that "unpublished work has shown even better results by using only two phases". To 2025-07-17T09:05:59.5656021Z mimic the behaviour of the original paper instead, set ``three_phase=True``. 2025-07-17T09:05:59.5656079Z 2025-07-17T09:05:59.5656144Z Args: 2025-07-17T09:05:59.5656231Z optimizer (Optimizer): Wrapped optimizer. 2025-07-17T09:05:59.5656360Z max_lr (float or list): Upper learning rate boundaries in the cycle 2025-07-17T09:05:59.5656435Z for each parameter group. 2025-07-17T09:05:59.5656569Z total_steps (int): The total number of steps in the cycle. Note that 2025-07-17T09:05:59.5656698Z if a value is not provided here, then it must be inferred by providing 2025-07-17T09:05:59.5656787Z a value for epochs and steps_per_epoch. 2025-07-17T09:05:59.5656852Z Default: None 2025-07-17T09:05:59.5656978Z epochs (int): The number of epochs to train for. This is used along 2025-07-17T09:05:59.5657127Z with steps_per_epoch in order to infer the total number of steps in the cycle 2025-07-17T09:05:59.5657216Z if a value for total_steps is not provided. 2025-07-17T09:05:59.5657280Z Default: None 2025-07-17T09:05:59.5657427Z steps_per_epoch (int): The number of steps per epoch to train for. This is 2025-07-17T09:05:59.5657571Z used along with epochs in order to infer the total number of steps in the 2025-07-17T09:05:59.5657679Z cycle if a value for total_steps is not provided. 2025-07-17T09:05:59.5657742Z Default: None 2025-07-17T09:05:59.5657943Z pct_start (float): The percentage of the cycle (in number of steps) spent 2025-07-17T09:05:59.5658026Z increasing the learning rate. 2025-07-17T09:05:59.5658145Z Default: 0.3 2025-07-17T09:05:59.5658229Z anneal_strategy (str): {'cos', 'linear'} 2025-07-17T09:05:59.5658435Z Specifies the annealing strategy: "cos" for cosine annealing, "linear" for 2025-07-17T09:05:59.5658505Z linear annealing. 2025-07-17T09:05:59.5658580Z Default: 'cos' 2025-07-17T09:05:59.5658705Z cycle_momentum (bool): If ``True``, momentum is cycled inversely 2025-07-17T09:05:59.5658884Z to learning rate between 'base_momentum' and 'max_momentum'. 2025-07-17T09:05:59.5658956Z Default: True 2025-07-17T09:05:59.5659089Z base_momentum (float or list): Lower momentum boundaries in the cycle 2025-07-17T09:05:59.5659221Z for each parameter group. Note that momentum is cycled inversely 2025-07-17T09:05:59.5659325Z to learning rate; at the peak of a cycle, momentum is 2025-07-17T09:05:59.5659419Z 'base_momentum' and learning rate is 'max_lr'. 2025-07-17T09:05:59.5659480Z Default: 0.85 2025-07-17T09:05:59.5659610Z max_momentum (float or list): Upper momentum boundaries in the cycle 2025-07-17T09:05:59.5659692Z for each parameter group. Functionally, 2025-07-17T09:05:59.5659812Z it defines the cycle amplitude (max_momentum - base_momentum). 2025-07-17T09:05:59.5659893Z Note that momentum is cycled inversely 2025-07-17T09:05:59.5660026Z to learning rate; at the start of a cycle, momentum is 'max_momentum' 2025-07-17T09:05:59.5660105Z and learning rate is 'base_lr' 2025-07-17T09:05:59.5660169Z Default: 0.95 2025-07-17T09:05:59.5660282Z div_factor (float): Determines the initial learning rate via 2025-07-17T09:05:59.5660360Z initial_lr = max_lr/div_factor 2025-07-17T09:05:59.5660424Z Default: 25 2025-07-17T09:05:59.5660551Z final_div_factor (float): Determines the minimum learning rate via 2025-07-17T09:05:59.5660631Z min_lr = initial_lr/final_div_factor 2025-07-17T09:05:59.5660698Z Default: 1e4 2025-07-17T09:05:59.5660850Z three_phase (bool): If ``True``, use a third phase of the schedule to annihilate the 2025-07-17T09:05:59.5661007Z learning rate according to 'final_div_factor' instead of modifying the second 2025-07-17T09:05:59.5661152Z phase (the first two phases will be symmetrical about the step indicated by 2025-07-17T09:05:59.5661217Z 'pct_start'). 2025-07-17T09:05:59.5661350Z last_epoch (int): The index of the last batch. This parameter is used when 2025-07-17T09:05:59.5661489Z resuming a training job. Since `step()` should be invoked after each 2025-07-17T09:05:59.5661623Z batch instead of after each epoch, this number represents the total 2025-07-17T09:05:59.5661753Z number of *batches* computed, not the total number of epochs computed. 2025-07-17T09:05:59.5661875Z When last_epoch=-1, the schedule is started from the beginning. 2025-07-17T09:05:59.5661939Z Default: -1 2025-07-17T09:05:59.5662004Z 2025-07-17T09:05:59.5662067Z Example: 2025-07-17T09:05:59.5662141Z >>> # xdoctest: +SKIP 2025-07-17T09:05:59.5662239Z >>> data_loader = torch.utils.data.DataLoader(...) 2025-07-17T09:05:59.5662386Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.9) 2025-07-17T09:05:59.5662490Z >>> scheduler = torch.optim.lr_scheduler.OneCycleLR( 2025-07-17T09:05:59.5662629Z ... optimizer, max_lr=0.01, steps_per_epoch=len(data_loader), epochs=10 2025-07-17T09:05:59.5662687Z ... ) 2025-07-17T09:05:59.5662824Z >>> for epoch in range(10): 2025-07-17T09:05:59.5662902Z >>> for batch in data_loader: 2025-07-17T09:05:59.5662985Z >>> train_batch(...) 2025-07-17T09:05:59.5663108Z >>> optimizer.step() 2025-07-17T09:05:59.5663234Z >>> scheduler.step() 2025-07-17T09:05:59.5663288Z 2025-07-17T09:05:59.5663399Z .. image:: ../scripts/lr_scheduler_images/OneCycleLR.png 2025-07-17T09:05:59.5663453Z 2025-07-17T09:05:59.5663629Z .. _Super-Convergence\: Very Fast Training of Neural Networks Using Large Learning Rates: 2025-07-17T09:05:59.5663767Z https://arxiv.org/abs/1708.07120 2025-07-17T09:05:59.5663827Z 2025-07-17T09:05:59.5663986Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T09:05:59.5664043Z 2025-07-17T09:05:59.5664112Z warnings.warn(msg) 2025-07-17T09:05:59.5664169Z 2025-07-17T09:05:59.5664258Z  2025-07-17T09:05:59.5664371Z === Found 9 run-time warnings === 2025-07-17T09:05:59.5664488Z --- Runtime Warning: 1 / 9 --- 2025-07-17T09:05:59.5664672Z example = 2025-07-17T09:05:59.5665036Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py:281: UserWarning: Warning only once for all operators, other operators may also be overridden. 2025-07-17T09:05:59.5665220Z Overriding a previously registered kernel for the same operator and the same dispatch key 2025-07-17T09:05:59.5665410Z operator: aten::div.Tensor(Tensor self, Tensor other) -> Tensor 2025-07-17T09:05:59.5665595Z registered at /var/lib/jenkins/workspace/build/aten/src/ATen/RegisterSchema.cpp:6 2025-07-17T09:05:59.5665669Z dispatch key: CPU 2025-07-17T09:05:59.5665914Z previous kernel: registered at /var/lib/jenkins/workspace/aten/src/ATen/LegacyBatchingRegistrations.cpp:1079 2025-07-17T09:05:59.5666227Z new kernel: registered at /dev/null:811 (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/core/dispatch/OperatorEntry.cpp:218.) 2025-07-17T09:05:59.5666329Z impl_fn(self.ns, name.split("::")[-1], dispatch_key) 2025-07-17T09:05:59.5666394Z 2025-07-17T09:05:59.5666506Z --- Runtime Warning: 2 / 9 --- 2025-07-17T09:05:59.5666663Z example = 2025-07-17T09:05:59.5667393Z /opt/conda/envs/py_3.12/lib/python3.12/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:1975.) 2025-07-17T09:05:59.5667472Z return super().refine_names(names) 2025-07-17T09:05:59.5667526Z 2025-07-17T09:05:59.5667636Z --- Runtime Warning: 3 / 9 --- 2025-07-17T09:05:59.5667780Z example = 2025-07-17T09:05:59.5668792Z /opt/conda/envs/py_3.12/lib/python3.12/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:178.) 2025-07-17T09:05:59.5668949Z return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None) 2025-07-17T09:05:59.5669006Z 2025-07-17T09:05:59.5669114Z --- Runtime Warning: 4 / 9 --- 2025-07-17T09:05:59.5669262Z example = 2025-07-17T09:05:59.5670212Z :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:53.) 2025-07-17T09:05:59.5670389Z 2025-07-17T09:05:59.5670509Z --- Runtime Warning: 5 / 9 --- 2025-07-17T09:05:59.5670707Z example = 2025-07-17T09:05:59.5671600Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T09:05:59.5671714Z new_node = root_const_gm.graph.get_attr(in_node.target) 2025-07-17T09:05:59.5671774Z 2025-07-17T09:05:59.5671897Z --- Runtime Warning: 6 / 9 --- 2025-07-17T09:05:59.5672066Z example = 2025-07-17T09:05:59.5672522Z /opt/conda/envs/py_3.12/lib/python3.12/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`. 2025-07-17T09:05:59.5672603Z WeightNorm.apply(module, name, dim) 2025-07-17T09:05:59.5672669Z 2025-07-17T09:05:59.5672777Z --- Runtime Warning: 7 / 9 --- 2025-07-17T09:05:59.5672963Z example = 2025-07-17T09:05:59.5673399Z /opt/conda/envs/py_3.12/lib/python3.12/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`. 2025-07-17T09:05:59.5673482Z WeightNorm.apply(module, name, dim) 2025-07-17T09:05:59.5673534Z 2025-07-17T09:05:59.5673646Z --- Runtime Warning: 8 / 9 --- 2025-07-17T09:05:59.5673817Z example = 2025-07-17T09:05:59.5674414Z /opt/conda/envs/py_3.12/lib/python3.12/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) 2025-07-17T09:05:59.5674482Z warnings.warn( 2025-07-17T09:05:59.5674537Z 2025-07-17T09:05:59.5674642Z --- Runtime Warning: 9 / 9 --- 2025-07-17T09:05:59.5674831Z example = 2025-07-17T09:05:59.5675406Z /opt/conda/envs/py_3.12/lib/python3.12/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) 2025-07-17T09:05:59.5675476Z warnings.warn( 2025-07-17T09:05:59.5675534Z 2025-07-17T09:05:59.5675727Z === 342 passed, 377 skipped, 145 warnings in 10.30 seconds === 2025-07-17T09:05:59.5675871Z Running dynamo/test_fake_distributed 1/1 ... [2025-07-17 09:05:59.455801] 2025-07-17T09:05:59.5675957Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T09:05:59.5676459Z Executing ['/opt/conda/envs/py_3.12/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 09:05:59.456217] 2025-07-17T09:06:08.1900629Z 2025-07-17T09:06:08.1902146Z 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_c2268c586678a3bc_.log 2025-07-17T09:06:08.1902923Z Running 1 items in this shard: test/dynamo/test_fake_distributed.py::TestFakeDistributed::test_all_to_all_single_autograd 2025-07-17T09:06:08.1903509Z 2025-07-17T09:06:08.1903911Z Running inductor/test_benchmark_fusion 1/1 ... [2025-07-17 09:06:08.190247] 2025-07-17T09:06:08.1904226Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T09:06:08.1907648Z Executing ['/opt/conda/envs/py_3.12/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 09:06:08.190591] 2025-07-17T09:06:52.3002652Z 2025-07-17T09:06:52.3007162Z 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_b7a70c04d5bf0c09_.log 2025-07-17T09:06:52.3011336Z Running 16 items in this shard: test/inductor/test_benchmark_fusion.py::BenchmarkFusionCudaTest::test_avoid_register_spilling_cuda, test/inductor/test_benchmark_fusion.py::BenchmarkFusionCudaTest::test_foreach_kernel_cuda, test/inductor/test_benchmark_fusion.py::BenchmarkFusionCudaTest::test_register_spills_cuda, test/inductor/test_benchmark_fusion.py::BenchmarkFusionCudaTest::test_resnet18_cuda, test/inductor/test_benchmark_fusion.py::BenchmarkFusionCudaTest::test_softmax_cuda, test/inductor/test_benchmark_fusion.py::BenchmarkFusionCudaTest::test_tield_kernel_fusion_cuda, test/inductor/test_benchmark_fusion.py::BenchmarkingTest::test_benchmark_on_non_zero_device, test/inductor/test_benchmark_fusion.py::BenchmarkMultiTemplateFusionCudaTest::test_changed_layout, test/inductor/test_benchmark_fusion.py::BenchmarkMultiTemplateFusionCudaTest::test_equivalent_extern_code, test/inductor/test_benchmark_fusion.py::BenchmarkMultiTemplateFusionCudaTest::test_equivalent_template_code, 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 2025-07-17T09:06:52.3015160Z 2025-07-17T09:06:52.3015322Z Running inductor/test_cutlass_backend 1/1 ... [2025-07-17 09:06:52.300292] 2025-07-17T09:06:52.3015622Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T09:06:52.3016280Z Executing ['/opt/conda/envs/py_3.12/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 09:06:52.300603] 2025-07-17T09:06:59.6315597Z 2025-07-17T09:06:59.6316676Z 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_c9e5bd123fce4659_.log 2025-07-17T09:06:59.6348732Z Running 127 items in this shard: test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_aoti_workspace_ptr, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_compilation_time_use_aoti_False, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_compilation_time_use_aoti_True, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_config_number_post_filtering_layout_rc_bfloat16, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_config_number_post_filtering_layout_rc_float16, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_config_number_post_filtering_layout_rr_bfloat16, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_config_number_post_filtering_layout_rr_float16, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_cutlass_backend_fp8_scaled_mm_fast_accum_filtering, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_cutlass_backend_integration, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_cutlass_backend_matmul_same_tensor, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_cutlass_backend_op_allowlist, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_cutlass_backend_op_denylist, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_cutlass_backend_shape_coverage_mm, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_cutlass_backend_subproc_addmm, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_cutlass_backend_subproc_bmm, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_cutlass_backend_subproc_mm, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_cutlass_key, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_cutlass_presets_presets_, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_cutlass_presets_presets_0, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_cutlass_presets_presets_0,999, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_diff_matmul_share_same_kernel_dynamic_False, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_diff_matmul_share_same_kernel_dynamic_True, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_broadcasting_add, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_broadcasting_div, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_broadcasting_mul, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_broadcasting_sub, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_flexible_layout, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_fusions_basic_add_shape0, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_fusions_basic_add_shape1, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_fusions_basic_add_shape2, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_fusions_basic_add_shape3, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_fusions_basic_div_shape0, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_fusions_basic_div_shape1, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_fusions_basic_div_shape2, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_fusions_basic_div_shape3, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_fusions_basic_mul_shape0, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_fusions_basic_mul_shape1, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_fusions_basic_mul_shape2, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_fusions_basic_mul_shape3, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_fusions_basic_relu_shape0, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_fusions_basic_relu_shape1, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_fusions_basic_relu_shape2, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_fusions_basic_relu_shape3, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_fusions_basic_sub_shape0, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_fusions_basic_sub_shape1, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_fusions_basic_sub_shape2, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_fusions_basic_sub_shape3, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_mixed_dtypes_add, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_mixed_dtypes_div, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_mixed_dtypes_mul, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_mixed_dtypes_relu, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_mixed_dtypes_sub, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_multi_op_add, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_multi_op_div, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_multi_op_mul, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_multi_op_relu, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_multi_op_sub, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_multi_output_add_dynamic_False, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_multi_output_add_dynamic_True, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_multi_output_div_dynamic_False, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_multi_output_div_dynamic_True, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_multi_output_mul_dynamic_False, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_multi_output_mul_dynamic_True, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_multi_output_relu_dynamic_False, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_multi_output_relu_dynamic_True, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_multi_output_sub_dynamic_False, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_multi_output_sub_dynamic_True, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_return_accumulator, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_reuse_matmul_input_add, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_reuse_matmul_input_div, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_reuse_matmul_input_mul, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_reuse_matmul_input_relu, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_evt_reuse_matmul_input_sub, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_filtered_ops_cache, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_flexible_layout, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_force_cutlass_backend_aoti_cexpr_codegen, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_force_cutlass_backend_aoti_dynamic, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_fp8_rowwise_scaling_float8_e4m3fn_shape0_has_bias_False_use_fast_accum_False, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_fp8_rowwise_scaling_float8_e4m3fn_shape0_has_bias_True_use_fast_accum_False, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_fp8_tensorwise_scaling_float8_e4m3fn_shape0_has_bias_False_use_fast_accum_False, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_fp8_tensorwise_scaling_float8_e4m3fn_shape0_has_bias_True_use_fast_accum_False, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_gemm_operation_serialization_arch_100_cuda_version_12_4, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_gemm_operation_serialization_arch_100_cuda_version_12_8, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_gemm_operation_serialization_arch_90_cuda_version_12_4, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_gemm_operation_serialization_arch_90_cuda_version_12_8, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_get_max_alignment, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_import_cutlass, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_addmm_dynamic_False_use_aoti_False_bfloat16, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_addmm_dynamic_False_use_aoti_False_float16, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_addmm_dynamic_False_use_aoti_True_bfloat16, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_addmm_dynamic_False_use_aoti_True_float16, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_addmm_dynamic_True_use_aoti_False_bfloat16, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_addmm_dynamic_True_use_aoti_False_float16, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_addmm_dynamic_True_use_aoti_True_bfloat16, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_addmm_dynamic_True_use_aoti_True_float16, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_bmm_dynamic_False_use_aoti_False_bfloat16, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_bmm_dynamic_False_use_aoti_False_float16, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_bmm_dynamic_False_use_aoti_True_bfloat16, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_bmm_dynamic_False_use_aoti_True_float16, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_bmm_dynamic_True_use_aoti_False_bfloat16, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_bmm_dynamic_True_use_aoti_False_float16, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_bmm_dynamic_True_use_aoti_True_bfloat16, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_bmm_dynamic_True_use_aoti_True_float16, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_chained_fusion_fp16_fp32acc, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_fp8_scaled_mm_dynamic_False_use_aoti_False_float8_e4m3fn, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_fp8_scaled_mm_dynamic_False_use_aoti_True_float8_e4m3fn, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_fp8_scaled_mm_dynamic_True_use_aoti_False_float8_e4m3fn, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_fp8_scaled_mm_dynamic_True_use_aoti_True_float8_e4m3fn, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_int_mm_dynamic_False, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_no_fusion_dtype_mismatch, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_regular_mm_dynamic_False_use_aoti_False_bfloat16, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_regular_mm_dynamic_False_use_aoti_False_float16, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_regular_mm_dynamic_False_use_aoti_True_bfloat16, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_regular_mm_dynamic_False_use_aoti_True_float16, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_regular_mm_dynamic_True_use_aoti_False_bfloat16, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_regular_mm_dynamic_True_use_aoti_False_float16, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_regular_mm_dynamic_True_use_aoti_True_bfloat16, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_regular_mm_dynamic_True_use_aoti_True_float16, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_regular_mm_streamk, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_relu6_fusion_fp16_fp32acc, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_relu_fusion_fp16_fp32acc, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_shape_dependent_normalization_fusion, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_simple_fusion_fp16_fp32acc, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_backend_sparse_semi_structured_mm_dynamic_False, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_max_autotune_cutlass_threshold, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_number_mm_precompiles, test/inductor/test_cutlass_backend.py::TestCutlassBackend::test_standalone_runner 2025-07-17T09:06:59.6387317Z 2025-07-17T09:06:59.6387508Z Running inductor/test_distributed_patterns 1/1 ... [2025-07-17 09:06:59.631631] 2025-07-17T09:06:59.6387832Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T09:06:59.6388508Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_distributed_patterns.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 09:06:59.631967] 2025-07-17T09:07:21.6936438Z 2025-07-17T09:07:21.6937497Z 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_10ece23db9813afb_.log 2025-07-17T09:07:21.6942939Z 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 2025-07-17T09:07:21.6948311Z 2025-07-17T09:07:21.6948483Z Running inductor/test_flex_attention 1/5 ... [2025-07-17 09:07:21.693683] 2025-07-17T09:07:21.6948776Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T09:07:21.6949438Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_flex_attention.py', '--shard-id=1', '--num-shards=5', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 09:07:21.694008] 2025-07-17T09:18:05.9988940Z 2025-07-17T09:18:05.9990567Z inductor/test_flex_attention 1/5 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_flex_attention_1.5_c0258f842003c0e1_.log 2025-07-17T09:18:06.0034753Z Running 149 items in this shard: test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_GQA_score_mod0_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_GQA_score_mod3_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_aot_eager_gradcheck_score_mod1_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_autograd_function_in_score_mod_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_automatic_dynamic_score_mod1_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_automatic_dynamic_score_mod4_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod0_BLOCK_SIZE3_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod0_BLOCK_SIZE3_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod1_BLOCK_SIZE2_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod2_BLOCK_SIZE2_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod2_BLOCK_SIZE3_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod3_BLOCK_SIZE2_cuda_float16, 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test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_multiplicative_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_multiplicative_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_multiplicative_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:bfloat16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_multiplicative_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_relative_1d_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:bfloat16_mode_max-autotune-no-cudagraphs_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_relative_1d_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float16_mode_max-autotune-no-cudagraphs_cuda, 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test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_symmetric_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16_mode_default_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_symmetric_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16_mode_max-autotune-no-cudagraphs_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_symmetric_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float16_mode_max-autotune-no-cudagraphs_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_symmetric_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:bfloat16_mode_max-autotune-no-cudagraphs_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_symmetric_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_mode_default_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_weird_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:bfloat16_cuda 2025-07-17T09:18:06.0077948Z 2025-07-17T09:18:06.0078099Z Running inductor/test_flex_attention 2/5 ... [2025-07-17 09:18:05.999072] 2025-07-17T09:18:06.0078394Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T09:18:06.0079044Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_flex_attention.py', '--shard-id=2', '--num-shards=5', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 09:18:05.999412] 2025-07-17T09:27:05.4652238Z 2025-07-17T09:27:05.4653223Z inductor/test_flex_attention 2/5 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_flex_attention_2.5_713df2e444e2b2c0_.log 2025-07-17T09:27:05.4694527Z Running 139 items in this shard: test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_GQA_score_mod6_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_GQA_score_mod7_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_aot_eager_gradcheck_score_mod2_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_automatic_dynamic_score_mod0_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod0_BLOCK_SIZE2_cuda_float16, 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test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_symmetric_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_mode_max-autotune-no-cudagraphs_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_weird_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_weird_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_weird_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_cuda 2025-07-17T09:27:05.4734894Z 2025-07-17T09:27:05.4735061Z GITHUB_RUN_ID, GITHUB_RUN_ATTEMPT, or ARTIFACTS_FILE_SUFFIX not set, not uploading 2025-07-17T09:27:05.4735368Z Uploading artifacts took 0.00 seconds 2025-07-17T09:27:05.4735652Z Running inductor/test_flex_attention 3/5 ... [2025-07-17 09:27:05.465428] 2025-07-17T09:27:05.4735948Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T09:27:05.4736608Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_flex_attention.py', '--shard-id=3', '--num-shards=5', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 09:27:05.465735] 2025-07-17T09:37:09.2948274Z 2025-07-17T09:37:09.2951423Z inductor/test_flex_attention 3/5 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_flex_attention_3.5_cc719d2a1e72ee1a_.log 2025-07-17T09:37:09.2993935Z Running 144 items in this shard: test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_GQA_causal_mask_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_block_mask_non_divisible_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod0_BLOCK_SIZE2_cuda_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod0_BLOCK_SIZE_128_cuda_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_different_block_size_score_mod1_BLOCK_SIZE2_cuda_float32, 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test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_comparison_vs_sdpa_with_learnable_bias_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_distinct_biases_batch:2_head:4_seq_len:256_headdim:16_dtype:bfloat16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_distinct_biases_batch:2_head:4_seq_len:256_headdim:16_dtype:float32_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_flipped_indexed_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_flipped_indexed_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:bfloat16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_global_tokens_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_head_specific_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:bfloat16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_head_specific_gate_batch:2_head:4_seq_len:256_headdim:16_dtype:float32_mode_max-autotune-no-cudagraphs_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_head_specific_gate_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_mode_max-autotune-no-cudagraphs_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_head_specific_gate_batch:2_head:4_seq_len:37_headdim:16_dtype:float16_mode_default_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_indirect_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_indirect_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_local_window_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_multiplicative_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_relative_1d_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:bfloat16_mode_default_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_relative_1d_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16_mode_default_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_relative_1d_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16_mode_max-autotune-no-cudagraphs_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_relative_1d_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:bfloat16_mode_default_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_relative_1d_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_mode_max-autotune-no-cudagraphs_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_relative_1d_bias_only_grad_batch:2_head:4_seq_len:256_headdim:16_dtype:float32_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_relative_1d_bias_only_grad_batch:2_head:4_seq_len:277_headdim:16_dtype:float16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_relative_1d_bias_only_grad_batch:2_head:4_seq_len:37_headdim:16_dtype:bfloat16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_relative_1d_bias_only_grad_batch:2_head:4_seq_len:37_headdim:16_dtype:float16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_symmetric_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32_mode_max-autotune-no-cudagraphs_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_symmetric_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_mode_default_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_weird_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_weird_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_cuda 2025-07-17T09:37:09.3035363Z 2025-07-17T09:37:09.3035516Z Running inductor/test_flex_attention 4/5 ... [2025-07-17 09:37:09.295064] 2025-07-17T09:37:09.3035812Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T09:37:09.3036465Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_flex_attention.py', '--shard-id=4', '--num-shards=5', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 09:37:09.295385] 2025-07-17T09:47:05.4779081Z 2025-07-17T09:47:05.4780203Z inductor/test_flex_attention 4/5 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_flex_attention_4.5_5c10f911c4758d5f_.log 2025-07-17T09:47:05.4839464Z Running 154 items in this shard: test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_GQA_score_mod1_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_GQA_score_mod2_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_GQA_score_mod5_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_aot_eager_gradcheck_score_mod4_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_automatic_dynamic_score_mod3_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_automatic_dynamic_score_mod5_cuda_float16, 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test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_symmetric_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:bfloat16_mode_default_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_symmetric_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float16_mode_default_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_symmetric_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float16_mode_max-autotune-no-cudagraphs_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_weird_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_weird_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float16_cuda 2025-07-17T09:47:05.4887022Z 2025-07-17T09:47:05.4887235Z GITHUB_RUN_ID, GITHUB_RUN_ATTEMPT, or ARTIFACTS_FILE_SUFFIX not set, not uploading 2025-07-17T09:47:05.4887632Z Uploading artifacts took 0.00 seconds 2025-07-17T09:47:05.4887974Z Running inductor/test_flex_attention 5/5 ... [2025-07-17 09:47:05.478257] 2025-07-17T09:47:05.4888345Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T09:47:05.4889158Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_flex_attention.py', '--shard-id=5', '--num-shards=5', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 09:47:05.478634] 2025-07-17T09:55:49.7798030Z 2025-07-17T09:55:49.7799073Z inductor/test_flex_attention 5/5 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_flex_attention_5.5_4bf2deb0b858876e_.log 2025-07-17T09:55:49.7837853Z Running 129 items in this shard: test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_GQA_score_mod4_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_aot_eager_gradcheck_score_mod0_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_aot_eager_gradcheck_score_mod3_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_aot_eager_gradcheck_score_mod5_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_automatic_dynamic_score_mod2_cuda_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_builtin_score_mods_automatic_dynamic_score_mod6_cuda_float16, 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test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_tensor_subclass_dispatch_order_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_triton_template_warp_specialization_cuda, test/inductor/test_flex_attention.py::TestFlexAttentionCUDA::test_zero_length_sequence_error_cuda, test/inductor/test_flex_attention.py::TestPagedAttentionCUDA::test_page_allocation_cuda, test/inductor/test_flex_attention.py::TestPagedAttentionCUDA::test_paged_builtin_score_mods_score_mod2_cuda_float32, test/inductor/test_flex_attention.py::TestPagedAttentionCUDA::test_paged_builtin_score_mods_score_mod3_cuda_float32, test/inductor/test_flex_attention.py::TestPagedAttentionCUDA::test_paged_builtin_score_mods_score_mod4_cuda_bfloat16, test/inductor/test_flex_attention.py::TestPagedAttentionCUDA::test_paged_builtin_score_mods_score_mod4_cuda_float32, test/inductor/test_flex_attention.py::TestPagedAttentionCUDA::test_paged_builtin_score_mods_score_mod7_cuda_float16, test/inductor/test_flex_attention.py::TestBlockMaskCUDA::test_backward_error_with_none_q_indices_cuda, test/inductor/test_flex_attention.py::TestBlockMaskCUDA::test_block_mask_viz_cuda, test/inductor/test_flex_attention.py::TestBlockMaskCUDA::test_block_size_cuda, test/inductor/test_flex_attention.py::TestBlockMaskCUDA::test_compiling_create_block_mask_no_recompile_cuda, test/inductor/test_flex_attention.py::TestBlockMaskCUDA::test_doc_mask_clamped_repro_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_batch_head_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_batch_head_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_distinct_biases_batch:2_head:4_seq_len:256_headdim:16_dtype:float16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_distinct_biases_batch:2_head:4_seq_len:277_headdim:16_dtype:float16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_distinct_biases_batch:2_head:4_seq_len:37_headdim:16_dtype:bfloat16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_flipped_indexed_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_flipped_indexed_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_flipped_indexed_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_global_tokens_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_global_tokens_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_global_tokens_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:bfloat16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_head_specific_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_head_specific_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_head_specific_gate_batch:2_head:4_seq_len:277_headdim:16_dtype:bfloat16_mode_default_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_head_specific_gate_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_mode_default_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_head_specific_gate_batch:2_head:4_seq_len:37_headdim:16_dtype:float16_mode_max-autotune-no-cudagraphs_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_head_specific_gate_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_mode_default_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_head_specific_gate_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_mode_max-autotune-no-cudagraphs_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_indirect_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_local_window_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_multiplicative_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:bfloat16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_multiplicative_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_multiplicative_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_multiplicative_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_relative_1d_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float16_mode_max-autotune-no-cudagraphs_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_relative_1d_bias_only_grad_batch:2_head:4_seq_len:256_headdim:16_dtype:bfloat16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_relative_1d_bias_only_grad_batch:2_head:4_seq_len:256_headdim:16_dtype:float16_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_relative_1d_bias_only_grad_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_relative_1d_bias_only_grad_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_symmetric_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:bfloat16_mode_max-autotune-no-cudagraphs_cuda, test/inductor/test_flex_attention.py::TestLearnableBiasesCUDA::test_weird_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:bfloat16_cuda 2025-07-17T09:55:49.7875577Z 2025-07-17T09:55:49.7875709Z Running nn/test_convolution 1/1 ... [2025-07-17 09:55:49.780003] 2025-07-17T09:55:49.7875982Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T09:55:49.7876613Z Executing ['/opt/conda/envs/py_3.12/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 09:55:49.780320] 2025-07-17T09:58:15.9910892Z 2025-07-17T09:58:15.9915507Z nn/test_convolution 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_convolution_1.1_335407198b7a3695_.log 2025-07-17T09:58:16.0128128Z Running 607 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::TestConvolutionNNDeviceTypeCUDA::test_Conv2d_backward_depthwise_cuda_complex128, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv2d_backward_depthwise_cuda_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv2d_depthwise_naive_groups_cuda_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv2d_depthwise_naive_groups_cuda_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv2d_depthwise_naive_groups_cuda_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv2d_deterministic_cudnn_dilation_1_cuda_bfloat16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv2d_deterministic_cudnn_dilation_1_cuda_complex128, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv2d_deterministic_cudnn_dilation_1_cuda_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv2d_deterministic_cudnn_dilation_1_cuda_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv2d_deterministic_cudnn_dilation_1_cuda_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv2d_deterministic_cudnn_dilation_1_cuda_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv2d_deterministic_cudnn_dilation_2_cuda_bfloat16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv2d_deterministic_cudnn_dilation_2_cuda_complex128, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv2d_deterministic_cudnn_dilation_2_cuda_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv2d_deterministic_cudnn_dilation_2_cuda_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv2d_deterministic_cudnn_dilation_2_cuda_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv2d_deterministic_cudnn_dilation_2_cuda_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv2d_deterministic_cudnn_dilation_3_cuda_bfloat16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv2d_deterministic_cudnn_dilation_3_cuda_complex128, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv2d_deterministic_cudnn_dilation_3_cuda_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv2d_deterministic_cudnn_dilation_3_cuda_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv2d_deterministic_cudnn_dilation_3_cuda_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv2d_deterministic_cudnn_dilation_3_cuda_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv2d_large_workspace_cuda_bfloat16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv2d_large_workspace_cuda_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv2d_large_workspace_cuda_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv2d_large_workspace_cuda_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv2d_naive_groups_cuda_bfloat16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv2d_naive_groups_cuda_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv2d_naive_groups_cuda_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv2d_naive_groups_cuda_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv2d_size_1_kernel_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv3d_depthwise_naive_groups_cuda_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv3d_depthwise_naive_groups_cuda_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_Conv3d_depthwise_naive_groups_cuda_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_ConvTranspose2d_large_output_padding_cuda_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_ConvTranspose2d_large_output_padding_cuda_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_ConvTranspose2d_size_1_kernel_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_ConvTranspose3d_size_1_kernel_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_contig_wrong_stride_cudnn_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv1d_same_padding_backward_cuda_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv1d_same_padding_backward_cuda_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv1d_same_padding_cuda_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv1d_same_padding_cuda_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv1d_valid_padding_backward_cuda_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv1d_valid_padding_backward_cuda_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv1d_valid_padding_cuda_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv1d_valid_padding_cuda_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv1d_vs_scipy_mode_same_cuda_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv1d_vs_scipy_mode_same_cuda_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv1d_vs_scipy_mode_valid_cuda_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv1d_vs_scipy_mode_valid_cuda_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv2d_no_grad_cuda_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv2d_same_padding_backward_cuda_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv2d_same_padding_backward_cuda_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv2d_same_padding_cuda_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv2d_same_padding_cuda_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv2d_valid_padding_backward_cuda_complex64, 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test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow2d_dilated_transposed_has_bias_True_strided_False_contiguous_True_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow2d_dilated_transposed_has_bias_True_strided_True_contiguous_False_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow2d_dilated_transposed_has_bias_True_strided_True_contiguous_True_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow2d_has_bias_False_strided_False_contiguous_False_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow2d_has_bias_False_strided_False_contiguous_True_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow2d_has_bias_False_strided_True_contiguous_False_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow2d_has_bias_False_strided_True_contiguous_True_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow2d_has_bias_True_strided_False_contiguous_False_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow2d_has_bias_True_strided_False_contiguous_True_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow2d_has_bias_True_strided_True_contiguous_False_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow2d_has_bias_True_strided_True_contiguous_True_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow2d_transposed_has_bias_False_strided_False_contiguous_False_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow2d_transposed_has_bias_False_strided_False_contiguous_True_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow2d_transposed_has_bias_False_strided_True_contiguous_False_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow2d_transposed_has_bias_False_strided_True_contiguous_True_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow2d_transposed_has_bias_True_strided_False_contiguous_False_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow2d_transposed_has_bias_True_strided_False_contiguous_True_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow2d_transposed_has_bias_True_strided_True_contiguous_False_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow2d_transposed_has_bias_True_strided_True_contiguous_True_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow3d_cpu_has_bias_False_strided_False_contiguous_False_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow3d_cpu_has_bias_False_strided_False_contiguous_True_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow3d_cpu_has_bias_False_strided_True_contiguous_False_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow3d_cpu_has_bias_False_strided_True_contiguous_True_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow3d_cpu_has_bias_True_strided_False_contiguous_False_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow3d_cpu_has_bias_True_strided_False_contiguous_True_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow3d_cpu_has_bias_True_strided_True_contiguous_False_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow3d_cpu_has_bias_True_strided_True_contiguous_True_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow3d_cuda_has_bias_False_strided_False_contiguous_False_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow3d_cuda_has_bias_False_strided_False_contiguous_True_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow3d_cuda_has_bias_False_strided_True_contiguous_False_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow3d_cuda_has_bias_False_strided_True_contiguous_True_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow3d_cuda_has_bias_True_strided_False_contiguous_False_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow3d_cuda_has_bias_True_strided_False_contiguous_True_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow3d_cuda_has_bias_True_strided_True_contiguous_False_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow3d_cuda_has_bias_True_strided_True_contiguous_True_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow3d_dilated_has_bias_False_strided_False_contiguous_False_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow3d_dilated_has_bias_False_strided_False_contiguous_True_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow3d_dilated_has_bias_False_strided_True_contiguous_False_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow3d_dilated_has_bias_False_strided_True_contiguous_True_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow3d_dilated_has_bias_True_strided_False_contiguous_False_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow3d_dilated_has_bias_True_strided_False_contiguous_True_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow3d_dilated_has_bias_True_strided_True_contiguous_False_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_backend_slow3d_dilated_has_bias_True_strided_True_contiguous_True_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_contiguous_for_oneDNN_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_cudnn_mismatch_memory_format_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_cudnn_ndhwc_cuda_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_cudnn_ndhwc_cuda_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_cudnn_nhwc_cuda_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_cudnn_nhwc_cuda_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_cudnn_nhwc_cuda_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_cudnn_nhwc_support_cuda_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_cudnn_nhwc_support_cuda_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_double_backward_cuda_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_double_backward_groups_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_double_backward_no_bias_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_double_backward_stride_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_double_backward_strided_with_3D_input_and_weight_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_empty_channel_cuda_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_empty_channel_cuda_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_ic1_channels_last_for_oneDNN_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_large_batch_1_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_large_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_large_nosplit_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_noncontig_weights_and_bias_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_noncontig_weights_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_thnn_nhwc_cuda_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_thnn_nhwc_cuda_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_transpose_with_output_size_and_no_batch_dim_ConvTranspose2d_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_transpose_with_output_size_and_no_batch_dim_ConvTranspose3d_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_conv_transposed_large_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_convert_conv2d_weight_memory_format_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_convert_conv3d_weight_memory_format_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_cudnn_convolution_add_relu_cuda_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_cudnn_convolution_add_relu_cuda_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_cudnn_convolution_relu_cuda_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_cudnn_convolution_relu_cuda_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_depthwise_conv_64bit_indexing_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_group_convTranspose_empty_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_group_conv_empty_cuda, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_noncontig_conv_grad_cuda_bfloat16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_noncontig_conv_grad_cuda_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_noncontig_conv_grad_cuda_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCUDA::test_noncontig_conv_grad_cuda_float64 2025-07-17T09:58:16.0333398Z 2025-07-17T09:58:16.0333550Z Running nn/test_pooling 1/1 ... [2025-07-17 09:58:15.992071] 2025-07-17T09:58:16.0333814Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T09:58:16.0334440Z Executing ['/opt/conda/envs/py_3.12/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 09:58:15.992379] 2025-07-17T09:58:39.0556282Z 2025-07-17T09:58:39.0557331Z nn/test_pooling 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_pooling_1.1_24e84411c9da7557_.log 2025-07-17T09:58:39.0588042Z Running 138 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::TestPoolingNNDeviceTypeCUDA::test_AdaptiveMaxPool1d_indices_cuda_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_AdaptiveMaxPool1d_indices_cuda_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_AdaptiveMaxPool1d_indices_cuda_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_AdaptiveMaxPool1d_indices_cuda_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_AdaptiveMaxPool2d_indices_cuda_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_AdaptiveMaxPool2d_indices_cuda_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_AdaptiveMaxPool2d_indices_cuda_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_AdaptiveMaxPool2d_indices_cuda_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_AdaptiveMaxPool3d_indices_cuda_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_AdaptiveMaxPool3d_indices_cuda_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_AdaptiveMaxPool3d_indices_cuda_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_AdaptiveMaxPool3d_indices_cuda_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_AdaptiveMaxPool_zero_batch_dim_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_AvgPool2d_empty_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_AvgPool3d_backward_after_cat_dim1_device_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_FractionalMaxPool2d_zero_batch_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_FractionalMaxPool2d_zero_out_size_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_FractionalMaxPool2d_zero_samples_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_FractionalMaxPool3d_errors_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_FractionalMaxPool3d_zero_batch_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_FractionalMaxPool3d_zero_out_size_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_FractionalMaxPool3d_zero_samples_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_MaxPool1d_indices_cuda_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_MaxPool1d_indices_cuda_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_MaxPool1d_indices_cuda_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_MaxPool1d_indices_cuda_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_MaxPool2d_indices_cuda_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_MaxPool2d_indices_cuda_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_MaxPool2d_indices_cuda_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_MaxPool2d_indices_cuda_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_MaxPool3d_indices_cuda_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_MaxPool3d_indices_cuda_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_MaxPool3d_indices_cuda_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_MaxPool3d_indices_cuda_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_MaxPool_zero_batch_dim_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_MaxUnpool_index_errors_case10_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_MaxUnpool_index_errors_case1_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_MaxUnpool_index_errors_case2_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_MaxUnpool_index_errors_case3_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_MaxUnpool_index_errors_case4_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_MaxUnpool_index_errors_case5_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_MaxUnpool_index_errors_case6_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_MaxUnpool_index_errors_case7_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_MaxUnpool_index_errors_case8_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_MaxUnpool_index_errors_case9_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_MaxUnpool_zero_batch_dim_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_adaptive_avg_pool2d_output_size_one_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_adaptive_avg_pool3d_output_size_one_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_adaptive_avg_pooling_backward_fails_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_adaptive_max_pooling_backward_fails_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_adaptive_pool_odd_size_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_adaptive_pooling_empty_output_size_cuda_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_adaptive_pooling_empty_output_size_cuda_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_adaptive_pooling_empty_output_size_cuda_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_adaptive_pooling_empty_output_size_cuda_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_adaptive_pooling_max_nhwc_cuda_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_adaptive_pooling_max_nhwc_cuda_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_adaptive_pooling_no_suppot_input_cuda_int16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_adaptive_pooling_no_suppot_input_cuda_int32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_adaptive_pooling_no_suppot_input_cuda_int64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_adaptive_pooling_no_suppot_input_cuda_int8, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_adaptive_pooling_no_suppot_input_cuda_uint8, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_adaptive_pooling_zero_batch_cuda_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_adaptive_pooling_zero_batch_cuda_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_avg_pool2d_nhwc_cuda_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_avg_pool2d_nhwc_cuda_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_avg_pool2d_nhwc_cuda_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_avg_pool2d_reduced_floating_cuda_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_avg_pool2d_reduced_floating_cuda_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_fractional_max_pool2d_backward_fails_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_fractional_max_pool2d_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_fractional_max_pool3d_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_fractional_max_pool_nan_inf_cuda_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_fractional_max_pool_nan_inf_cuda_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_fractional_max_pool_nan_inf_cuda_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_max_pool1d_corner_cases_cuda_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_max_pool1d_corner_cases_cuda_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_max_pool1d_cuda_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_max_pool1d_cuda_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_max_pool2d_corner_cases_cuda_int32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_max_pool2d_corner_cases_cuda_int64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_max_pool2d_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_max_pool2d_indices_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_max_pool2d_nhwc_cuda_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_max_pool2d_nhwc_cuda_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_max_pool2d_nhwc_cuda_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_max_pool2d_with_indices_backward_fails_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_max_pool3d_ndhwc_cuda_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_max_pool3d_ndhwc_cuda_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_max_pool3d_ndhwc_cuda_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_max_pool_bfloat16_half_cuda_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_max_pool_bfloat16_half_cuda_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_max_pool_nan_inf_cuda_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_max_pool_nan_inf_cuda_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_max_pool_nan_inf_cuda_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_maxpool3d_non_square_backward_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_maxpool_indices_no_batch_dim_cuda_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_maxpool_indices_no_batch_dim_cuda_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_maxpool_indices_no_batch_dim_cuda_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_maxpool_indices_no_batch_dim_cuda_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_pool3d_large_size_int64_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_pool3d_size_one_feature_dim_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_pool_invalid_size_cuda_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_pool_invalid_size_cuda_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_pool_invalid_size_cuda_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_pool_invalid_size_cuda_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_pool_large_size_cuda_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_pool_large_size_cuda_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_pool_large_size_cuda_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_pool_large_size_cuda_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_pooling_bfloat16_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_pooling_large_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_pooling_max_nhwc_cuda_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_pooling_max_nhwc_cuda_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_pooling_shape_cuda, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCUDA::test_pooling_zero_stride_cuda 2025-07-17T09:58:39.0617325Z 2025-07-17T09:58:39.0617462Z Running test_autocast 1/1 ... [2025-07-17 09:58:39.055704] 2025-07-17T09:58:39.0617810Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T09:58:39.0618519Z Executing ['/opt/conda/envs/py_3.12/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 09:58:39.056007] 2025-07-17T09:58:52.3004182Z 2025-07-17T09:58:52.3005786Z test_autocast 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_autocast_1.1_776416bb9814a33b_.log 2025-07-17T09:58:52.3009380Z 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 2025-07-17T09:58:52.3012728Z 2025-07-17T09:58:52.3012873Z Running test_autograd_fallback 1/1 ... [2025-07-17 09:58:52.300359] 2025-07-17T09:58:52.3013184Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T09:58:52.3013835Z Executing ['/opt/conda/envs/py_3.12/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 09:58:52.300668] 2025-07-17T09:58:55.9229925Z 2025-07-17T09:58:55.9230973Z test_autograd_fallback 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_autograd_fallback_1.1_14145de99f2f0bd4_.log 2025-07-17T09:58:55.9238508Z 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 2025-07-17T09:58:55.9245449Z 2025-07-17T09:58:55.9245589Z Running test_autoload_disable 1/1 ... [2025-07-17 09:58:55.922931] 2025-07-17T09:58:58.3296598Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T09:58:58.3297561Z import pkg_resources 2025-07-17T09:58:58.3584815Z /var/lib/jenkins/pytorch/test/cpp_extensions/cuda_extension.cpp -> /var/lib/jenkins/pytorch/test/cpp_extensions/cuda_extension.cpp [skipped, no changes] 2025-07-17T09:58:58.3588085Z /var/lib/jenkins/pytorch/test/cpp_extensions/cuda_extension_kernel.cu -> /var/lib/jenkins/pytorch/test/cpp_extensions/hip_extension_kernel.hip [ok] 2025-07-17T09:58:58.3591081Z /var/lib/jenkins/pytorch/test/cpp_extensions/cuda_extension_kernel2.cu -> /var/lib/jenkins/pytorch/test/cpp_extensions/hip_extension_kernel2.hip [ok] 2025-07-17T09:58:58.3591886Z Successfully preprocessed all matching files. 2025-07-17T09:58:58.3592199Z Total number of unsupported CUDA function calls: 0 2025-07-17T09:58:58.3592371Z 2025-07-17T09:58:58.3592375Z 2025-07-17T09:58:58.3592468Z Total number of replaced kernel launches: 2 2025-07-17T09:58:58.3607783Z /var/lib/jenkins/pytorch/test/cpp_extensions/torch_library.cu -> /var/lib/jenkins/pytorch/test/cpp_extensions/torch_library.cu [skipped, no changes] 2025-07-17T09:58:58.3608360Z Successfully preprocessed all matching files. 2025-07-17T09:58:58.3608656Z Total number of unsupported CUDA function calls: 0 2025-07-17T09:58:58.3608824Z 2025-07-17T09:58:58.3608827Z 2025-07-17T09:58:58.3608915Z Total number of replaced kernel launches: 0 2025-07-17T09:58:58.3966035Z running install 2025-07-17T09:58:58.3966634Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:90: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-07-17T09:58:58.3967151Z !! 2025-07-17T09:58:58.3967235Z 2025-07-17T09:58:58.3967334Z ******************************************************************************** 2025-07-17T09:58:58.3967592Z Please avoid running ``setup.py`` directly. 2025-07-17T09:58:58.3967856Z Instead, use pypa/build, pypa/installer or other 2025-07-17T09:58:58.3968760Z standards-based tools. 2025-07-17T09:58:58.3968892Z 2025-07-17T09:58:58.3969049Z By 2025-Oct-31, you need to update your project and remove deprecated calls 2025-07-17T09:58:58.3969501Z or your builds will no longer be supported. 2025-07-17T09:58:58.3969772Z 2025-07-17T09:58:58.3969978Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-07-17T09:58:58.3970315Z ******************************************************************************** 2025-07-17T09:58:58.3970473Z 2025-07-17T09:58:58.3970548Z !! 2025-07-17T09:58:58.3970714Z self.initialize_options() 2025-07-17T09:58:58.4076506Z running build 2025-07-17T09:58:58.4076732Z running build_py 2025-07-17T09:58:58.4150153Z creating build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension 2025-07-17T09:58:58.4152655Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension 2025-07-17T09:58:58.4156288Z running build_ext 2025-07-17T09:58:58.4170716Z building 'torch_test_cpp_extension.cpp' extension 2025-07-17T09:58:58.4172116Z creating build/temp.linux-x86_64-cpython-312 2025-07-17T09:58:58.4175690Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c extension.cpp -o build/temp.linux-x86_64-cpython-312/extension.o -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1016\" -DTORCH_EXTENSION_NAME=cpp -std=c++17 2025-07-17T09:59:19.2411901Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/Exceptions.h:12, 2025-07-17T09:59:19.2413503Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11, 2025-07-17T09:59:19.2415310Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:9, 2025-07-17T09:59:19.2415998Z from extension.cpp:1: 2025-07-17T09:59:19.2417281Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_’: 2025-07-17T09:59:19.2417820Z extension.cpp:45:53: required from here 2025-07-17T09:59:19.2421978Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h:1539:7: warning: ‘pybind11::class_’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes] 2025-07-17T09:59:19.2422984Z 1539 | class class_ : public detail::generic_type { 2025-07-17T09:59:19.2423269Z | ^~~~~~ 2025-07-17T09:59:19.2424542Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]’: 2025-07-17T09:59:19.2425792Z extension.cpp:45:53: required from here 2025-07-17T09:59:19.2427503Z /opt/conda/envs/py_3.12/lib/python3.12/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] 2025-07-17T09:59:19.2428855Z 1599 | with_internals([&](internals &internals) { 2025-07-17T09:59:19.2429674Z | ^~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T09:59:19.2430075Z 1600 | auto &instances = record.module_local ? get_local_internals().registered_types_cpp 2025-07-17T09:59:19.2430693Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T09:59:19.2431119Z 1601 | : internals.registered_types_cpp; 2025-07-17T09:59:19.2431412Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T09:59:19.2431830Z 1602 | instances[std::type_index(typeid(type_alias))] 2025-07-17T09:59:19.2432093Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T09:59:19.2432351Z 1603 | = instances[std::type_index(typeid(type))]; 2025-07-17T09:59:19.2432597Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T09:59:19.2432815Z 1604 | }); 2025-07-17T09:59:19.2433004Z | ~ 2025-07-17T09:59:19.2496653Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cpp.cpython-312-x86_64-linux-gnu.so 2025-07-17T09:59:19.7579081Z building 'torch_test_cpp_extension.maia' extension 2025-07-17T09:59:19.7581179Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c maia_extension.cpp -o build/temp.linux-x86_64-cpython-312/maia_extension.o -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1016\" -DTORCH_EXTENSION_NAME=maia -std=c++17 2025-07-17T09:59:40.3050593Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/maia_extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/maia.cpython-312-x86_64-linux-gnu.so 2025-07-17T09:59:40.7985750Z building 'torch_test_cpp_extension.rng' extension 2025-07-17T09:59:40.7990635Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c rng_extension.cpp -o build/temp.linux-x86_64-cpython-312/rng_extension.o -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1016\" -DTORCH_EXTENSION_NAME=rng -std=c++17 2025-07-17T10:00:03.1296790Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-07-17T10:00:03.1297410Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-07-17T10:00:03.1298639Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-07-17T10:00:03.1299236Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-07-17T10:00:03.1299652Z from rng_extension.cpp:6: 2025-07-17T10:00:03.1300512Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1458: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-07-17T10:00:03.1301005Z 1458 | #pragma unroll 2025-07-17T10:00:03.1301221Z | 2025-07-17T10:00:03.1301552Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_convert.h:4, 2025-07-17T10:00:03.1302080Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1510, 2025-07-17T10:00:03.1302564Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-07-17T10:00:03.1303025Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-07-17T10:00:03.1303482Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-07-17T10:00:03.1304009Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-07-17T10:00:03.1304405Z from rng_extension.cpp:6: 2025-07-17T10:00:03.1304996Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:59: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-07-17T10:00:03.1305645Z 59 | #pragma unroll 2025-07-17T10:00:03.1305823Z | 2025-07-17T10:00:03.1306324Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:72: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-07-17T10:00:03.1306870Z 72 | #pragma unroll 2025-07-17T10:00:03.1307048Z | 2025-07-17T10:00:03.1307571Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:87: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-07-17T10:00:03.1308023Z 87 | #pragma unroll 2025-07-17T10:00:03.1308188Z | 2025-07-17T10:00:03.1308582Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1511, 2025-07-17T10:00:03.1309131Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-07-17T10:00:03.1309637Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-07-17T10:00:03.1310103Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-07-17T10:00:03.1310643Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-07-17T10:00:03.1311045Z from rng_extension.cpp:6: 2025-07-17T10:00:03.1311603Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_mask.h:160: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-07-17T10:00:03.1312237Z 160 | #pragma unroll 2025-07-17T10:00:03.1312412Z | 2025-07-17T10:00:03.1312737Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:00:03.1313379Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:00:03.1313961Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:00:03.1314428Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:00:03.1314941Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:00:03.1315434Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:00:03.1315965Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:00:03.1316708Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:00:03.1317527Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:00:03.1318405Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:00:03.1319266Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:00:03.1319927Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.1320344Z from rng_extension.cpp:1: 2025-07-17T10:00:03.1321260Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:00:03.1322226Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:00:03.1323365Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:00:03.1324336Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:00:03.1325839Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:00:03.1327360Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:00:03.1331145Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:00:03.1337807Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:00:03.1341135Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:00:03.1341707Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:00:03.1341972Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:00:03.1345568Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:00:03.1349242Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:00:03.1350323Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:00:03.1350603Z | ^~~~~~~~ 2025-07-17T10:00:03.1350980Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:00:03.1351639Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:00:03.1352545Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:00:03.1353008Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:00:03.1353516Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:00:03.1353996Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:00:03.1354910Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:00:03.1355749Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:00:03.1356525Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:00:03.1357153Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:00:03.1357723Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:00:03.1358257Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:00:03.1358734Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.1359069Z from rng_extension.cpp:1: 2025-07-17T10:00:03.1359603Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:00:03.1360051Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:00:03.1360277Z | ^~~~ 2025-07-17T10:00:03.1360629Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:00:03.1361122Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:00:03.1361575Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:00:03.1362017Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:00:03.1362446Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:00:03.1362920Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:00:03.1363444Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:00:03.1363940Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:00:03.1364436Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:00:03.1364966Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:00:03.1365589Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:00:03.1366068Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.1366393Z from rng_extension.cpp:1: 2025-07-17T10:00:03.1367111Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:00:03.1368004Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:00:03.1369019Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:00:03.1370017Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:00:03.1371057Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:00:03.1372200Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:00:03.1375553Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:00:03.1381219Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:00:03.1384498Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:00:03.1385093Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:00:03.1385662Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:00:03.1389463Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:00:03.1393028Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:00:03.1393748Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:00:03.1394013Z | ^~~~~~~~ 2025-07-17T10:00:03.1394389Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:00:03.1394952Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:00:03.1395462Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:00:03.1395934Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:00:03.1396369Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:00:03.1396840Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:00:03.1397433Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:00:03.1398066Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:00:03.1398705Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:00:03.1399337Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:00:03.1399995Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:00:03.1400535Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:00:03.1401113Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.1401507Z from rng_extension.cpp:1: 2025-07-17T10:00:03.1401997Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:00:03.1402478Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:00:03.1402770Z | ^~~~ 2025-07-17T10:00:03.1403127Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:00:03.1403622Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:00:03.1404089Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:00:03.1404542Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:00:03.1404978Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:00:03.1405456Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:00:03.1405983Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:00:03.1406476Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:00:03.1406965Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:00:03.1407495Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:00:03.1408033Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:00:03.1408518Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.1408849Z from rng_extension.cpp:1: 2025-07-17T10:00:03.1409445Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:00:03.1410322Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:00:03.1411286Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:00:03.1412260Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:00:03.1413322Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:00:03.1414430Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:00:03.1417707Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:00:03.1435619Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:00:03.1438780Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:00:03.1439326Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:00:03.1451372Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:00:03.1454785Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:00:03.1458331Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:00:03.1459190Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:00:03.1459533Z | ^~~~~~~~ 2025-07-17T10:00:03.1459927Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:00:03.1460563Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:00:03.1461081Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:00:03.1461560Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:00:03.1461998Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:00:03.1462489Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:00:03.1463075Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:00:03.1463719Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:00:03.1464361Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:00:03.1464981Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:00:03.1465622Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:00:03.1466156Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:00:03.1466638Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.1466983Z from rng_extension.cpp:1: 2025-07-17T10:00:03.1467501Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:00:03.1467949Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:00:03.1468188Z | ^~~~ 2025-07-17T10:00:03.1468535Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:00:03.1469033Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:00:03.1469492Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:00:03.1469939Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:00:03.1470372Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:00:03.1470850Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:00:03.1471364Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:00:03.1471859Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:00:03.1472435Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:00:03.1472966Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:00:03.1473575Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:00:03.1474122Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.1474453Z from rng_extension.cpp:1: 2025-07-17T10:00:03.1475112Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:00:03.1476012Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:00:03.1476974Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:00:03.1477950Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:00:03.1478988Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:00:03.1480091Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:00:03.1483266Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:00:03.1488732Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:00:03.1491952Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:00:03.1492492Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:00:03.1492837Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:00:03.1496047Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:00:03.1499391Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:00:03.1500138Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:00:03.1500400Z | ^~~~~~~~ 2025-07-17T10:00:03.1500777Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:00:03.1501352Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:00:03.1501848Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:00:03.1502322Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:00:03.1502757Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:00:03.1503233Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:00:03.1503830Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:00:03.1504471Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:00:03.1505105Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:00:03.1505842Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:00:03.1506505Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:00:03.1507038Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:00:03.1507686Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.1508021Z from rng_extension.cpp:1: 2025-07-17T10:00:03.1508524Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:00:03.1509056Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:00:03.1509286Z | ^~~~ 2025-07-17T10:00:03.1509629Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:00:03.1510120Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:00:03.1510580Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:00:03.1511024Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:00:03.1511456Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:00:03.1511934Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:00:03.1512448Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:00:03.1512943Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:00:03.1513431Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:00:03.1513950Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:00:03.1514488Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:00:03.1514967Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.1515294Z from rng_extension.cpp:1: 2025-07-17T10:00:03.1515872Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:00:03.1516772Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:00:03.1517734Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:00:03.1518707Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:00:03.1519748Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:00:03.1520874Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:00:03.1524327Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:00:03.1530077Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:00:03.1533253Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:00:03.1533785Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:00:03.1534040Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:00:03.1537565Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:00:03.1541123Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:00:03.1541926Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:00:03.1542249Z | ^~~~~~~~ 2025-07-17T10:00:03.1542649Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:00:03.1543213Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:00:03.1543719Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:00:03.1544164Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:00:03.1544592Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:00:03.1545067Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:00:03.1545788Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:00:03.1546420Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:00:03.1547057Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:00:03.1547669Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:00:03.1548227Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:00:03.1548767Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:00:03.1549238Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.1549567Z from rng_extension.cpp:1: 2025-07-17T10:00:03.1550071Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:00:03.1550511Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:00:03.1550745Z | ^~~~ 2025-07-17T10:00:03.1551098Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:00:03.1551589Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:00:03.1552056Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:00:03.1552496Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:00:03.1552951Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:00:03.1553420Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:00:03.1554018Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:00:03.1554527Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:00:03.1555014Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:00:03.1555673Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:00:03.1556206Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:00:03.1556761Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.1557094Z from rng_extension.cpp:1: 2025-07-17T10:00:03.1557685Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:00:03.1558568Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:00:03.1559518Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:00:03.1560490Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:00:03.1561514Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:00:03.1562622Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:00:03.1565787Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:00:03.1571130Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:00:03.1574385Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:00:03.1574915Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:00:03.1575149Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:00:03.1578388Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:00:03.1581707Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:00:03.1582429Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:00:03.1582685Z | ^~~~~~~~ 2025-07-17T10:00:03.1583038Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:00:03.1583615Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:00:03.1584095Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:00:03.1584541Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:00:03.1584963Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:00:03.1585483Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:00:03.1586072Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:00:03.1586697Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:00:03.1587405Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:00:03.1588031Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:00:03.1588748Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:00:03.1589279Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:00:03.1589823Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.1590159Z from rng_extension.cpp:1: 2025-07-17T10:00:03.1590658Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:00:03.1591092Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:00:03.1591317Z | ^~~~ 2025-07-17T10:00:03.1591662Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:00:03.1592147Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:00:03.1592603Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:00:03.1593041Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:00:03.1593467Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:00:03.1593940Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:00:03.1594451Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:00:03.1594941Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:00:03.1595428Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:00:03.1595953Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:00:03.1596491Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:00:03.1596962Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.1597287Z from rng_extension.cpp:1: 2025-07-17T10:00:03.1597870Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:00:03.1598745Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:00:03.1599700Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:00:03.1600673Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:00:03.1601686Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:00:03.1602890Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:00:03.1606298Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:00:03.1611610Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:00:03.1614654Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:00:03.1615187Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:00:03.1615439Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:00:03.1618766Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:00:03.1622266Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:00:03.1623042Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:00:03.1623307Z | ^~~~~~~~ 2025-07-17T10:00:03.1623678Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:00:03.1624245Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:00:03.1624734Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:00:03.1625200Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:00:03.1625749Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:00:03.1626221Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:00:03.1626805Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:00:03.1627430Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:00:03.1628056Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:00:03.1628668Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:00:03.1629231Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:00:03.1629753Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:00:03.1630226Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.1630550Z from rng_extension.cpp:1: 2025-07-17T10:00:03.1631045Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:00:03.1631486Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:00:03.1631713Z | ^~~~ 2025-07-17T10:00:03.1632057Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:00:03.1632544Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:00:03.1632996Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:00:03.1633436Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:00:03.1633871Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:00:03.1634345Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:00:03.1634962Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:00:03.1635475Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:00:03.1636101Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:00:03.1636624Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:00:03.1637232Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:00:03.1637712Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.1638035Z from rng_extension.cpp:1: 2025-07-17T10:00:03.1638604Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:00:03.1639504Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:00:03.1640460Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:00:03.1641431Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:00:03.1642462Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:00:03.1643570Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:00:03.1646912Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:00:03.1652675Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:00:03.1656024Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:00:03.1656560Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:00:03.1656814Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:00:03.1660169Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:00:03.1663658Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:00:03.1664373Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:00:03.1664634Z | ^~~~~~~~ 2025-07-17T10:00:03.1665008Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:00:03.1665713Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:00:03.1666229Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:00:03.1666679Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:00:03.1667127Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:00:03.1667605Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:00:03.1668327Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:00:03.1668986Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:00:03.1669696Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:00:03.1670384Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:00:03.1671013Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:00:03.1671547Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:00:03.1672021Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.1672353Z from rng_extension.cpp:1: 2025-07-17T10:00:03.1672846Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:00:03.1673325Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:00:03.1673552Z | ^~~~ 2025-07-17T10:00:03.1673894Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:00:03.1674370Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:00:03.1674827Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:00:03.1675267Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:00:03.1675696Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:00:03.1676167Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:00:03.1676682Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:00:03.1677180Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:00:03.1677661Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:00:03.1678187Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:00:03.1678721Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:00:03.1679193Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.1679518Z from rng_extension.cpp:1: 2025-07-17T10:00:03.1680096Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:00:03.1680985Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:00:03.1681949Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:00:03.1682996Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:00:03.1684043Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:00:03.1685304Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:00:03.1688585Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:00:03.1693899Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:00:03.1696962Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:00:03.1697490Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:00:03.1697740Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:00:03.1700996Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:00:03.1704536Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:00:03.1705264Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:00:03.1705596Z | ^~~~~~~~ 2025-07-17T10:00:03.1705961Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:00:03.1706535Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:00:03.1707040Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:00:03.1707505Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:00:03.1707941Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:00:03.1708409Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:00:03.1708995Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:00:03.1709627Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:00:03.1710261Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:00:03.1710876Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:00:03.1711432Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:00:03.1711948Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:00:03.1712418Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.1712746Z from rng_extension.cpp:1: 2025-07-17T10:00:03.1713231Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:00:03.1713666Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:00:03.1713890Z | ^~~~ 2025-07-17T10:00:03.1714228Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:00:03.1714711Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:00:03.1715168Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:00:03.1715606Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:00:03.1716123Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:00:03.1716595Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:00:03.1717247Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:00:03.1717734Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:00:03.1718292Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:00:03.1718819Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:00:03.1719343Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:00:03.1719809Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.1720116Z from rng_extension.cpp:1: 2025-07-17T10:00:03.1720695Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:00:03.1721568Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:00:03.1722529Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:00:03.1723523Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:00:03.1724546Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:00:03.1725649Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:00:03.1728852Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:00:03.1734328Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:00:03.1737570Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:00:03.1738117Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:00:03.1738361Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:00:03.1741629Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:00:03.1745001Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:00:03.1746029Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:00:03.1746291Z | ^~~~~~~~ 2025-07-17T10:00:03.1746657Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:00:03.1747222Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:00:03.1747721Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:00:03.1748175Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:00:03.1748605Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:00:03.1749074Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:00:03.1749761Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:00:03.1750408Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:00:03.1751218Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:00:03.1751857Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:00:03.1752489Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:00:03.1753055Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:00:03.1753527Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.1753870Z from rng_extension.cpp:1: 2025-07-17T10:00:03.1754378Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:00:03.1754830Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:00:03.1755069Z | ^~~~ 2025-07-17T10:00:03.1755422Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:00:03.1755921Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:00:03.1756385Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:00:03.1756857Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:00:03.1757282Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:00:03.1757757Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:00:03.1758272Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:00:03.1758761Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:00:03.1759261Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:00:03.1759818Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:00:03.1760378Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:00:03.1760843Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.1761176Z from rng_extension.cpp:1: 2025-07-17T10:00:03.1761789Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:00:03.1762697Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:00:03.1763663Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:00:03.1764712Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:00:03.1765773Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:00:03.1767039Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:00:03.1770258Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:00:03.1775622Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:00:03.1778642Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:00:03.1779198Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:00:03.1779490Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:00:03.1782838Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:00:03.1786359Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:00:03.1787072Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:00:03.1787351Z | ^~~~~~~~ 2025-07-17T10:00:03.1787723Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:00:03.1788287Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:00:03.1788777Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:00:03.1789230Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:00:03.1789653Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:00:03.1790139Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:00:03.1790724Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:00:03.1791351Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:00:03.1791975Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:00:03.1792585Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:00:03.1793142Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:00:03.1793658Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:00:03.1794128Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.1794446Z from rng_extension.cpp:1: 2025-07-17T10:00:03.1794928Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:00:03.1795350Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:00:03.1795571Z | ^~~~ 2025-07-17T10:00:03.1795914Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:00:03.1796387Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:00:03.1796828Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:00:03.1797374Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:00:03.1797800Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:00:03.1798259Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:00:03.1798909Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:00:03.1799410Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:00:03.1799957Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:00:03.1800478Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:00:03.1801041Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:00:03.1801508Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.1801827Z from rng_extension.cpp:1: 2025-07-17T10:00:03.1802424Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:00:03.1803295Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:00:03.1804237Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:00:03.1805207Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:00:03.1806215Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:00:03.1807319Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:00:03.1810508Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:00:03.1816093Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:00:03.1819238Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:00:03.1819783Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:00:03.1820040Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:00:03.1823286Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:00:03.1826688Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:00:03.1827397Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:00:03.1827658Z | ^~~~~~~~ 2025-07-17T10:00:03.1828020Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:00:03.1828663Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:00:03.1829154Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:00:03.1829609Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:00:03.1830034Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:00:03.1830566Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:00:03.1831157Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:00:03.1831789Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:00:03.1832482Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:00:03.1833192Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:00:03.1833755Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:00:03.1834274Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:00:03.1834749Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.1835069Z from rng_extension.cpp:1: 2025-07-17T10:00:03.1835556Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:00:03.1835983Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:00:03.1836196Z | ^~~~ 2025-07-17T10:00:03.1836532Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:00:03.1837009Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:00:03.1837462Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:00:03.1837900Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:00:03.1838314Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:00:03.1838788Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:00:03.1839297Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:00:03.1839777Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:00:03.1840260Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:00:03.1840772Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:00:03.1841302Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:00:03.1841768Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.1842169Z from rng_extension.cpp:1: 2025-07-17T10:00:03.1842749Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:00:03.1843628Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:00:03.1844570Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:00:03.1845600Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:00:03.1846649Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:00:03.1847872Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:00:03.1851238Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:00:03.1856849Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:00:03.1860118Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:00:03.1860641Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:00:03.1860888Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:00:03.1864417Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:00:03.1868062Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:00:03.1868782Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:00:03.1869058Z | ^~~~~~~~ 2025-07-17T10:00:03.1869432Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:00:03.1869998Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:00:03.1870492Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:00:03.1870949Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:00:03.1871390Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:00:03.1871867Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:00:03.1872472Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:00:03.1873104Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:00:03.1873738Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:00:03.1874357Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:00:03.1874919Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:00:03.1875438Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:00:03.1876019Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.1876344Z from rng_extension.cpp:1: 2025-07-17T10:00:03.1876844Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:00:03.1877273Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:00:03.1877487Z | ^~~~ 2025-07-17T10:00:03.1877824Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:00:03.1878394Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:00:03.1878851Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:00:03.1879295Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:00:03.1879810Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:00:03.1880293Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:00:03.1880899Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:00:03.1881399Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:00:03.1881891Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:00:03.1882425Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:00:03.1882960Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:00:03.1883436Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.1883758Z from rng_extension.cpp:1: 2025-07-17T10:00:03.1884346Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:00:03.1885225Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:00:03.1886188Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:00:03.1887155Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:00:03.1888202Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:00:03.1889318Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:00:03.1892789Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:00:03.1898631Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:00:03.1901928Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:00:03.1902465Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:00:03.1902715Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:00:03.1906289Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:00:03.1909913Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:00:03.1910638Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:00:03.1910901Z | ^~~~~~~~ 2025-07-17T10:00:03.1911281Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:00:03.1911909Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:00:03.1912421Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:00:03.1912889Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:00:03.1913394Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:00:03.1913870Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:00:03.1914517Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:00:03.1915152Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:00:03.1915804Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:00:03.1916422Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:00:03.1916988Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:00:03.1917515Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:00:03.1917997Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.1918325Z from rng_extension.cpp:1: 2025-07-17T10:00:03.1918813Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:00:03.1919249Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:00:03.1919475Z | ^~~~ 2025-07-17T10:00:03.1919823Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:00:03.1920317Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:00:03.1920762Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:00:03.1921210Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:00:03.1921643Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:00:03.1922117Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:00:03.1922642Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:00:03.1923142Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:00:03.1923715Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:00:03.1924247Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:00:03.1924787Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:00:03.1925275Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.1925600Z from rng_extension.cpp:1: 2025-07-17T10:00:03.1926267Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:00:03.1927162Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:00:03.1928173Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:00:03.1929221Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:00:03.1930267Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:00:03.1931380Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:00:03.1934815Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:00:03.1940529Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:00:03.1943923Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:00:03.1944456Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:00:03.1944706Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:00:03.1948416Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:00:03.1951986Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:00:03.1952701Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:00:03.1952965Z | ^~~~~~~~ 2025-07-17T10:00:03.1953351Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:00:03.1953922Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:00:03.1954419Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:00:03.1954890Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:00:03.1955318Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:00:03.1955788Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:00:03.1956375Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:00:03.1957005Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:00:03.1957745Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:00:03.1958374Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:00:03.1958939Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:00:03.1959467Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:00:03.1960012Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.1960351Z from rng_extension.cpp:1: 2025-07-17T10:00:03.1960839Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:00:03.1961344Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:00:03.1961573Z | ^~~~ 2025-07-17T10:00:03.1961922Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:00:03.1962471Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:00:03.1962935Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:00:03.1963381Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:00:03.1963802Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:00:03.1964283Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:00:03.1964803Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:00:03.1965294Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:00:03.1965779Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:00:03.1966305Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:00:03.1966840Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:00:03.1967307Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.1967624Z from rng_extension.cpp:1: 2025-07-17T10:00:03.1968210Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:00:03.1969089Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:00:03.1970039Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:00:03.1971008Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:00:03.1972052Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:00:03.1973259Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:00:03.1976752Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:00:03.1982527Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:00:03.1985861Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:00:03.1986401Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:00:03.1986653Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:00:03.1990180Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:00:03.1993817Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:00:03.1994642Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:00:03.1994909Z | ^~~~~~~~ 2025-07-17T10:00:03.1995274Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:00:03.1995901Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:00:03.1996404Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:00:03.1996867Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:00:03.1997296Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:00:03.1997770Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:00:03.1998364Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:00:03.1999007Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:00:03.1999636Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:00:03.2000257Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:00:03.2000815Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:00:03.2001343Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:00:03.2001819Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.2002148Z from rng_extension.cpp:1: 2025-07-17T10:00:03.2002638Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:00:03.2003068Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:00:03.2003297Z | ^~~~ 2025-07-17T10:00:03.2003639Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:00:03.2004121Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:00:03.2004575Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:00:03.2005092Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:00:03.2005521Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:00:03.2005992Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:00:03.2006500Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:00:03.2006992Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:00:03.2007535Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:00:03.2008074Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:00:03.2008662Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:00:03.2009127Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.2009444Z from rng_extension.cpp:1: 2025-07-17T10:00:03.2010081Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:00:03.2010971Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:00:03.2011919Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:00:03.2012907Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:00:03.2013933Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:00:03.2015042Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:00:03.2018418Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:00:03.2024248Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:00:03.2027648Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:00:03.2028182Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:00:03.2028422Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:00:03.2031912Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:00:03.2035433Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:00:03.2036134Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:00:03.2036391Z | ^~~~~~~~ 2025-07-17T10:00:03.2036935Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:00:03.2037616Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:00:03.2038175Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:00:03.2039029Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:00:03.2039560Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:00:03.2040142Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:00:03.2040855Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:00:03.2051175Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:00:03.2051855Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:00:03.2052467Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:00:03.2053091Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:00:03.2053671Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:00:03.2054136Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.2054446Z from rng_extension.cpp:1: 2025-07-17T10:00:03.2054993Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:00:03.2055420Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:00:03.2055636Z | ^~~~ 2025-07-17T10:00:03.2055971Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:00:03.2056446Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:00:03.2056889Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:00:03.2057315Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:00:03.2057732Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:00:03.2058203Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:00:03.2058706Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:00:03.2059184Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:00:03.2059654Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:00:03.2060155Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:00:03.2060675Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:00:03.2061129Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.2061443Z from rng_extension.cpp:1: 2025-07-17T10:00:03.2062041Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:00:03.2062987Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:00:03.2063928Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:00:03.2064888Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:00:03.2066061Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:00:03.2067209Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:00:03.2070770Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:00:03.2076560Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:00:03.2079856Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:00:03.2080481Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:00:03.2080734Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:00:03.2084325Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:00:03.2087978Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:00:03.2088688Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:00:03.2088954Z | ^~~~~~~~ 2025-07-17T10:00:03.2089341Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:00:03.2089913Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:00:03.2090421Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:00:03.2090887Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:00:03.2091315Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:00:03.2091791Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:00:03.2092370Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:00:03.2093026Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:00:03.2093669Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:00:03.2094294Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:00:03.2094854Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:00:03.2095386Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:00:03.2095860Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.2096264Z from rng_extension.cpp:1: 2025-07-17T10:00:03.2096762Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:00:03.2097200Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:00:03.2097429Z | ^~~~ 2025-07-17T10:00:03.2097781Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:00:03.2098267Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:00:03.2098724Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:00:03.2099235Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:00:03.2099671Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:00:03.2100154Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:00:03.2100733Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:00:03.2101301Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:00:03.2101796Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:00:03.2102321Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:00:03.2102854Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:00:03.2103332Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.2103655Z from rng_extension.cpp:1: 2025-07-17T10:00:03.2104242Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:00:03.2105126Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:00:03.2106148Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:00:03.2107126Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:00:03.2108158Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:00:03.2109280Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:00:03.2112678Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:00:03.2118600Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:00:03.2121941Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:00:03.2122479Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:00:03.2122729Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:00:03.2126198Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:00:03.2129727Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:00:03.2130506Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:00:03.2130767Z | ^~~~~~~~ 2025-07-17T10:00:03.2131202Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:00:03.2131771Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:00:03.2132269Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:00:03.2132785Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:00:03.2133215Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:00:03.2133747Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:00:03.2134340Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:00:03.2134968Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:00:03.2135619Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:00:03.2136233Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:00:03.2136792Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:00:03.2137322Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:00:03.2137796Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.2138121Z from rng_extension.cpp:1: 2025-07-17T10:00:03.2138619Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:00:03.2139057Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:00:03.2139279Z | ^~~~ 2025-07-17T10:00:03.2139628Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:00:03.2140108Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:00:03.2140568Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:00:03.2141012Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:00:03.2141434Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:00:03.2141915Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:00:03.2142423Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:00:03.2142985Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:00:03.2143478Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:00:03.2144060Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:00:03.2144669Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:00:03.2145163Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.2145616Z from rng_extension.cpp:1: 2025-07-17T10:00:03.2146221Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:00:03.2147097Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:00:03.2148043Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:00:03.2149017Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:00:03.2150046Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:00:03.2151155Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:00:03.2154562Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:00:03.2160390Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:00:03.2163788Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:00:03.2164330Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:00:03.2164587Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:00:03.2168043Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:00:03.2171617Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:00:03.2172329Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:00:03.2172593Z | ^~~~~~~~ 2025-07-17T10:00:03.2172962Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:00:03.2173520Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:00:03.2174011Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:00:03.2174468Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:00:03.2174903Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:00:03.2175380Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:00:03.2175965Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:00:03.2176661Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:00:03.2177301Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:00:03.2178045Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:00:03.2178602Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:00:03.2179187Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:00:03.2179670Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.2180000Z from rng_extension.cpp:1: 2025-07-17T10:00:03.2180490Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:00:03.2180922Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:00:03.2181144Z | ^~~~ 2025-07-17T10:00:03.2181484Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:00:03.2181962Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:00:03.2182411Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:00:03.2182852Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:00:03.2183267Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:00:03.2183751Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:00:03.2184261Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:00:03.2184747Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:00:03.2185226Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:00:03.2185826Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:00:03.2186360Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:00:03.2186836Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.2187167Z from rng_extension.cpp:1: 2025-07-17T10:00:03.2187746Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:00:03.2188623Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:00:03.2189576Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:00:03.2190552Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:00:03.2191644Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:00:03.2192839Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:00:03.2196385Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:00:03.2202111Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:00:03.2205374Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:00:03.2205908Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:00:03.2206142Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:00:03.2209689Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:00:03.2213326Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:00:03.2214026Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:00:03.2214287Z | ^~~~~~~~ 2025-07-17T10:00:03.2214661Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:00:03.2215213Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:00:03.2215706Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:00:03.2216152Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:00:03.2216567Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:00:03.2217026Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:00:03.2217609Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:00:03.2218251Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:00:03.2218869Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:00:03.2219482Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:00:03.2220041Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:00:03.2220572Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:00:03.2221043Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.2221359Z from rng_extension.cpp:1: 2025-07-17T10:00:03.2221840Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:00:03.2222264Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:00:03.2222489Z | ^~~~ 2025-07-17T10:00:03.2222840Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:00:03.2223315Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:00:03.2223826Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:00:03.2224265Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:00:03.2224678Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:00:03.2225260Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:00:03.2225830Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:00:03.2226379Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:00:03.2226867Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:00:03.2227377Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:00:03.2227902Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:00:03.2228373Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.2228682Z from rng_extension.cpp:1: 2025-07-17T10:00:03.2229258Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:00:03.2230142Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:00:03.2231096Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:00:03.2232040Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:00:03.2233066Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:00:03.2234174Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:00:03.2237358Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:00:03.2242774Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:00:03.2245964Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:00:03.2246489Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:00:03.2246732Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:00:03.2249956Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:00:03.2253294Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:00:03.2253999Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:00:03.2254255Z | ^~~~~~~~ 2025-07-17T10:00:03.2254617Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:00:03.2255167Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:00:03.2255662Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:00:03.2256110Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:00:03.2256529Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:00:03.2257057Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:00:03.2257653Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:00:03.2258402Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:00:03.2259023Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:00:03.2259682Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:00:03.2260245Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:00:03.2260760Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:00:03.2261223Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.2261542Z from rng_extension.cpp:1: 2025-07-17T10:00:03.2262031Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:00:03.2262451Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:00:03.2262668Z | ^~~~ 2025-07-17T10:00:03.2263012Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:00:03.2263489Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:00:03.2263691Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:00:03.2263868Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:00:03.2264050Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:00:03.2264281Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:00:03.2264496Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:00:03.2264706Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:00:03.2264913Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:00:03.2265153Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:00:03.2265487Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:00:03.2265663Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.2265745Z from rng_extension.cpp:1: 2025-07-17T10:00:03.2266182Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:00:03.2266671Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:00:03.2267285Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:00:03.2267817Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:00:03.2268534Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:00:03.2269200Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:00:03.2272086Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:00:03.2275313Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:00:03.2275799Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:00:03.2275909Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:00:03.2275999Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:00:03.2279473Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:00:03.2280315Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:00:03.2280457Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:00:03.2280522Z | ^~~~~~~~ 2025-07-17T10:00:03.2280799Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:00:03.2281033Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:00:03.2281240Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:00:03.2281431Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:00:03.2281607Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:00:03.2281842Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:00:03.2282139Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:00:03.2282410Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:00:03.2282695Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:00:03.2282952Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:00:03.2283188Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:00:03.2283414Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:00:03.2283597Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.2283681Z from rng_extension.cpp:1: 2025-07-17T10:00:03.2284031Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:00:03.2284127Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:00:03.2284195Z | ^~~~ 2025-07-17T10:00:03.2284472Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:00:03.2284675Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:00:03.2284976Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:00:03.2285163Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:00:03.2285389Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:00:03.2285634Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:00:03.2285850Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:00:03.2286069Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:00:03.2286272Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:00:03.2286520Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:00:03.2286745Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:00:03.2286938Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.2287016Z from rng_extension.cpp:1: 2025-07-17T10:00:03.2287457Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:00:03.2287957Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:00:03.2288474Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:00:03.2288982Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:00:03.2289554Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:00:03.2290159Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:00:03.2293104Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:00:03.2296533Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:00:03.2297002Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:00:03.2297121Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:00:03.2297204Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:00:03.2300546Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:00:03.2301271Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:00:03.2301406Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:00:03.2301469Z | ^~~~~~~~ 2025-07-17T10:00:03.2301807Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:00:03.2302049Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:00:03.2302365Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:00:03.2302558Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:00:03.2302790Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:00:03.2303030Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:00:03.2303327Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:00:03.2303603Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:00:03.2303889Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:00:03.2304155Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:00:03.2304390Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:00:03.2304612Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:00:03.2304798Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.2304882Z from rng_extension.cpp:1: 2025-07-17T10:00:03.2305228Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:00:03.2305403Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:00:03.2305482Z | ^~~~ 2025-07-17T10:00:03.2305695Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:00:03.2305890Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:00:03.2306081Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:00:03.2306261Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:00:03.2306437Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:00:03.2306667Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:00:03.2306884Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:00:03.2307102Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:00:03.2307309Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:00:03.2307562Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:00:03.2307783Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:00:03.2308042Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.2308119Z from rng_extension.cpp:1: 2025-07-17T10:00:03.2308559Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:00:03.2309167Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:00:03.2309756Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:00:03.2310271Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:00:03.2310836Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:00:03.2311452Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:00:03.2314319Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:00:03.2317564Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:00:03.2318037Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:00:03.2318254Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:00:03.2318332Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:00:03.2321744Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:00:03.2322471Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:00:03.2322601Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:00:03.2322677Z | ^~~~~~~~ 2025-07-17T10:00:03.2322938Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:00:03.2323179Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:00:03.2323379Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:00:03.2323582Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:00:03.2323749Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:00:03.2323988Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:00:03.2324276Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:00:03.2324563Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:00:03.2324851Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:00:03.2325117Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:00:03.2325467Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:00:03.2325703Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:00:03.2325951Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.2326113Z from rng_extension.cpp:1: 2025-07-17T10:00:03.2326447Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:00:03.2326548Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:00:03.2326669Z | ^~~~ 2025-07-17T10:00:03.2326900Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:00:03.2327096Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:00:03.2327296Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:00:03.2327477Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:00:03.2327663Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:00:03.2327891Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:00:03.2328112Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:00:03.2328320Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:00:03.2328534Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:00:03.2328779Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:00:03.2329007Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:00:03.2329195Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.2329272Z from rng_extension.cpp:1: 2025-07-17T10:00:03.2329716Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:00:03.2330199Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:00:03.2330727Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:00:03.2331225Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:00:03.2331806Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:00:03.2332402Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:00:03.2335174Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:00:03.2338284Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:00:03.2338756Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:00:03.2338858Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:00:03.2338940Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:00:03.2342059Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:00:03.2342822Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:00:03.2343010Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:00:03.2343123Z | ^~~~~~~~ 2025-07-17T10:00:03.2343393Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:00:03.2343679Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:00:03.2343885Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:00:03.2344074Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:00:03.2344268Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:00:03.2344498Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:00:03.2344800Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:00:03.2345071Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:00:03.2345470Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:00:03.2345755Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:00:03.2346003Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:00:03.2346253Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:00:03.2346437Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.2346532Z from rng_extension.cpp:1: 2025-07-17T10:00:03.2346877Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:00:03.2346981Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:00:03.2347049Z | ^~~~ 2025-07-17T10:00:03.2347267Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:00:03.2347470Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:00:03.2347662Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:00:03.2347846Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:00:03.2348024Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:00:03.2348260Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:00:03.2348481Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:00:03.2348689Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:00:03.2349012Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:00:03.2349264Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:00:03.2349565Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:00:03.2349812Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.2349896Z from rng_extension.cpp:1: 2025-07-17T10:00:03.2350397Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:00:03.2350889Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:00:03.2351405Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:00:03.2351918Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:00:03.2352496Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:00:03.2353102Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:00:03.2355956Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:00:03.2359269Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:00:03.2359918Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:00:03.2360034Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:00:03.2360108Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:00:03.2363514Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:00:03.2367200Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:00:03.2367944Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:00:03.2368287Z | ^~~~~~~~ 2025-07-17T10:00:03.2368719Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:00:03.2369283Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:00:03.2369850Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:00:03.2370317Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:00:03.2370751Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:00:03.2371295Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:00:03.2371870Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:00:03.2372519Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:00:03.2373405Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:00:03.2374042Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:00:03.2374718Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:00:03.2375242Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:00:03.2375804Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:00:03.2376141Z from rng_extension.cpp:1: 2025-07-17T10:00:03.2376623Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:00:03.2377067Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:00:03.2377285Z | ^~~~ 2025-07-17T10:00:03.2379132Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/rng_extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/rng.cpython-312-x86_64-linux-gnu.so 2025-07-17T10:00:03.6711155Z building 'torch_test_cpp_extension.cuda' extension 2025-07-17T10:00:03.6713479Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THH -I/opt/rocm/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c cuda_extension.cpp -o build/temp.linux-x86_64-cpython-312/cuda_extension.o -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1016\" -DTORCH_EXTENSION_NAME=cuda -std=c++17 2025-07-17T10:00:23.0464512Z /opt/rocm/bin/hipcc -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THH -I/opt/rocm/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c hip_extension_kernel.hip -o build/temp.linux-x86_64-cpython-312/hip_extension_kernel.o -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -DCUDA_HAS_FP16=1 -D__HIP_NO_HALF_OPERATORS__=1 -D__HIP_NO_HALF_CONVERSIONS__=1 -DHIP_ENABLE_WARP_SYNC_BUILTINS=1 -O2 -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1016\" -DTORCH_EXTENSION_NAME=cuda --offload-arch=gfx90a --offload-arch=gfx942 -fno-gpu-rdc -std=c++17 2025-07-17T10:00:41.6738715Z /opt/rocm/bin/hipcc -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THH -I/opt/rocm/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c hip_extension_kernel2.hip -o build/temp.linux-x86_64-cpython-312/hip_extension_kernel2.o -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -DCUDA_HAS_FP16=1 -D__HIP_NO_HALF_OPERATORS__=1 -D__HIP_NO_HALF_CONVERSIONS__=1 -DHIP_ENABLE_WARP_SYNC_BUILTINS=1 -O2 -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1016\" -DTORCH_EXTENSION_NAME=cuda --offload-arch=gfx90a --offload-arch=gfx942 -fno-gpu-rdc -std=c++17 2025-07-17T10:01:00.5295616Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/cuda_extension.o build/temp.linux-x86_64-cpython-312/hip_extension_kernel.o build/temp.linux-x86_64-cpython-312/hip_extension_kernel2.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -L/opt/rocm/lib -L/opt/rocm/hip/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -lamdhip64 -lc10_hip -ltorch_hip -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cuda.cpython-312-x86_64-linux-gnu.so 2025-07-17T10:01:01.0893013Z building 'torch_test_cpp_extension.torch_library' extension 2025-07-17T10:01:01.0896691Z /opt/rocm/bin/hipcc -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THH -I/opt/rocm/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c torch_library.cu -o build/temp.linux-x86_64-cpython-312/torch_library.o -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -DCUDA_HAS_FP16=1 -D__HIP_NO_HALF_OPERATORS__=1 -D__HIP_NO_HALF_CONVERSIONS__=1 -DHIP_ENABLE_WARP_SYNC_BUILTINS=1 -O2 -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1016\" -DTORCH_EXTENSION_NAME=torch_library --offload-arch=gfx90a --offload-arch=gfx942 -fno-gpu-rdc -std=c++17 2025-07-17T10:01:42.5486566Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/torch_library.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -L/opt/rocm/lib -L/opt/rocm/hip/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -lamdhip64 -lc10_hip -ltorch_hip -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/torch_library.cpython-312-x86_64-linux-gnu.so 2025-07-17T10:01:42.8934587Z running install_lib 2025-07-17T10:01:42.9013885Z creating install/opt/conda/envs/py_3.12/lib/python3.12/site-packages 2025-07-17T10:01:42.9019257Z creating install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2025-07-17T10:01:42.9020193Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cpp.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2025-07-17T10:01:42.9084565Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/rng.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2025-07-17T10:01:42.9161495Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cuda.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2025-07-17T10:01:42.9238890Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/__init__.py -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2025-07-17T10:01:42.9240203Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/maia.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2025-07-17T10:01:42.9327100Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/torch_library.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2025-07-17T10:01:42.9337464Z byte-compiling ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension/__init__.py to __init__.cpython-312.pyc 2025-07-17T10:01:42.9340481Z running install_egg_info 2025-07-17T10:01:42.9512334Z running egg_info 2025-07-17T10:01:42.9577015Z creating torch_test_cpp_extension.egg-info 2025-07-17T10:01:42.9580230Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2025-07-17T10:01:42.9580977Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2025-07-17T10:01:42.9582398Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt 2025-07-17T10:01:42.9584636Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2025-07-17T10:01:42.9586342Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-07-17T10:01:42.9660019Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-07-17T10:01:42.9665455Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-07-17T10:01:42.9666562Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension-0.0.0-py3.12.egg-info 2025-07-17T10:01:42.9677594Z running install_scripts 2025-07-17T10:01:46.2251050Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T10:01:46.2252067Z import pkg_resources 2025-07-17T10:01:46.2551382Z 2025-07-17T10:01:46.2551814Z Running tests... 2025-07-17T10:01:46.2552080Z ---------------------------------------------------------------------- 2025-07-17T10:01:46.5111326Z . 2025-07-17T10:01:46.5111713Z ---------------------------------------------------------------------- 2025-07-17T10:01:46.5112015Z Ran 1 test in 0.256s 2025-07-17T10:01:46.5112136Z 2025-07-17T10:01:46.5112195Z OK 2025-07-17T10:01:46.5112283Z 2025-07-17T10:01:46.5112391Z Generating XML reports... 2025-07-17T10:01:47.0991664Z Running test_autoload_enable 1/1 ... [2025-07-17 10:01:47.098520] 2025-07-17T10:01:49.5159913Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T10:01:49.5160909Z import pkg_resources 2025-07-17T10:01:49.5459577Z /var/lib/jenkins/pytorch/test/cpp_extensions/cuda_extension.cpp -> /var/lib/jenkins/pytorch/test/cpp_extensions/cuda_extension.cpp [skipped, no changes] 2025-07-17T10:01:49.5460624Z /var/lib/jenkins/pytorch/test/cpp_extensions/cuda_extension_kernel.cu -> /var/lib/jenkins/pytorch/test/cpp_extensions/hip_extension_kernel.hip [skipped, already hipified] 2025-07-17T10:01:49.5462612Z /var/lib/jenkins/pytorch/test/cpp_extensions/cuda_extension_kernel2.cu -> /var/lib/jenkins/pytorch/test/cpp_extensions/hip_extension_kernel2.hip [skipped, already hipified] 2025-07-17T10:01:49.5463488Z Successfully preprocessed all matching files. 2025-07-17T10:01:49.5463773Z Total number of unsupported CUDA function calls: 0 2025-07-17T10:01:49.5464080Z 2025-07-17T10:01:49.5464084Z 2025-07-17T10:01:49.5464288Z Total number of replaced kernel launches: 2 2025-07-17T10:01:49.5483533Z /var/lib/jenkins/pytorch/test/cpp_extensions/torch_library.cu -> /var/lib/jenkins/pytorch/test/cpp_extensions/torch_library.cu [skipped, no changes] 2025-07-17T10:01:49.5484085Z Successfully preprocessed all matching files. 2025-07-17T10:01:49.5484370Z Total number of unsupported CUDA function calls: 0 2025-07-17T10:01:49.5484656Z 2025-07-17T10:01:49.5484659Z 2025-07-17T10:01:49.5484752Z Total number of replaced kernel launches: 0 2025-07-17T10:01:49.5843756Z running install 2025-07-17T10:01:49.5844358Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:90: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-07-17T10:01:49.5844859Z !! 2025-07-17T10:01:49.5844943Z 2025-07-17T10:01:49.5845048Z ******************************************************************************** 2025-07-17T10:01:49.5845296Z Please avoid running ``setup.py`` directly. 2025-07-17T10:01:49.5845565Z Instead, use pypa/build, pypa/installer or other 2025-07-17T10:01:49.5845797Z standards-based tools. 2025-07-17T10:01:49.5845927Z 2025-07-17T10:01:49.5846071Z By 2025-Oct-31, you need to update your project and remove deprecated calls 2025-07-17T10:01:49.5846369Z or your builds will no longer be supported. 2025-07-17T10:01:49.5846520Z 2025-07-17T10:01:49.5846724Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-07-17T10:01:49.5847039Z ******************************************************************************** 2025-07-17T10:01:49.5847191Z 2025-07-17T10:01:49.5847250Z !! 2025-07-17T10:01:49.5847457Z self.initialize_options() 2025-07-17T10:01:49.5956149Z running build 2025-07-17T10:01:49.5956411Z running build_py 2025-07-17T10:01:49.6029073Z creating build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension 2025-07-17T10:01:49.6031532Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension 2025-07-17T10:01:49.6034546Z running build_ext 2025-07-17T10:01:49.6047471Z building 'torch_test_cpp_extension.cpp' extension 2025-07-17T10:01:49.6048773Z creating build/temp.linux-x86_64-cpython-312 2025-07-17T10:01:49.6052330Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c extension.cpp -o build/temp.linux-x86_64-cpython-312/extension.o -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1016\" -DTORCH_EXTENSION_NAME=cpp -std=c++17 2025-07-17T10:01:49.7851990Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/Exceptions.h:12, 2025-07-17T10:01:49.7852663Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11, 2025-07-17T10:01:49.7853222Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:9, 2025-07-17T10:01:49.7853562Z from extension.cpp:1: 2025-07-17T10:01:49.7854576Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_’: 2025-07-17T10:01:49.7855601Z extension.cpp:45:53: required from here 2025-07-17T10:01:49.7856456Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h:1539:7: warning: ‘pybind11::class_’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes] 2025-07-17T10:01:49.7857446Z 1539 | class class_ : public detail::generic_type { 2025-07-17T10:01:49.7857682Z | ^~~~~~ 2025-07-17T10:01:49.7858877Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]’: 2025-07-17T10:01:49.7859853Z extension.cpp:45:53: required from here 2025-07-17T10:01:49.7861553Z /opt/conda/envs/py_3.12/lib/python3.12/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] 2025-07-17T10:01:49.7862656Z 1599 | with_internals([&](internals &internals) { 2025-07-17T10:01:49.7862898Z | ^~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:01:49.7863209Z 1600 | auto &instances = record.module_local ? get_local_internals().registered_types_cpp 2025-07-17T10:01:49.7863556Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:01:49.7863849Z 1601 | : internals.registered_types_cpp; 2025-07-17T10:01:49.7864113Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:01:49.7864382Z 1602 | instances[std::type_index(typeid(type_alias))] 2025-07-17T10:01:49.7864625Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:01:49.7864873Z 1603 | = instances[std::type_index(typeid(type))]; 2025-07-17T10:01:49.7865122Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:01:49.7865456Z 1604 | }); 2025-07-17T10:01:49.7865635Z | ~ 2025-07-17T10:01:49.7933882Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cpp.cpython-312-x86_64-linux-gnu.so 2025-07-17T10:01:50.2894853Z building 'torch_test_cpp_extension.maia' extension 2025-07-17T10:01:50.2897449Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c maia_extension.cpp -o build/temp.linux-x86_64-cpython-312/maia_extension.o -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1016\" -DTORCH_EXTENSION_NAME=maia -std=c++17 2025-07-17T10:01:50.4754546Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/maia_extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/maia.cpython-312-x86_64-linux-gnu.so 2025-07-17T10:01:50.9559869Z building 'torch_test_cpp_extension.rng' extension 2025-07-17T10:01:51.1398770Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c rng_extension.cpp -o build/temp.linux-x86_64-cpython-312/rng_extension.o -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1016\" -DTORCH_EXTENSION_NAME=rng -std=c++17 2025-07-17T10:01:51.1401681Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-07-17T10:01:51.1402508Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-07-17T10:01:51.1403255Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-07-17T10:01:51.1403968Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-07-17T10:01:51.1404461Z from rng_extension.cpp:6: 2025-07-17T10:01:51.1405555Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1458: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-07-17T10:01:51.1406187Z 1458 | #pragma unroll 2025-07-17T10:01:51.1406409Z | 2025-07-17T10:01:51.1406838Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_convert.h:4, 2025-07-17T10:01:51.1407551Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1510, 2025-07-17T10:01:51.1408036Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-07-17T10:01:51.1408512Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-07-17T10:01:51.1408993Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-07-17T10:01:51.1409525Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-07-17T10:01:51.1409941Z from rng_extension.cpp:6: 2025-07-17T10:01:51.1410516Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:59: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-07-17T10:01:51.1410986Z 59 | #pragma unroll 2025-07-17T10:01:51.1411160Z | 2025-07-17T10:01:51.1411662Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:72: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-07-17T10:01:51.1412558Z 72 | #pragma unroll 2025-07-17T10:01:51.1412721Z | 2025-07-17T10:01:51.1413235Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:87: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-07-17T10:01:51.1413927Z 87 | #pragma unroll 2025-07-17T10:01:51.1414085Z | 2025-07-17T10:01:51.1414404Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1511, 2025-07-17T10:01:51.1414916Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-07-17T10:01:51.1415496Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-07-17T10:01:51.1415952Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-07-17T10:01:51.1416469Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-07-17T10:01:51.1416874Z from rng_extension.cpp:6: 2025-07-17T10:01:51.1417442Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_mask.h:160: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-07-17T10:01:51.1417905Z 160 | #pragma unroll 2025-07-17T10:01:51.1418071Z | 2025-07-17T10:01:51.1418372Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:01:51.1418873Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:01:51.1419325Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:01:51.1419766Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:01:51.1420193Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:01:51.1420671Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:01:51.1421201Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:01:51.1421698Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:01:51.1422190Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:01:51.1422731Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:01:51.1423413Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:01:51.1423900Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.1424215Z from rng_extension.cpp:1: 2025-07-17T10:01:51.1424927Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:01:51.1426021Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:01:51.1427006Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:01:51.1428116Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:01:51.1429184Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:01:51.1430449Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:01:51.1444991Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:01:51.1450890Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:01:51.1454146Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:01:51.1454707Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:01:51.1454947Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:01:51.1458556Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:01:51.1462270Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:01:51.1463029Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:01:51.1463296Z | ^~~~~~~~ 2025-07-17T10:01:51.1463672Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:01:51.1464244Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:01:51.1477341Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:01:51.1477907Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:01:51.1478361Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:01:51.1478841Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:01:51.1479445Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:01:51.1480102Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:01:51.1480731Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:01:51.1481353Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:01:51.1481932Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:01:51.1482460Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:01:51.1482938Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.1483274Z from rng_extension.cpp:1: 2025-07-17T10:01:51.1483821Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:01:51.1484281Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:01:51.1484515Z | ^~~~ 2025-07-17T10:01:51.1484873Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:01:51.1485521Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:01:51.1485987Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:01:51.1486520Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:01:51.1487034Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:01:51.1487524Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:01:51.1488436Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:01:51.1488956Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:01:51.1489438Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:01:51.1489961Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:01:51.1490509Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:01:51.1490998Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.1491342Z from rng_extension.cpp:1: 2025-07-17T10:01:51.1491964Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:01:51.1492867Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:01:51.1493821Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:01:51.1494805Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:01:51.1495838Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:01:51.1496943Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:01:51.1500368Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:01:51.1506281Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:01:51.1509570Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:01:51.1510110Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:01:51.1510363Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:01:51.1513779Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:01:51.1517323Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:01:51.1518049Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:01:51.1518304Z | ^~~~~~~~ 2025-07-17T10:01:51.1518676Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:01:51.1519247Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:01:51.1519828Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:01:51.1520300Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:01:51.1520883Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:01:51.1521367Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:01:51.1522017Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:01:51.1522660Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:01:51.1523301Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:01:51.1523918Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:01:51.1524483Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:01:51.1525020Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:01:51.1525498Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.1525834Z from rng_extension.cpp:1: 2025-07-17T10:01:51.1526327Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:01:51.1526754Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:01:51.1526987Z | ^~~~ 2025-07-17T10:01:51.1527332Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:01:51.1527820Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:01:51.1528294Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:01:51.1528734Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:01:51.1529171Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:01:51.1529648Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:01:51.1530167Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:01:51.1530670Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:01:51.1531162Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:01:51.1531690Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:01:51.1532217Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:01:51.1532697Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.1533024Z from rng_extension.cpp:1: 2025-07-17T10:01:51.1533603Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:01:51.1534543Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:01:51.1535564Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:01:51.1536685Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:01:51.1537706Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:01:51.1538818Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:01:51.1541972Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:01:51.1547301Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:01:51.1550881Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:01:51.1551431Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:01:51.1551684Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:01:51.1555194Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:01:51.1558745Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:01:51.1559471Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:01:51.1559750Z | ^~~~~~~~ 2025-07-17T10:01:51.1560124Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:01:51.1560688Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:01:51.1561197Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:01:51.1561657Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:01:51.1562094Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:01:51.1562567Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:01:51.1563156Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:01:51.1563787Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:01:51.1564416Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:01:51.1565034Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:01:51.1565595Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:01:51.1566122Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:01:51.1566603Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.1566933Z from rng_extension.cpp:1: 2025-07-17T10:01:51.1567432Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:01:51.1567875Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:01:51.1568186Z | ^~~~ 2025-07-17T10:01:51.1568537Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:01:51.1569099Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:01:51.1569618Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:01:51.1570064Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:01:51.1570539Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:01:51.1571041Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:01:51.1571561Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:01:51.1572056Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:01:51.1572545Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:01:51.1573068Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:01:51.1573605Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:01:51.1574090Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.1574419Z from rng_extension.cpp:1: 2025-07-17T10:01:51.1575005Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:01:51.1575881Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:01:51.1576841Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:01:51.1577800Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:01:51.1578835Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:01:51.1579957Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:01:51.1583255Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:01:51.1588869Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:01:51.1591989Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:01:51.1592545Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:01:51.1592808Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:01:51.1596051Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:01:51.1599451Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:01:51.1600178Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:01:51.1600444Z | ^~~~~~~~ 2025-07-17T10:01:51.1600816Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:01:51.1601451Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:01:51.1601961Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:01:51.1602425Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:01:51.1602947Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:01:51.1603444Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:01:51.1604081Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:01:51.1604719Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:01:51.1605353Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:01:51.1605978Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:01:51.1606540Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:01:51.1607064Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:01:51.1607541Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.1607868Z from rng_extension.cpp:1: 2025-07-17T10:01:51.1608370Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:01:51.1608808Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:01:51.1609034Z | ^~~~ 2025-07-17T10:01:51.1609386Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:01:51.1609877Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:01:51.1610337Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:01:51.1610777Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:01:51.1611200Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:01:51.1611677Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:01:51.1612199Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:01:51.1612691Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:01:51.1613257Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:01:51.1613817Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:01:51.1614357Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:01:51.1614821Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.1615152Z from rng_extension.cpp:1: 2025-07-17T10:01:51.1615809Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:01:51.1616692Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:01:51.1617702Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:01:51.1618729Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:01:51.1619781Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:01:51.1620896Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:01:51.1624226Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:01:51.1629917Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:01:51.1633270Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:01:51.1633824Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:01:51.1634068Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:01:51.1637531Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:01:51.1641285Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:01:51.1641995Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:01:51.1642253Z | ^~~~~~~~ 2025-07-17T10:01:51.1642626Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:01:51.1643180Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:01:51.1643685Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:01:51.1644160Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:01:51.1644592Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:01:51.1645061Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:01:51.1645645Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:01:51.1646270Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:01:51.1646980Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:01:51.1647610Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:01:51.1648171Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:01:51.1648700Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:01:51.1649179Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.1649566Z from rng_extension.cpp:1: 2025-07-17T10:01:51.1650054Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:01:51.1650481Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:01:51.1650759Z | ^~~~ 2025-07-17T10:01:51.1651098Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:01:51.1651576Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:01:51.1652075Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:01:51.1652518Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:01:51.1652949Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:01:51.1653421Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:01:51.1653931Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:01:51.1654419Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:01:51.1654909Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:01:51.1655434Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:01:51.1655963Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:01:51.1656428Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.1656753Z from rng_extension.cpp:1: 2025-07-17T10:01:51.1657340Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:01:51.1658221Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:01:51.1659170Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:01:51.1660165Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:01:51.1661199Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:01:51.1662404Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:01:51.1665819Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:01:51.1671180Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:01:51.1674202Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:01:51.1674727Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:01:51.1674987Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:01:51.1678180Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:01:51.1681647Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:01:51.1682356Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:01:51.1682672Z | ^~~~~~~~ 2025-07-17T10:01:51.1683052Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:01:51.1683617Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:01:51.1684180Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:01:51.1684639Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:01:51.1685125Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:01:51.1685628Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:01:51.1686225Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:01:51.1686869Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:01:51.1687511Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:01:51.1688182Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:01:51.1688804Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:01:51.1689384Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:01:51.1689921Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.1690297Z from rng_extension.cpp:1: 2025-07-17T10:01:51.1690816Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:01:51.1691320Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:01:51.1691539Z | ^~~~ 2025-07-17T10:01:51.1691934Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:01:51.1692467Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:01:51.1692982Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:01:51.1693428Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:01:51.1693913Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:01:51.1694414Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:01:51.1695000Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:01:51.1695492Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:01:51.1695995Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:01:51.1696509Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:01:51.1697046Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:01:51.1697592Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.1697909Z from rng_extension.cpp:1: 2025-07-17T10:01:51.1698487Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:01:51.1699437Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:01:51.1700433Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:01:51.1701397Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:01:51.1702410Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:01:51.1703502Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:01:51.1706737Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:01:51.1712046Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:01:51.1715210Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:01:51.1715734Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:01:51.1716042Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:01:51.1719381Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:01:51.1722776Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:01:51.1723522Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:01:51.1723788Z | ^~~~~~~~ 2025-07-17T10:01:51.1724154Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:01:51.1724715Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:01:51.1725233Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:01:51.1725686Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:01:51.1726115Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:01:51.1726581Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:01:51.1727158Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:01:51.1727774Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:01:51.1728483Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:01:51.1729096Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:01:51.1729648Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:01:51.1730161Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:01:51.1730680Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.1731003Z from rng_extension.cpp:1: 2025-07-17T10:01:51.1731473Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:01:51.1731973Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:01:51.1732183Z | ^~~~ 2025-07-17T10:01:51.1732514Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:01:51.1733045Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:01:51.1733511Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:01:51.1733943Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:01:51.1734357Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:01:51.1734836Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:01:51.1735358Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:01:51.1735843Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:01:51.1736324Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:01:51.1736836Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:01:51.1737369Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:01:51.1737836Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.1738157Z from rng_extension.cpp:1: 2025-07-17T10:01:51.1738734Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:01:51.1739603Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:01:51.1740541Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:01:51.1741496Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:01:51.1742513Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:01:51.1743703Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:01:51.1747271Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:01:51.1752985Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:01:51.1756120Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:01:51.1756641Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:01:51.1756874Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:01:51.1760235Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:01:51.1763830Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:01:51.1764535Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:01:51.1764849Z | ^~~~~~~~ 2025-07-17T10:01:51.1765212Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:01:51.1765754Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:01:51.1766303Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:01:51.1766754Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:01:51.1767179Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:01:51.1767637Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:01:51.1768207Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:01:51.1768839Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:01:51.1769487Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:01:51.1770103Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:01:51.1770668Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:01:51.1771189Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:01:51.1771667Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.1771995Z from rng_extension.cpp:1: 2025-07-17T10:01:51.1772497Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:01:51.1772919Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:01:51.1773151Z | ^~~~ 2025-07-17T10:01:51.1773502Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:01:51.1773986Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:01:51.1774438Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:01:51.1774876Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:01:51.1775372Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:01:51.1775841Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:01:51.1776364Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:01:51.1776865Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:01:51.1777345Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:01:51.1777918Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:01:51.1778451Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:01:51.1778979Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.1779302Z from rng_extension.cpp:1: 2025-07-17T10:01:51.1779953Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:01:51.1780831Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:01:51.1781782Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:01:51.1782742Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:01:51.1783796Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:01:51.1784904Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:01:51.1788165Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:01:51.1793575Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:01:51.1796755Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:01:51.1797295Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:01:51.1797542Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:01:51.1800818Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:01:51.1804214Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:01:51.1804926Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:01:51.1805186Z | ^~~~~~~~ 2025-07-17T10:01:51.1805552Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:01:51.1806123Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:01:51.1806617Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:01:51.1807072Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:01:51.1807497Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:01:51.1807970Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:01:51.1808633Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:01:51.1809265Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:01:51.1809894Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:01:51.1810508Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:01:51.1811136Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:01:51.1811714Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:01:51.1812187Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.1812576Z from rng_extension.cpp:1: 2025-07-17T10:01:51.1813073Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:01:51.1813505Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:01:51.1813789Z | ^~~~ 2025-07-17T10:01:51.1814131Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:01:51.1814629Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:01:51.1815097Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:01:51.1815536Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:01:51.1815971Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:01:51.1816447Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:01:51.1816979Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:01:51.1817471Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:01:51.1817955Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:01:51.1818473Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:01:51.1819008Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:01:51.1819478Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.1819802Z from rng_extension.cpp:1: 2025-07-17T10:01:51.1820402Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:01:51.1821286Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:01:51.1822249Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:01:51.1823301Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:01:51.1824339Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:01:51.1825543Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:01:51.1828859Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:01:51.1834253Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:01:51.1837280Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:01:51.1837821Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:01:51.1838072Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:01:51.1841261Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:01:51.1844735Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:01:51.1845515Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:01:51.1845777Z | ^~~~~~~~ 2025-07-17T10:01:51.1846144Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:01:51.1846765Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:01:51.1847274Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:01:51.1847732Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:01:51.1848165Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:01:51.1848631Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:01:51.1849231Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:01:51.1849855Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:01:51.1850492Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:01:51.1851103Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:01:51.1851659Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:01:51.1852185Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:01:51.1852660Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.1852993Z from rng_extension.cpp:1: 2025-07-17T10:01:51.1853483Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:01:51.1853925Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:01:51.1854148Z | ^~~~ 2025-07-17T10:01:51.1854489Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:01:51.1854969Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:01:51.1855425Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:01:51.1855942Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:01:51.1856366Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:01:51.1856853Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:01:51.1857366Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:01:51.1857862Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:01:51.1858413Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:01:51.1858931Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:01:51.1859465Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:01:51.1860022Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.1860349Z from rng_extension.cpp:1: 2025-07-17T10:01:51.1861012Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:01:51.1861900Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:01:51.1862853Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:01:51.1863820Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:01:51.1864845Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:01:51.1866049Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:01:51.1869196Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:01:51.1874658Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:01:51.1877813Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:01:51.1878354Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:01:51.1878677Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:01:51.1881947Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:01:51.1885291Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:01:51.1886010Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:01:51.1886271Z | ^~~~~~~~ 2025-07-17T10:01:51.1886640Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:01:51.1887210Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:01:51.1887714Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:01:51.1888261Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:01:51.1888700Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:01:51.1889169Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:01:51.1890021Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:01:51.1890665Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:01:51.1891361Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:01:51.1891981Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:01:51.1892541Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:01:51.1893069Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:01:51.1893544Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.1893874Z from rng_extension.cpp:1: 2025-07-17T10:01:51.1894365Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:01:51.1894799Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:01:51.1895028Z | ^~~~ 2025-07-17T10:01:51.1895370Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:01:51.1895856Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:01:51.1896314Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:01:51.1896762Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:01:51.1897183Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:01:51.1897660Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:01:51.1898176Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:01:51.1898672Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:01:51.1899193Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:01:51.1899724Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:01:51.1900268Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:01:51.1900745Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.1901068Z from rng_extension.cpp:1: 2025-07-17T10:01:51.1901667Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:01:51.1902553Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:01:51.1903580Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:01:51.1904717Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:01:51.1905982Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:01:51.1907198Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:01:51.1910419Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:01:51.1915787Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:01:51.1918854Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:01:51.1919400Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:01:51.1919649Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:01:51.1923086Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:01:51.1926632Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:01:51.1927354Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:01:51.1927622Z | ^~~~~~~~ 2025-07-17T10:01:51.1927995Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:01:51.1928556Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:01:51.1929066Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:01:51.1929529Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:01:51.1929960Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:01:51.1930442Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:01:51.1931048Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:01:51.1931682Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:01:51.1932311Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:01:51.1932924Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:01:51.1933487Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:01:51.1934010Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:01:51.1934485Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.1934818Z from rng_extension.cpp:1: 2025-07-17T10:01:51.1935315Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:01:51.1935756Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:01:51.1935984Z | ^~~~ 2025-07-17T10:01:51.1936336Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:01:51.1936894Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:01:51.1937350Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:01:51.1937900Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:01:51.1938319Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:01:51.1938852Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:01:51.1939371Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:01:51.1939860Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:01:51.1940353Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:01:51.1940885Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:01:51.1941423Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:01:51.1941895Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.1942219Z from rng_extension.cpp:1: 2025-07-17T10:01:51.1942805Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:01:51.1943689Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:01:51.1944643Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:01:51.1945692Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:01:51.1946754Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:01:51.1947880Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:01:51.1951344Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:01:51.1957242Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:01:51.1960505Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:01:51.1961054Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:01:51.1961316Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:01:51.1964741Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:01:51.1968207Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:01:51.1968923Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:01:51.1969181Z | ^~~~~~~~ 2025-07-17T10:01:51.1969558Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:01:51.1970208Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:01:51.1970718Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:01:51.1971245Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:01:51.1971753Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:01:51.1972232Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:01:51.1972896Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:01:51.1973566Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:01:51.1974205Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:01:51.1974835Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:01:51.1975401Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:01:51.1975924Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:01:51.1976401Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.1976736Z from rng_extension.cpp:1: 2025-07-17T10:01:51.1977236Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:01:51.1977670Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:01:51.1977898Z | ^~~~ 2025-07-17T10:01:51.1978255Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:01:51.1978738Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:01:51.1979200Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:01:51.1979644Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:01:51.1980069Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:01:51.1980537Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:01:51.1981055Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:01:51.1981557Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:01:51.1982051Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:01:51.1982569Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:01:51.1983104Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:01:51.1983573Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.1983893Z from rng_extension.cpp:1: 2025-07-17T10:01:51.1984620Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:01:51.1985634Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:01:51.1986743Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:01:51.1987808Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:01:51.1988854Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:01:51.1989967Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:01:51.1993379Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:01:51.1999108Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:01:51.2002407Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:01:51.2002947Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:01:51.2003250Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:01:51.2006797Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:01:51.2010361Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:01:51.2011074Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:01:51.2011337Z | ^~~~~~~~ 2025-07-17T10:01:51.2011714Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:01:51.2012281Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:01:51.2012776Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:01:51.2013243Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:01:51.2013679Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:01:51.2014150Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:01:51.2014736Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:01:51.2015362Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:01:51.2015997Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:01:51.2016614Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:01:51.2017177Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:01:51.2017704Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:01:51.2018241Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.2018575Z from rng_extension.cpp:1: 2025-07-17T10:01:51.2019075Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:01:51.2019638Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:01:51.2019866Z | ^~~~ 2025-07-17T10:01:51.2020210Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:01:51.2020791Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:01:51.2021264Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:01:51.2021704Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:01:51.2022131Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:01:51.2022623Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:01:51.2023143Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:01:51.2023643Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:01:51.2024135Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:01:51.2024661Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:01:51.2025201Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:01:51.2031992Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.2032347Z from rng_extension.cpp:1: 2025-07-17T10:01:51.2033049Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:01:51.2033968Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:01:51.2034928Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:01:51.2035901Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:01:51.2036954Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:01:51.2038072Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:01:51.2041664Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:01:51.2047483Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:01:51.2050915Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:01:51.2051487Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:01:51.2051742Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:01:51.2055254Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:01:51.2058835Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:01:51.2059625Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:01:51.2059884Z | ^~~~~~~~ 2025-07-17T10:01:51.2060253Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:01:51.2060879Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:01:51.2061385Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:01:51.2061842Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:01:51.2062268Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:01:51.2062748Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:01:51.2063322Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:01:51.2063944Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:01:51.2064574Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:01:51.2065216Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:01:51.2065897Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:01:51.2066522Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:01:51.2066998Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.2067333Z from rng_extension.cpp:1: 2025-07-17T10:01:51.2076037Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:01:51.2076500Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:01:51.2076739Z | ^~~~ 2025-07-17T10:01:51.2077088Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:01:51.2077577Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:01:51.2078060Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:01:51.2078519Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:01:51.2078951Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:01:51.2079434Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:01:51.2079948Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:01:51.2080438Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:01:51.2081042Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:01:51.2081572Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:01:51.2082176Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:01:51.2082644Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.2082971Z from rng_extension.cpp:1: 2025-07-17T10:01:51.2083668Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:01:51.2084587Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:01:51.2085552Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:01:51.2086545Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:01:51.2087569Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:01:51.2088675Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:01:51.2092068Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:01:51.2097905Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:01:51.2101262Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:01:51.2101795Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:01:51.2102038Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:01:51.2105595Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:01:51.2109231Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:01:51.2109952Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:01:51.2110211Z | ^~~~~~~~ 2025-07-17T10:01:51.2110582Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:01:51.2111140Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:01:51.2111645Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:01:51.2112103Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:01:51.2112530Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:01:51.2112999Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:01:51.2113580Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:01:51.2114209Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:01:51.2114950Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:01:51.2115647Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:01:51.2116210Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:01:51.2116743Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:01:51.2117279Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.2117621Z from rng_extension.cpp:1: 2025-07-17T10:01:51.2118110Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:01:51.2118540Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:01:51.2118767Z | ^~~~ 2025-07-17T10:01:51.2119110Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:01:51.2119591Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:01:51.2120037Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:01:51.2120472Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:01:51.2120890Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:01:51.2121358Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:01:51.2121870Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:01:51.2122431Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:01:51.2122912Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:01:51.2123425Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:01:51.2123964Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:01:51.2124426Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.2124752Z from rng_extension.cpp:1: 2025-07-17T10:01:51.2125340Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:01:51.2126234Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:01:51.2127198Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:01:51.2128169Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:01:51.2129256Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:01:51.2130385Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:01:51.2133918Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:01:51.2139668Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:01:51.2142940Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:01:51.2143500Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:01:51.2143752Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:01:51.2147438Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:01:51.2151100Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:01:51.2151804Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:01:51.2152066Z | ^~~~~~~~ 2025-07-17T10:01:51.2152440Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:01:51.2153002Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:01:51.2153497Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:01:51.2153962Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:01:51.2154393Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:01:51.2154873Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:01:51.2155455Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:01:51.2156320Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:01:51.2156943Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:01:51.2157563Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:01:51.2158129Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:01:51.2158657Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:01:51.2159130Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.2159463Z from rng_extension.cpp:1: 2025-07-17T10:01:51.2159951Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:01:51.2160379Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:01:51.2160598Z | ^~~~ 2025-07-17T10:01:51.2160937Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:01:51.2161418Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:01:51.2161867Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:01:51.2162363Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:01:51.2162804Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:01:51.2163342Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:01:51.2163866Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:01:51.2164371Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:01:51.2164925Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:01:51.2165467Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:01:51.2165997Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:01:51.2166470Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.2166799Z from rng_extension.cpp:1: 2025-07-17T10:01:51.2167383Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:01:51.2168278Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:01:51.2169230Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:01:51.2170197Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:01:51.2171303Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:01:51.2172407Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:01:51.2175812Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:01:51.2181614Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:01:51.2184944Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:01:51.2185572Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:01:51.2185835Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:01:51.2189322Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:01:51.2192909Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:01:51.2193616Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:01:51.2193885Z | ^~~~~~~~ 2025-07-17T10:01:51.2194262Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:01:51.2194833Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:01:51.2195332Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:01:51.2195862Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:01:51.2196295Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:01:51.2196844Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:01:51.2197428Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:01:51.2198115Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:01:51.2198744Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:01:51.2199363Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:01:51.2199918Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:01:51.2200455Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:01:51.2200928Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.2201252Z from rng_extension.cpp:1: 2025-07-17T10:01:51.2201749Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:01:51.2202182Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:01:51.2202403Z | ^~~~ 2025-07-17T10:01:51.2202742Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:01:51.2203225Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:01:51.2203741Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:01:51.2204177Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:01:51.2204602Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:01:51.2205078Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:01:51.2205588Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:01:51.2206077Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:01:51.2206569Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:01:51.2207092Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:01:51.2207620Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:01:51.2208087Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.2208406Z from rng_extension.cpp:1: 2025-07-17T10:01:51.2208985Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:01:51.2209922Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:01:51.2210864Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:01:51.2211900Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:01:51.2212993Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:01:51.2214134Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:01:51.2217522Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:01:51.2223195Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:01:51.2226492Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:01:51.2227029Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:01:51.2227355Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:01:51.2230869Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:01:51.2234424Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:01:51.2235128Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:01:51.2235390Z | ^~~~~~~~ 2025-07-17T10:01:51.2235760Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:01:51.2236317Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:01:51.2236888Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:01:51.2237350Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:01:51.2237780Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:01:51.2238251Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:01:51.2238836Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:01:51.2239456Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:01:51.2240082Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:01:51.2240702Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:01:51.2241255Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:01:51.2241762Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:01:51.2242221Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.2242534Z from rng_extension.cpp:1: 2025-07-17T10:01:51.2243087Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:01:51.2243513Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:01:51.2243732Z | ^~~~ 2025-07-17T10:01:51.2244124Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:01:51.2244596Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:01:51.2245040Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:01:51.2245531Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:01:51.2245953Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:01:51.2246425Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:01:51.2246939Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:01:51.2247422Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:01:51.2247902Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:01:51.2248417Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:01:51.2248944Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:01:51.2249404Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.2249718Z from rng_extension.cpp:1: 2025-07-17T10:01:51.2250298Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:01:51.2251239Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:01:51.2252178Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:01:51.2253147Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:01:51.2254179Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:01:51.2255271Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:01:51.2258760Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:01:51.2264564Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:01:51.2267915Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:01:51.2268530Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:01:51.2268778Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:01:51.2272238Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:01:51.2275799Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:01:51.2276514Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:01:51.2276763Z | ^~~~~~~~ 2025-07-17T10:01:51.2277226Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:01:51.2277784Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:01:51.2278275Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:01:51.2278794Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:01:51.2279229Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:01:51.2279706Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:01:51.2280294Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:01:51.2280918Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:01:51.2281547Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:01:51.2282161Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:01:51.2282714Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:01:51.2283305Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:01:51.2283836Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.2284152Z from rng_extension.cpp:1: 2025-07-17T10:01:51.2284628Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:01:51.2285043Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:01:51.2285261Z | ^~~~ 2025-07-17T10:01:51.2285604Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:01:51.2286068Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:01:51.2286509Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:01:51.2286935Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:01:51.2287350Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:01:51.2287830Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:01:51.2288341Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:01:51.2288829Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:01:51.2289312Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:01:51.2289822Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:01:51.2290402Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:01:51.2290934Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.2291248Z from rng_extension.cpp:1: 2025-07-17T10:01:51.2291828Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:01:51.2292767Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:01:51.2293733Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:01:51.2294692Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:01:51.2295725Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:01:51.2296819Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:01:51.2300199Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:01:51.2306127Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:01:51.2309398Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:01:51.2309923Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:01:51.2310230Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:01:51.2313642Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:01:51.2317237Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:01:51.2317937Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:01:51.2318189Z | ^~~~~~~~ 2025-07-17T10:01:51.2318560Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:01:51.2319114Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:01:51.2319606Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:01:51.2320059Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:01:51.2320481Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:01:51.2320941Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:01:51.2321507Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:01:51.2322129Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:01:51.2322745Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:01:51.2323414Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:01:51.2323979Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:01:51.2324565Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:01:51.2325028Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.2325357Z from rng_extension.cpp:1: 2025-07-17T10:01:51.2325912Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:01:51.2326331Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:01:51.2326536Z | ^~~~ 2025-07-17T10:01:51.2326875Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:01:51.2327351Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:01:51.2327807Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:01:51.2328250Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:01:51.2328668Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:01:51.2329136Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:01:51.2329648Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:01:51.2330132Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:01:51.2330691Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:01:51.2331199Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:01:51.2331724Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:01:51.2332178Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.2332507Z from rng_extension.cpp:1: 2025-07-17T10:01:51.2333077Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:01:51.2333950Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:01:51.2334893Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:01:51.2335850Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:01:51.2336868Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:01:51.2338041Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:01:51.2341268Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:01:51.2346685Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:01:51.2349763Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:01:51.2350287Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:01:51.2350517Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:01:51.2353771Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:01:51.2357139Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:01:51.2357846Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:01:51.2358090Z | ^~~~~~~~ 2025-07-17T10:01:51.2358514Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:01:51.2359070Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:01:51.2359568Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:01:51.2360022Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:01:51.2360446Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:01:51.2360903Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:01:51.2361469Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:01:51.2362098Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:01:51.2362719Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:01:51.2363328Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:01:51.2363941Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:01:51.2364455Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:01:51.2364914Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.2365241Z from rng_extension.cpp:1: 2025-07-17T10:01:51.2365720Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:01:51.2366145Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:01:51.2366367Z | ^~~~ 2025-07-17T10:01:51.2366598Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:01:51.2366798Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:01:51.2367002Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:01:51.2367184Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:01:51.2367364Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:01:51.2367600Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:01:51.2367832Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:01:51.2368114Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:01:51.2368337Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:01:51.2368639Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:01:51.2368874Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:01:51.2369108Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.2369202Z from rng_extension.cpp:1: 2025-07-17T10:01:51.2369636Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:01:51.2370135Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:01:51.2370662Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:01:51.2371162Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:01:51.2371752Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:01:51.2372359Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:01:51.2375320Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:01:51.2378612Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:01:51.2379156Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:01:51.2379266Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:01:51.2379361Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:01:51.2382667Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:01:51.2383455Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:01:51.2383591Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:01:51.2383661Z | ^~~~~~~~ 2025-07-17T10:01:51.2383932Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:01:51.2384180Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:01:51.2384451Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:01:51.2384654Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:01:51.2384828Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:01:51.2385071Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:01:51.2385412Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:01:51.2385779Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:01:51.2386077Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:01:51.2386356Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:01:51.2386597Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:01:51.2387031Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:01:51.2387214Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.2387307Z from rng_extension.cpp:1: 2025-07-17T10:01:51.2387661Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:01:51.2387776Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:01:51.2387846Z | ^~~~ 2025-07-17T10:01:51.2388072Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:01:51.2388267Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:01:51.2388462Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:01:51.2388641Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:01:51.2388818Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:01:51.2389049Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:01:51.2389342Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:01:51.2389558Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:01:51.2389778Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:01:51.2390022Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:01:51.2390247Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:01:51.2390422Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.2390506Z from rng_extension.cpp:1: 2025-07-17T10:01:51.2390942Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:01:51.2391508Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:01:51.2392027Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:01:51.2392538Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:01:51.2393188Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:01:51.2393806Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:01:51.2396727Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:01:51.2399984Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:01:51.2400528Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:01:51.2400688Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:01:51.2400776Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:01:51.2404177Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:01:51.2404896Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:01:51.2405043Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:01:51.2405109Z | ^~~~~~~~ 2025-07-17T10:01:51.2405384Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:01:51.2405629Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:01:51.2405842Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:01:51.2406035Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:01:51.2406215Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:01:51.2406447Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:01:51.2406800Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:01:51.2407075Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:01:51.2407376Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:01:51.2407647Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:01:51.2407899Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:01:51.2408128Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:01:51.2408383Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.2408465Z from rng_extension.cpp:1: 2025-07-17T10:01:51.2408819Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:01:51.2408919Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:01:51.2409001Z | ^~~~ 2025-07-17T10:01:51.2409221Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:01:51.2409423Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:01:51.2409670Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:01:51.2409866Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:01:51.2410045Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:01:51.2410283Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:01:51.2410498Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:01:51.2410783Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:01:51.2411000Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:01:51.2411262Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:01:51.2411487Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:01:51.2411678Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.2411758Z from rng_extension.cpp:1: 2025-07-17T10:01:51.2412215Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:01:51.2412713Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:01:51.2413229Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:01:51.2413814Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:01:51.2414401Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:01:51.2415025Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:01:51.2417937Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:01:51.2421287Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:01:51.2421762Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:01:51.2421869Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:01:51.2421947Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:01:51.2425250Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:01:51.2426164Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:01:51.2426302Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:01:51.2426366Z | ^~~~~~~~ 2025-07-17T10:01:51.2426642Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:01:51.2426876Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:01:51.2427148Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:01:51.2427346Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:01:51.2427524Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:01:51.2427757Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:01:51.2428119Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:01:51.2428402Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:01:51.2428700Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:01:51.2428963Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:01:51.2429219Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:01:51.2429441Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:01:51.2429623Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.2429718Z from rng_extension.cpp:1: 2025-07-17T10:01:51.2430064Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:01:51.2430158Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:01:51.2430239Z | ^~~~ 2025-07-17T10:01:51.2430458Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:01:51.2430712Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:01:51.2430913Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:01:51.2431106Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:01:51.2431286Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:01:51.2431525Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:01:51.2431739Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:01:51.2431958Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:01:51.2432166Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:01:51.2432479Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:01:51.2432699Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:01:51.2432884Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.2432954Z from rng_extension.cpp:1: 2025-07-17T10:01:51.2433396Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:01:51.2433937Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:01:51.2434470Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:01:51.2435039Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:01:51.2435612Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:01:51.2436222Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:01:51.2438852Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:01:51.2441914Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:01:51.2442450Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:01:51.2442561Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:01:51.2442640Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:01:51.2445841Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:01:51.2446567Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:01:51.2446696Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:01:51.2446773Z | ^~~~~~~~ 2025-07-17T10:01:51.2447037Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:01:51.2447274Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:01:51.2447474Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:01:51.2447733Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:01:51.2447908Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:01:51.2448145Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:01:51.2448436Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:01:51.2448726Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:01:51.2449014Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:01:51.2449286Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:01:51.2449579Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:01:51.2449815Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:01:51.2449995Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.2450083Z from rng_extension.cpp:1: 2025-07-17T10:01:51.2450423Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:01:51.2450524Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:01:51.2450649Z | ^~~~ 2025-07-17T10:01:51.2450879Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:01:51.2451077Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:01:51.2451280Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:01:51.2451460Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:01:51.2451700Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:01:51.2451931Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:01:51.2452151Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:01:51.2452366Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:01:51.2452586Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:01:51.2452825Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:01:51.2453047Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:01:51.2453227Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.2453310Z from rng_extension.cpp:1: 2025-07-17T10:01:51.2453773Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:01:51.2454330Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:01:51.2454840Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:01:51.2455357Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:01:51.2455940Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:01:51.2456541Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:01:51.2459555Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:01:51.2462860Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:01:51.2463348Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:01:51.2463510Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:01:51.2463598Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:01:51.2467016Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:01:51.2467790Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:01:51.2467927Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:01:51.2468054Z | ^~~~~~~~ 2025-07-17T10:01:51.2468338Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:01:51.2468578Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:01:51.2468789Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:01:51.2468982Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:01:51.2469226Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:01:51.2469462Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:01:51.2469758Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:01:51.2470033Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:01:51.2470332Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:01:51.2470595Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:01:51.2470838Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:01:51.2471071Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:01:51.2471255Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:01:51.2471417Z from rng_extension.cpp:1: 2025-07-17T10:01:51.2471756Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:01:51.2471855Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:01:51.2471936Z | ^~~~ 2025-07-17T10:01:51.2473661Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/rng_extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/rng.cpython-312-x86_64-linux-gnu.so 2025-07-17T10:01:51.6813206Z building 'torch_test_cpp_extension.cuda' extension 2025-07-17T10:01:51.6816051Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THH -I/opt/rocm/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c cuda_extension.cpp -o build/temp.linux-x86_64-cpython-312/cuda_extension.o -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1016\" -DTORCH_EXTENSION_NAME=cuda -std=c++17 2025-07-17T10:01:51.8614235Z /opt/rocm/bin/hipcc -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THH -I/opt/rocm/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c hip_extension_kernel.hip -o build/temp.linux-x86_64-cpython-312/hip_extension_kernel.o -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -DCUDA_HAS_FP16=1 -D__HIP_NO_HALF_OPERATORS__=1 -D__HIP_NO_HALF_CONVERSIONS__=1 -DHIP_ENABLE_WARP_SYNC_BUILTINS=1 -O2 -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1016\" -DTORCH_EXTENSION_NAME=cuda --offload-arch=gfx90a --offload-arch=gfx942 -fno-gpu-rdc -std=c++17 2025-07-17T10:01:52.0426705Z /opt/rocm/bin/hipcc -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THH -I/opt/rocm/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c hip_extension_kernel2.hip -o build/temp.linux-x86_64-cpython-312/hip_extension_kernel2.o -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -DCUDA_HAS_FP16=1 -D__HIP_NO_HALF_OPERATORS__=1 -D__HIP_NO_HALF_CONVERSIONS__=1 -DHIP_ENABLE_WARP_SYNC_BUILTINS=1 -O2 -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1016\" -DTORCH_EXTENSION_NAME=cuda --offload-arch=gfx90a --offload-arch=gfx942 -fno-gpu-rdc -std=c++17 2025-07-17T10:01:52.2261729Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/cuda_extension.o build/temp.linux-x86_64-cpython-312/hip_extension_kernel.o build/temp.linux-x86_64-cpython-312/hip_extension_kernel2.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -L/opt/rocm/lib -L/opt/rocm/hip/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -lamdhip64 -lc10_hip -ltorch_hip -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cuda.cpython-312-x86_64-linux-gnu.so 2025-07-17T10:01:52.7634631Z building 'torch_test_cpp_extension.torch_library' extension 2025-07-17T10:01:52.7636898Z /opt/rocm/bin/hipcc -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THH -I/opt/rocm/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c torch_library.cu -o build/temp.linux-x86_64-cpython-312/torch_library.o -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -DCUDA_HAS_FP16=1 -D__HIP_NO_HALF_OPERATORS__=1 -D__HIP_NO_HALF_CONVERSIONS__=1 -DHIP_ENABLE_WARP_SYNC_BUILTINS=1 -O2 -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1016\" -DTORCH_EXTENSION_NAME=torch_library --offload-arch=gfx90a --offload-arch=gfx942 -fno-gpu-rdc -std=c++17 2025-07-17T10:01:52.9566120Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/torch_library.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -L/opt/rocm/lib -L/opt/rocm/hip/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -lamdhip64 -lc10_hip -ltorch_hip -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/torch_library.cpython-312-x86_64-linux-gnu.so 2025-07-17T10:01:53.3005626Z running install_lib 2025-07-17T10:01:53.3086024Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/maia.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2025-07-17T10:01:53.3175715Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/torch_library.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2025-07-17T10:01:53.3180325Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cuda.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2025-07-17T10:01:53.3272559Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/rng.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2025-07-17T10:01:53.3358935Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cpp.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2025-07-17T10:01:53.3433457Z running install_egg_info 2025-07-17T10:01:53.3598463Z running egg_info 2025-07-17T10:01:53.3664564Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2025-07-17T10:01:53.3669568Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2025-07-17T10:01:53.3670686Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt 2025-07-17T10:01:53.3672641Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2025-07-17T10:01:53.3745852Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-07-17T10:01:53.3752823Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-07-17T10:01:53.3753739Z removing './install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension-0.0.0-py3.12.egg-info' (and everything under it) 2025-07-17T10:01:53.3758734Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension-0.0.0-py3.12.egg-info 2025-07-17T10:01:53.3768677Z running install_scripts 2025-07-17T10:01:56.3649283Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T10:01:56.3650802Z import pkg_resources 2025-07-17T10:01:56.3947533Z 2025-07-17T10:01:56.3947861Z Running tests... 2025-07-17T10:01:56.3948128Z ---------------------------------------------------------------------- 2025-07-17T10:01:56.6654269Z . 2025-07-17T10:01:56.6654629Z ---------------------------------------------------------------------- 2025-07-17T10:01:56.6654922Z Ran 1 test in 0.271s 2025-07-17T10:01:56.6655034Z 2025-07-17T10:01:56.6655102Z OK 2025-07-17T10:01:56.6655182Z 2025-07-17T10:01:56.6655306Z Generating XML reports... 2025-07-17T10:01:57.2467727Z Running test_ci_sanity_check_fail 1/1 ... [2025-07-17 10:01:57.246220] 2025-07-17T10:01:57.2468179Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:01:57.2469450Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_ci_sanity_check_fail.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 10:01:57.246736] 2025-07-17T10:02:11.3475869Z Running test_cpp_api_parity 1/1 ... [2025-07-17 10:02:11.347100] 2025-07-17T10:02:11.3476234Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:02:11.3476902Z Executing ['/opt/conda/envs/py_3.12/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 10:02:11.347399] 2025-07-17T10:03:10.3211667Z 2025-07-17T10:03:10.3213568Z 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_58e3086ca7e8479e_.log 2025-07-17T10:03:10.3322889Z 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, 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test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bicubic_scale_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bicubic_scale_tuple_shared_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bicubic_scale_tuple_shared_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bicubic_scale_tuple_skewed_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bicubic_scale_tuple_skewed_2d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bicubic_scale_tuple_skewed_2d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bicubic_scale_tuple_skewed_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bicubic_tuple_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bicubic_tuple_2d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bicubic_tuple_2d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bicubic_tuple_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_2d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_2d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_scale_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_scale_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_scale_tuple_shared_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_scale_tuple_shared_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_scale_tuple_skewed_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_scale_tuple_skewed_2d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_scale_tuple_skewed_2d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_scale_tuple_skewed_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_tuple_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_tuple_2d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_tuple_2d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_tuple_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_1d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_1d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_1d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_1d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_scale_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_scale_1d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_scale_1d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_scale_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_tuple_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_tuple_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_1d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_1d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d_launch_configs, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d_launch_configs_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_3d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_3d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_3d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_3d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_scale_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_scale_3d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_scale_3d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_scale_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_tuple_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_tuple_3d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_tuple_3d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_tuple_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_dim0, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_dim0_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_dim3, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_dim3_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_lastdim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_lastdim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_scalar, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_scalar_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_spatial, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_spatial_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_spatial_special, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_spatial_special_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_multimarginloss_1d_input_0d_target_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_multimarginloss_1d_input_0d_target_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_has_parity, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_has_parity_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_no_parity, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_no_parity_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim0, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim0_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim3, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim3_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_scalar, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_scalar_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim_dtype, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim_dtype_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_dtype, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_dtype_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_special, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_special_cuda 2025-07-17T10:03:10.3439449Z 2025-07-17T10:03:10.3439673Z Running test_cpp_extensions_aot_ninja 1/1 ... [2025-07-17 10:03:10.321830] 2025-07-17T10:03:13.6858969Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T10:03:13.6859954Z import pkg_resources 2025-07-17T10:03:13.7179546Z /var/lib/jenkins/pytorch/test/cpp_extensions/cuda_extension.cpp -> /var/lib/jenkins/pytorch/test/cpp_extensions/cuda_extension.cpp [skipped, no changes] 2025-07-17T10:03:13.7181193Z /var/lib/jenkins/pytorch/test/cpp_extensions/cuda_extension_kernel.cu -> /var/lib/jenkins/pytorch/test/cpp_extensions/hip_extension_kernel.hip [skipped, already hipified] 2025-07-17T10:03:13.7182274Z /var/lib/jenkins/pytorch/test/cpp_extensions/cuda_extension_kernel2.cu -> /var/lib/jenkins/pytorch/test/cpp_extensions/hip_extension_kernel2.hip [skipped, already hipified] 2025-07-17T10:03:13.7183134Z Successfully preprocessed all matching files. 2025-07-17T10:03:13.7183418Z Total number of unsupported CUDA function calls: 0 2025-07-17T10:03:13.7183589Z 2025-07-17T10:03:13.7183593Z 2025-07-17T10:03:13.7183687Z Total number of replaced kernel launches: 2 2025-07-17T10:03:13.7205490Z /var/lib/jenkins/pytorch/test/cpp_extensions/torch_library.cu -> /var/lib/jenkins/pytorch/test/cpp_extensions/torch_library.cu [skipped, no changes] 2025-07-17T10:03:13.7206209Z Successfully preprocessed all matching files. 2025-07-17T10:03:13.7206477Z Total number of unsupported CUDA function calls: 0 2025-07-17T10:03:13.7206639Z 2025-07-17T10:03:13.7206642Z 2025-07-17T10:03:13.7206725Z Total number of replaced kernel launches: 0 2025-07-17T10:03:13.7587264Z running install 2025-07-17T10:03:13.7588071Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:90: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-07-17T10:03:13.7602991Z !! 2025-07-17T10:03:13.7603158Z 2025-07-17T10:03:13.7603711Z ******************************************************************************** 2025-07-17T10:03:13.7604164Z Please avoid running ``setup.py`` directly. 2025-07-17T10:03:13.7604581Z Instead, use pypa/build, pypa/installer or other 2025-07-17T10:03:13.7604958Z standards-based tools. 2025-07-17T10:03:13.7605186Z 2025-07-17T10:03:13.7605407Z By 2025-Oct-31, you need to update your project and remove deprecated calls 2025-07-17T10:03:13.7605914Z or your builds will no longer be supported. 2025-07-17T10:03:13.7606167Z 2025-07-17T10:03:13.7606503Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-07-17T10:03:13.7607041Z ******************************************************************************** 2025-07-17T10:03:13.7607273Z 2025-07-17T10:03:13.7607382Z !! 2025-07-17T10:03:13.7607644Z self.initialize_options() 2025-07-17T10:03:13.7703110Z running build 2025-07-17T10:03:13.7703468Z running build_py 2025-07-17T10:03:13.7781741Z creating build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension 2025-07-17T10:03:13.7783225Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension 2025-07-17T10:03:13.7787808Z running build_ext 2025-07-17T10:03:13.8083847Z building 'torch_test_cpp_extension.cpp' extension 2025-07-17T10:03:13.8084589Z creating /var/lib/jenkins/pytorch/test/cpp_extensions/build/temp.linux-x86_64-cpython-312 2025-07-17T10:03:36.3731298Z [1/1] c++ -MMD -MF /var/lib/jenkins/pytorch/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/extension.o.d -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/var/lib/jenkins/pytorch/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c -c /var/lib/jenkins/pytorch/test/cpp_extensions/extension.cpp -o /var/lib/jenkins/pytorch/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/extension.o -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -g -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1016"' -DTORCH_EXTENSION_NAME=cpp -std=c++17 2025-07-17T10:03:36.3736827Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/Exceptions.h:12, 2025-07-17T10:03:36.3737755Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11, 2025-07-17T10:03:36.3738571Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:9, 2025-07-17T10:03:36.3739224Z from /var/lib/jenkins/pytorch/test/cpp_extensions/extension.cpp:1: 2025-07-17T10:03:36.3740807Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_’: 2025-07-17T10:03:36.3742074Z /var/lib/jenkins/pytorch/test/cpp_extensions/extension.cpp:45:53: required from here 2025-07-17T10:03:36.3743696Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h:1539:7: warning: ‘pybind11::class_’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes] 2025-07-17T10:03:36.3744895Z 1539 | class class_ : public detail::generic_type { 2025-07-17T10:03:36.3745447Z | ^~~~~~ 2025-07-17T10:03:36.3747274Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]’: 2025-07-17T10:03:36.3748746Z /var/lib/jenkins/pytorch/test/cpp_extensions/extension.cpp:45:53: required from here 2025-07-17T10:03:36.3751186Z /opt/conda/envs/py_3.12/lib/python3.12/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] 2025-07-17T10:03:36.3753021Z 1599 | with_internals([&](internals &internals) { 2025-07-17T10:03:36.3753421Z | ^~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:03:36.3753934Z 1600 | auto &instances = record.module_local ? get_local_internals().registered_types_cpp 2025-07-17T10:03:36.3754535Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:03:36.3755001Z 1601 | : internals.registered_types_cpp; 2025-07-17T10:03:36.3755410Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:03:36.3755857Z 1602 | instances[std::type_index(typeid(type_alias))] 2025-07-17T10:03:36.3756471Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:03:36.3756895Z 1603 | = instances[std::type_index(typeid(type))]; 2025-07-17T10:03:36.3757309Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:03:36.3757657Z 1604 | }); 2025-07-17T10:03:36.3757928Z | ~ 2025-07-17T10:03:36.4293237Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib /var/lib/jenkins/pytorch/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cpp.cpython-312-x86_64-linux-gnu.so 2025-07-17T10:03:37.0157685Z building 'torch_test_cpp_extension.maia' extension 2025-07-17T10:03:58.0106993Z [1/1] c++ -MMD -MF /var/lib/jenkins/pytorch/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/maia_extension.o.d -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/var/lib/jenkins/pytorch/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c -c /var/lib/jenkins/pytorch/test/cpp_extensions/maia_extension.cpp -o /var/lib/jenkins/pytorch/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/maia_extension.o -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -g -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1016"' -DTORCH_EXTENSION_NAME=maia -std=c++17 2025-07-17T10:03:58.0150754Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib /var/lib/jenkins/pytorch/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/maia_extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/maia.cpython-312-x86_64-linux-gnu.so 2025-07-17T10:03:58.5021473Z building 'torch_test_cpp_extension.rng' extension 2025-07-17T10:04:20.7992102Z [1/1] c++ -MMD -MF /var/lib/jenkins/pytorch/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/rng_extension.o.d -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/var/lib/jenkins/pytorch/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c -c /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp -o /var/lib/jenkins/pytorch/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/rng_extension.o -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -g -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1016"' -DTORCH_EXTENSION_NAME=rng -std=c++17 2025-07-17T10:04:20.7996077Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-07-17T10:04:20.7996872Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-07-17T10:04:20.7997590Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-07-17T10:04:20.7998397Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-07-17T10:04:20.7999142Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:6: 2025-07-17T10:04:20.8000230Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1458: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-07-17T10:04:20.8000702Z 1458 | #pragma unroll 2025-07-17T10:04:20.8000877Z | 2025-07-17T10:04:20.8001213Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_convert.h:4, 2025-07-17T10:04:20.8001874Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1510, 2025-07-17T10:04:20.8002667Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-07-17T10:04:20.8003450Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-07-17T10:04:20.8004304Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-07-17T10:04:20.8005008Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-07-17T10:04:20.8005503Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:6: 2025-07-17T10:04:20.8006322Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:59: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-07-17T10:04:20.8006786Z 59 | #pragma unroll 2025-07-17T10:04:20.8007115Z | 2025-07-17T10:04:20.8007599Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:72: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-07-17T10:04:20.8008054Z 72 | #pragma unroll 2025-07-17T10:04:20.8008228Z | 2025-07-17T10:04:20.8008718Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:87: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-07-17T10:04:20.8009161Z 87 | #pragma unroll 2025-07-17T10:04:20.8009333Z | 2025-07-17T10:04:20.8009662Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1511, 2025-07-17T10:04:20.8010180Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-07-17T10:04:20.8010645Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-07-17T10:04:20.8011102Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-07-17T10:04:20.8011625Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-07-17T10:04:20.8012100Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:6: 2025-07-17T10:04:20.8012727Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_mask.h:160: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-07-17T10:04:20.8013272Z 160 | #pragma unroll 2025-07-17T10:04:20.8013441Z | 2025-07-17T10:04:20.8013749Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:04:20.8014234Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:04:20.8014694Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:04:20.8015137Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:04:20.8015562Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:04:20.8016050Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:04:20.8016569Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:04:20.8017135Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:04:20.8017627Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:04:20.8018156Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:04:20.8018692Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:04:20.8019231Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8019645Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8020429Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:04:20.8021349Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:04:20.8022375Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:04:20.8023348Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:04:20.8024392Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:04:20.8025623Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:04:20.8029049Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:04:20.8034941Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:04:20.8038254Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:04:20.8038812Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:04:20.8039062Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:04:20.8042634Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:04:20.8046214Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:04:20.8046989Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:04:20.8047258Z | ^~~~~~~~ 2025-07-17T10:04:20.8047631Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:04:20.8048198Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:04:20.8048709Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:04:20.8049172Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:04:20.8049607Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:04:20.8050075Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:04:20.8050727Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:04:20.8051350Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:04:20.8051994Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:04:20.8052610Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:04:20.8053243Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:04:20.8053776Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:04:20.8054251Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8054646Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8055207Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:04:20.8055705Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:04:20.8055931Z | ^~~~ 2025-07-17T10:04:20.8056270Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:04:20.8056757Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:04:20.8057208Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:04:20.8057648Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:04:20.8058055Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:04:20.8058526Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:04:20.8059039Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:04:20.8059529Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:04:20.8060006Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:04:20.8060525Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:04:20.8061160Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:04:20.8061638Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8062031Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8062700Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:04:20.8063575Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:04:20.8064528Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:04:20.8065658Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:04:20.8066723Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:04:20.8067829Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:04:20.8071306Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:04:20.8076999Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:04:20.8080249Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:04:20.8080780Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:04:20.8081026Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:04:20.8084559Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:04:20.8088111Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:04:20.8088815Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:04:20.8089137Z | ^~~~~~~~ 2025-07-17T10:04:20.8089507Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:04:20.8090064Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:04:20.8090562Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:04:20.8091024Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:04:20.8091456Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:04:20.8091936Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:04:20.8092527Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:04:20.8093155Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:04:20.8093785Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:04:20.8094534Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:04:20.8095099Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:04:20.8095620Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:04:20.8096093Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8096488Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8097050Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:04:20.8097492Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:04:20.8097711Z | ^~~~ 2025-07-17T10:04:20.8098061Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:04:20.8098672Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:04:20.8099131Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:04:20.8099570Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:04:20.8099996Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:04:20.8100462Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:04:20.8101057Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:04:20.8101561Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:04:20.8102044Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:04:20.8102561Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:04:20.8103152Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:04:20.8103630Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8104023Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8104687Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:04:20.8105660Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:04:20.8106614Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:04:20.8107586Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:04:20.8108618Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:04:20.8109837Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:04:20.8113112Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:04:20.8118627Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:04:20.8121717Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:04:20.8122257Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:04:20.8122509Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:04:20.8125779Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:04:20.8129217Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:04:20.8129938Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:04:20.8130199Z | ^~~~~~~~ 2025-07-17T10:04:20.8130567Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:04:20.8131194Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:04:20.8131705Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:04:20.8132230Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:04:20.8132663Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:04:20.8133188Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:04:20.8133787Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:04:20.8134421Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:04:20.8135049Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:04:20.8135667Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:04:20.8136230Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:04:20.8136751Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:04:20.8137231Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8137626Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8138191Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:04:20.8138636Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:04:20.8138862Z | ^~~~ 2025-07-17T10:04:20.8139210Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:04:20.8139693Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:04:20.8140144Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:04:20.8140585Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:04:20.8141024Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:04:20.8141487Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:04:20.8142095Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:04:20.8142593Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:04:20.8143079Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:04:20.8143601Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:04:20.8144133Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:04:20.8144614Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8145098Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8145834Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:04:20.8146804Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:04:20.8147827Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:04:20.8148805Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:04:20.8149839Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:04:20.8150950Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:04:20.8154115Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:04:20.8159363Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:04:20.8162557Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:04:20.8163092Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:04:20.8163337Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:04:20.8166682Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:04:20.8170063Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:04:20.8170772Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:04:20.8171032Z | ^~~~~~~~ 2025-07-17T10:04:20.8171410Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:04:20.8171977Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:04:20.8172484Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:04:20.8172944Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:04:20.8173378Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:04:20.8173854Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:04:20.8174434Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:04:20.8175058Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:04:20.8175778Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:04:20.8176393Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:04:20.8176949Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:04:20.8177472Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:04:20.8177938Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8178429Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8178997Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:04:20.8179480Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:04:20.8179697Z | ^~~~ 2025-07-17T10:04:20.8180037Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:04:20.8180513Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:04:20.8181028Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:04:20.8181468Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:04:20.8181899Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:04:20.8182367Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:04:20.8182891Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:04:20.8183379Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:04:20.8183873Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:04:20.8184416Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:04:20.8184951Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:04:20.8185497Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8185906Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8186570Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:04:20.8187443Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:04:20.8188391Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:04:20.8189351Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:04:20.8190476Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:04:20.8191599Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:04:20.8195061Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:04:20.8200830Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:04:20.8204027Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:04:20.8204569Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:04:20.8204815Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:04:20.8208237Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:04:20.8211915Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:04:20.8212625Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:04:20.8212971Z | ^~~~~~~~ 2025-07-17T10:04:20.8213347Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:04:20.8213906Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:04:20.8214487Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:04:20.8214947Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:04:20.8215375Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:04:20.8215858Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:04:20.8216442Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:04:20.8217063Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:04:20.8217683Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:04:20.8218295Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:04:20.8218844Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:04:20.8219365Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:04:20.8219839Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8220241Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8220814Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:04:20.8221241Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:04:20.8221466Z | ^~~~ 2025-07-17T10:04:20.8221805Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:04:20.8222284Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:04:20.8222738Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:04:20.8223279Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:04:20.8223706Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:04:20.8224184Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:04:20.8224699Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:04:20.8225183Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:04:20.8225750Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:04:20.8226339Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:04:20.8226879Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:04:20.8227415Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8227802Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8228541Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:04:20.8229418Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:04:20.8230367Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:04:20.8231349Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:04:20.8232376Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:04:20.8233471Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:04:20.8236639Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:04:20.8242090Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:04:20.8245219Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:04:20.8245749Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:04:20.8246057Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:04:20.8249297Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:04:20.8252636Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:04:20.8253343Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:04:20.8253610Z | ^~~~~~~~ 2025-07-17T10:04:20.8253979Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:04:20.8254541Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:04:20.8255038Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:04:20.8255497Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:04:20.8255934Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:04:20.8256473Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:04:20.8257070Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:04:20.8257694Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:04:20.8258327Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:04:20.8258942Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:04:20.8259554Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:04:20.8260083Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:04:20.8260651Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8261055Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8261674Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:04:20.8262107Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:04:20.8262324Z | ^~~~ 2025-07-17T10:04:20.8262667Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:04:20.8263154Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:04:20.8263602Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:04:20.8264038Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:04:20.8264455Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:04:20.8264922Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:04:20.8265497Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:04:20.8265991Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:04:20.8266480Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:04:20.8267088Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:04:20.8267621Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:04:20.8268087Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8268480Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8269134Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:04:20.8270008Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:04:20.8270959Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:04:20.8271920Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:04:20.8272960Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:04:20.8274199Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:04:20.8277452Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:04:20.8282841Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:04:20.8285955Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:04:20.8286482Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:04:20.8286728Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:04:20.8290005Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:04:20.8293369Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:04:20.8294149Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:04:20.8294405Z | ^~~~~~~~ 2025-07-17T10:04:20.8294830Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:04:20.8295393Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:04:20.8295887Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:04:20.8296367Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:04:20.8296797Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:04:20.8297272Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:04:20.8297859Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:04:20.8298482Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:04:20.8299115Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:04:20.8299740Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:04:20.8300366Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:04:20.8300897Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:04:20.8301364Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8301760Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8302331Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:04:20.8302764Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:04:20.8302982Z | ^~~~ 2025-07-17T10:04:20.8303336Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:04:20.8303826Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:04:20.8304270Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:04:20.8304710Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:04:20.8305127Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:04:20.8305719Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:04:20.8306227Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:04:20.8306790Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:04:20.8307291Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:04:20.8307879Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:04:20.8308408Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:04:20.8308940Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8309344Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8309998Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:04:20.8310880Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:04:20.8311827Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:04:20.8312788Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:04:20.8313818Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:04:20.8314958Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:04:20.8318389Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:04:20.8324105Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:04:20.8327369Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:04:20.8327898Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:04:20.8328145Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:04:20.8331578Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:04:20.8335200Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:04:20.8335914Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:04:20.8336175Z | ^~~~~~~~ 2025-07-17T10:04:20.8336551Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:04:20.8337113Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:04:20.8337620Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:04:20.8338080Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:04:20.8338512Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:04:20.8338983Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:04:20.8339572Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:04:20.8340318Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:04:20.8340956Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:04:20.8341630Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:04:20.8342187Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:04:20.8342776Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:04:20.8343254Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8343649Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8344221Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:04:20.8344651Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:04:20.8344876Z | ^~~~ 2025-07-17T10:04:20.8345218Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:04:20.8346117Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:04:20.8346583Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:04:20.8347023Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:04:20.8347446Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:04:20.8347914Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:04:20.8348575Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:04:20.8349076Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:04:20.8349557Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:04:20.8350089Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:04:20.8350621Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:04:20.8351089Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8351481Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8352150Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:04:20.8353026Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:04:20.8353968Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:04:20.8354926Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:04:20.8356057Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:04:20.8357248Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:04:20.8360466Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:04:20.8365826Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:04:20.8368923Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:04:20.8369471Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:04:20.8369727Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:04:20.8373030Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:04:20.8376540Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:04:20.8377258Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:04:20.8377518Z | ^~~~~~~~ 2025-07-17T10:04:20.8377902Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:04:20.8378483Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:04:20.8378978Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:04:20.8379434Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:04:20.8379865Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:04:20.8380343Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:04:20.8380919Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:04:20.8381608Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:04:20.8382235Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:04:20.8382849Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:04:20.8383410Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:04:20.8383944Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:04:20.8384417Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8384816Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8385464Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:04:20.8385904Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:04:20.8386129Z | ^~~~ 2025-07-17T10:04:20.8386475Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:04:20.8386965Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:04:20.8387417Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:04:20.8387851Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:04:20.8388343Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:04:20.8388819Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:04:20.8389405Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:04:20.8389901Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:04:20.8390459Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:04:20.8390980Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:04:20.8391508Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:04:20.8391976Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8392364Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8393028Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:04:20.8393908Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:04:20.8394853Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:04:20.8395818Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:04:20.8396941Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:04:20.8398056Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:04:20.8401225Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:04:20.8406629Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:04:20.8409734Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:04:20.8410265Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:04:20.8410498Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:04:20.8413770Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:04:20.8417238Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:04:20.8417949Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:04:20.8418219Z | ^~~~~~~~ 2025-07-17T10:04:20.8418585Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:04:20.8419147Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:04:20.8419653Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:04:20.8420112Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:04:20.8420540Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:04:20.8421010Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:04:20.8421660Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:04:20.8422367Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:04:20.8422993Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:04:20.8423661Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:04:20.8424228Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:04:20.8424754Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:04:20.8425232Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8425693Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8426266Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:04:20.8426692Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:04:20.8426914Z | ^~~~ 2025-07-17T10:04:20.8427250Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:04:20.8427728Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:04:20.8428179Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:04:20.8428623Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:04:20.8429122Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:04:20.8429597Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:04:20.8430114Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:04:20.8430603Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:04:20.8431088Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:04:20.8431603Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:04:20.8432133Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:04:20.8432602Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8432998Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8433653Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:04:20.8434527Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:04:20.8435546Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:04:20.8436534Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:04:20.8437646Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:04:20.8438840Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:04:20.8442017Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:04:20.8447331Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:04:20.8450423Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:04:20.8450944Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:04:20.8451188Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:04:20.8454542Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:04:20.8458033Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:04:20.8458744Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:04:20.8459006Z | ^~~~~~~~ 2025-07-17T10:04:20.8459370Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:04:20.8459931Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:04:20.8460441Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:04:20.8460906Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:04:20.8461346Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:04:20.8461821Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:04:20.8462478Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:04:20.8463108Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:04:20.8463734Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:04:20.8464353Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:04:20.8464913Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:04:20.8465512Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:04:20.8466002Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8466406Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8466992Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:04:20.8467417Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:04:20.8467629Z | ^~~~ 2025-07-17T10:04:20.8467982Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:04:20.8468459Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:04:20.8468994Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:04:20.8469445Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:04:20.8469934Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:04:20.8470395Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:04:20.8470896Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:04:20.8471466Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:04:20.8471952Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:04:20.8472463Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:04:20.8472984Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:04:20.8473442Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8473824Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8474478Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:04:20.8475351Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:04:20.8476289Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:04:20.8489141Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:04:20.8490317Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:04:20.8491451Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:04:20.8494794Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:04:20.8500152Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:04:20.8503292Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:04:20.8503846Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:04:20.8504106Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:04:20.8507447Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:04:20.8510957Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:04:20.8511678Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:04:20.8511941Z | ^~~~~~~~ 2025-07-17T10:04:20.8512320Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:04:20.8512891Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:04:20.8513399Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:04:20.8513869Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:04:20.8514370Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:04:20.8514857Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:04:20.8515514Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:04:20.8516148Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:04:20.8516861Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:04:20.8517502Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:04:20.8518052Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:04:20.8518581Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:04:20.8519067Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8519486Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8520066Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:04:20.8520501Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:04:20.8520727Z | ^~~~ 2025-07-17T10:04:20.8521072Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:04:20.8521560Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:04:20.8522081Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:04:20.8522529Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:04:20.8522947Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:04:20.8523424Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:04:20.8523943Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:04:20.8524445Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:04:20.8524943Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:04:20.8525471Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:04:20.8526009Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:04:20.8526484Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8526882Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8527548Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:04:20.8528502Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:04:20.8529474Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:04:20.8530504Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:04:20.8531595Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:04:20.8532717Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:04:20.8536094Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:04:20.8541746Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:04:20.8544882Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:04:20.8545464Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:04:20.8545704Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:04:20.8549222Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:04:20.8552779Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:04:20.8553476Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:04:20.8553725Z | ^~~~~~~~ 2025-07-17T10:04:20.8554089Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:04:20.8554638Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:04:20.8555123Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:04:20.8555680Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:04:20.8556115Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:04:20.8556590Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:04:20.8557168Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:04:20.8557792Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:04:20.8558422Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:04:20.8559034Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:04:20.8559581Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:04:20.8560086Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:04:20.8560553Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8560939Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8561485Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:04:20.8562016Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:04:20.8562239Z | ^~~~ 2025-07-17T10:04:20.8562585Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:04:20.8563134Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:04:20.8563596Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:04:20.8564097Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:04:20.8564524Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:04:20.8564994Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:04:20.8565514Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:04:20.8566006Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:04:20.8566493Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:04:20.8567007Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:04:20.8567530Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:04:20.8567990Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8568373Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8569035Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:04:20.8569997Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:04:20.8570941Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:04:20.8571913Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:04:20.8572939Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:04:20.8574038Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:04:20.8577464Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:04:20.8583286Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:04:20.8586639Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:04:20.8587253Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:04:20.8587498Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:04:20.8590931Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:04:20.8594493Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:04:20.8595204Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:04:20.8595476Z | ^~~~~~~~ 2025-07-17T10:04:20.8595949Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:04:20.8596514Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:04:20.8597005Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:04:20.8597539Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:04:20.8597974Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:04:20.8598459Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:04:20.8599055Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:04:20.8599688Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:04:20.8600318Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:04:20.8600939Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:04:20.8601500Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:04:20.8602026Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:04:20.8602493Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8602963Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8603538Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:04:20.8603968Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:04:20.8604189Z | ^~~~ 2025-07-17T10:04:20.8604534Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:04:20.8605015Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:04:20.8605467Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:04:20.8605913Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:04:20.8606341Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:04:20.8606814Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:04:20.8607326Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:04:20.8607817Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:04:20.8608301Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:04:20.8608875Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:04:20.8609407Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:04:20.8609971Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8610365Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8611026Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:04:20.8611972Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:04:20.8612925Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:04:20.8613885Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:04:20.8614909Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:04:20.8616019Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:04:20.8619392Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:04:20.8625183Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:04:20.8628586Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:04:20.8629187Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:04:20.8629424Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:04:20.8632923Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:04:20.8636584Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:04:20.8637298Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:04:20.8637551Z | ^~~~~~~~ 2025-07-17T10:04:20.8637916Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:04:20.8638479Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:04:20.8638981Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:04:20.8639442Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:04:20.8639946Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:04:20.8640420Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:04:20.8641004Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:04:20.8641621Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:04:20.8642250Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:04:20.8642938Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:04:20.8643506Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:04:20.8644033Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:04:20.8644499Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8644947Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8645521Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:04:20.8645947Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:04:20.8646170Z | ^~~~ 2025-07-17T10:04:20.8646499Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:04:20.8646984Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:04:20.8647435Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:04:20.8647879Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:04:20.8648304Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:04:20.8648777Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:04:20.8649282Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:04:20.8649773Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:04:20.8650335Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:04:20.8650854Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:04:20.8651387Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:04:20.8651859Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8652254Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8652906Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:04:20.8653784Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:04:20.8654810Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:04:20.8655771Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:04:20.8656789Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:04:20.8657963Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:04:20.8661430Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:04:20.8667213Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:04:20.8670569Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:04:20.8671105Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:04:20.8671344Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:04:20.8675014Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:04:20.8678688Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:04:20.8679410Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:04:20.8679664Z | ^~~~~~~~ 2025-07-17T10:04:20.8680041Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:04:20.8680606Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:04:20.8681100Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:04:20.8681553Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:04:20.8681979Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:04:20.8682445Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:04:20.8683024Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:04:20.8683725Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:04:20.8684353Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:04:20.8684990Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:04:20.8685551Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:04:20.8686079Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:04:20.8686555Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8686952Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8687587Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:04:20.8688018Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:04:20.8688244Z | ^~~~ 2025-07-17T10:04:20.8688590Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:04:20.8689082Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:04:20.8689531Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:04:20.8690031Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:04:20.8690462Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:04:20.8690936Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:04:20.8691451Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:04:20.8691943Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:04:20.8692487Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:04:20.8693004Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:04:20.8693541Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:04:20.8694008Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8694407Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8695057Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:04:20.8695933Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:04:20.8696881Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:04:20.8697850Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:04:20.8698947Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:04:20.8700055Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:04:20.8703440Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:04:20.8709468Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:04:20.8712736Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:04:20.8713275Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:04:20.8713525Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:04:20.8717028Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:04:20.8720597Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:04:20.8721371Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:04:20.8721622Z | ^~~~~~~~ 2025-07-17T10:04:20.8721983Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:04:20.8722542Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:04:20.8723031Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:04:20.8723538Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:04:20.8723975Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:04:20.8724440Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:04:20.8725013Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:04:20.8725712Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:04:20.8726345Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:04:20.8726959Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:04:20.8727510Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:04:20.8728023Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:04:20.8728493Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8728886Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8729459Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:04:20.8729886Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:04:20.8730098Z | ^~~~ 2025-07-17T10:04:20.8730448Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:04:20.8730930Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:04:20.8731470Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:04:20.8731906Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:04:20.8732328Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:04:20.8732802Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:04:20.8733311Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:04:20.8733803Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:04:20.8734283Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:04:20.8734877Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:04:20.8735405Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:04:20.8735880Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8736277Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8736936Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:04:20.8737866Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:04:20.8738836Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:04:20.8739797Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:04:20.8740900Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:04:20.8742011Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:04:20.8745461Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:04:20.8751290Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:04:20.8754619Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:04:20.8755216Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:04:20.8755454Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:04:20.8758981Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:04:20.8762511Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:04:20.8763213Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:04:20.8763464Z | ^~~~~~~~ 2025-07-17T10:04:20.8763834Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:04:20.8764462Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:04:20.8764970Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:04:20.8765427Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:04:20.8765855Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:04:20.8766330Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:04:20.8766911Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:04:20.8767543Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:04:20.8768173Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:04:20.8768857Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:04:20.8769415Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:04:20.8769945Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:04:20.8770402Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8770857Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8771431Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:04:20.8771870Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:04:20.8772093Z | ^~~~ 2025-07-17T10:04:20.8772431Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:04:20.8772914Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:04:20.8773431Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:04:20.8773885Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:04:20.8774318Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:04:20.8774789Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:04:20.8775315Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:04:20.8775810Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:04:20.8776293Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:04:20.8776810Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:04:20.8777343Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:04:20.8777836Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8778301Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8778965Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:04:20.8779838Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:04:20.8780787Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:04:20.8781751Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:04:20.8782775Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:04:20.8783943Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:04:20.8787557Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:04:20.8793315Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:04:20.8796640Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:04:20.8797171Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:04:20.8797409Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:04:20.8800938Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:04:20.8804653Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:04:20.8805408Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:04:20.8805661Z | ^~~~~~~~ 2025-07-17T10:04:20.8806033Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:04:20.8806652Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:04:20.8807157Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:04:20.8807614Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:04:20.8808045Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:04:20.8808525Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:04:20.8809114Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:04:20.8809754Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:04:20.8810386Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:04:20.8811011Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:04:20.8811578Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:04:20.8812169Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:04:20.8812647Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8813045Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8813611Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:04:20.8814041Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:04:20.8814256Z | ^~~~ 2025-07-17T10:04:20.8814594Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:04:20.8815071Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:04:20.8815527Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:04:20.8816038Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:04:20.8816461Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:04:20.8816940Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:04:20.8817451Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:04:20.8817944Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:04:20.8818492Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:04:20.8819026Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:04:20.8819562Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:04:20.8820037Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8820422Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8821142Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:04:20.8822029Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:04:20.8822979Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:04:20.8823949Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:04:20.8824980Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:04:20.8826162Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:04:20.8829649Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:04:20.8835515Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:04:20.8838845Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:04:20.8839384Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:04:20.8839623Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:04:20.8843089Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:04:20.8846708Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:04:20.8847421Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:04:20.8847675Z | ^~~~~~~~ 2025-07-17T10:04:20.8848058Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:04:20.8848625Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:04:20.8849122Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:04:20.8849652Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:04:20.8850088Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:04:20.8850563Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:04:20.8851144Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:04:20.8851881Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:04:20.8852525Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:04:20.8853147Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:04:20.8853707Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:04:20.8854290Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:04:20.8854766Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8855157Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8855736Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:04:20.8856160Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:04:20.8856381Z | ^~~~ 2025-07-17T10:04:20.8856723Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:04:20.8857203Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:04:20.8857652Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:04:20.8858084Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:04:20.8858501Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:04:20.8858981Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:04:20.8859568Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:04:20.8860076Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:04:20.8860557Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:04:20.8861083Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:04:20.8861641Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:04:20.8862116Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8862515Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8863184Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:04:20.8864132Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:04:20.8865084Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:04:20.8866117Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:04:20.8867247Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:04:20.8868355Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:04:20.8871838Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:04:20.8877635Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:04:20.8880994Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:04:20.8881610Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:04:20.8881859Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:04:20.8885467Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:04:20.8889103Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:04:20.8889817Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:04:20.8890075Z | ^~~~~~~~ 2025-07-17T10:04:20.8890448Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:04:20.8891011Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:04:20.8891514Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:04:20.8891973Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:04:20.8892401Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:04:20.8892874Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:04:20.8893535Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:04:20.8894174Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:04:20.8894814Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:04:20.8895437Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:04:20.8896004Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:04:20.8896537Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:04:20.8897012Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8897498Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8898062Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:04:20.8898494Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:04:20.8898720Z | ^~~~ 2025-07-17T10:04:20.8899066Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:04:20.8899545Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:04:20.8900059Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:04:20.8900507Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:04:20.8900929Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:04:20.8901403Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:04:20.8901926Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:04:20.8902474Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:04:20.8902981Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:04:20.8903502Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:04:20.8904033Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:04:20.8904504Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8904891Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8905616Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:04:20.8906508Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:04:20.8907488Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:04:20.8908566Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:04:20.8909600Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:04:20.8910728Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:04:20.8913984Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:04:20.8919516Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:04:20.8922603Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:04:20.8923145Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:04:20.8923397Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:04:20.8926633Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:04:20.8930050Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:04:20.8930834Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:04:20.8931097Z | ^~~~~~~~ 2025-07-17T10:04:20.8931472Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:04:20.8932032Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:04:20.8932526Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:04:20.8932982Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:04:20.8933529Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:04:20.8934021Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:04:20.8934620Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:04:20.8935252Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:04:20.8935994Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:04:20.8936624Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:04:20.8937190Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:04:20.8937717Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:04:20.8938202Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8938604Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8939170Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:04:20.8939603Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:04:20.8939828Z | ^~~~ 2025-07-17T10:04:20.8940168Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:04:20.8940371Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:04:20.8940683Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:04:20.8940873Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:04:20.8941060Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:04:20.8941294Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:04:20.8941523Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:04:20.8941733Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:04:20.8941959Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:04:20.8942203Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:04:20.8942504Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:04:20.8942682Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8942841Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8943289Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:04:20.8943854Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:04:20.8944396Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:04:20.8944909Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:04:20.8945647Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:04:20.8946261Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:04:20.8949159Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:04:20.8952555Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:04:20.8953160Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:04:20.8953272Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:04:20.8953370Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:04:20.8956740Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:04:20.8957468Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:04:20.8957615Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:04:20.8957681Z | ^~~~~~~~ 2025-07-17T10:04:20.8957957Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:04:20.8958199Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:04:20.8958523Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:04:20.8958720Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:04:20.8958905Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:04:20.8959143Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:04:20.8959442Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:04:20.8959776Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:04:20.8960079Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:04:20.8960408Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:04:20.8960643Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:04:20.8960941Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:04:20.8961129Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8961294Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8961647Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:04:20.8961754Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:04:20.8961824Z | ^~~~ 2025-07-17T10:04:20.8962061Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:04:20.8962268Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:04:20.8962467Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:04:20.8962659Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:04:20.8962840Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:04:20.8963077Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:04:20.8963304Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:04:20.8963521Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:04:20.8963738Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:04:20.8963983Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:04:20.8964216Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:04:20.8964392Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8964558Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8965059Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:04:20.8965555Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:04:20.8966071Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:04:20.8966583Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:04:20.8967223Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:04:20.8967897Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:04:20.8970872Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:04:20.8974143Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:04:20.8974686Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:04:20.8974797Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:04:20.8974883Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:04:20.8978291Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:04:20.8979068Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:04:20.8979214Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:04:20.8979282Z | ^~~~~~~~ 2025-07-17T10:04:20.8979553Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:04:20.8979788Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:04:20.8979998Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:04:20.8980192Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:04:20.8980369Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:04:20.8980610Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:04:20.8980932Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:04:20.8981228Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:04:20.8981531Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:04:20.8981795Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:04:20.8982034Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:04:20.8982267Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:04:20.8982536Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8982688Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8983044Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:04:20.8983138Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:04:20.8983220Z | ^~~~ 2025-07-17T10:04:20.8983440Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:04:20.8983646Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:04:20.8983908Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:04:20.8984102Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:04:20.8984342Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:04:20.8984593Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:04:20.8984886Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:04:20.8985116Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:04:20.8985404Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:04:20.8985660Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:04:20.8985884Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:04:20.8986071Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.8986220Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.8986676Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:04:20.8987166Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:04:20.8987686Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:04:20.8988205Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:04:20.8988774Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:04:20.8989387Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:04:20.8992340Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:04:20.8995746Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:04:20.8996285Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:04:20.8996404Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:04:20.8996482Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:04:20.8999805Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:04:20.9000586Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:04:20.9000732Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:04:20.9000796Z | ^~~~~~~~ 2025-07-17T10:04:20.9001075Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:04:20.9001370Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:04:20.9001578Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:04:20.9001830Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:04:20.9002020Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:04:20.9002252Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:04:20.9002610Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:04:20.9002898Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:04:20.9003216Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:04:20.9003480Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:04:20.9003724Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:04:20.9003948Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:04:20.9004141Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.9004293Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.9004649Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:04:20.9004748Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:04:20.9004831Z | ^~~~ 2025-07-17T10:04:20.9005055Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:04:20.9005261Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:04:20.9005452Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:04:20.9005650Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:04:20.9005823Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:04:20.9006059Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:04:20.9006274Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:04:20.9006580Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:04:20.9006792Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:04:20.9007049Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:04:20.9007280Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:04:20.9007472Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.9007624Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.9008136Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:04:20.9008624Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:04:20.9009214Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:04:20.9009785Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:04:20.9010374Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:04:20.9010989Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:04:20.9013629Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:04:20.9016689Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:04:20.9017159Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:04:20.9017331Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:04:20.9017417Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:04:20.9020626Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:04:20.9021415Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:04:20.9021548Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:04:20.9021616Z | ^~~~~~~~ 2025-07-17T10:04:20.9021881Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:04:20.9022125Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:04:20.9022387Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:04:20.9022607Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:04:20.9022785Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:04:20.9023038Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:04:20.9023324Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:04:20.9023614Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:04:20.9023915Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:04:20.9024185Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:04:20.9024415Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:04:20.9024652Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:04:20.9024831Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.9024993Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.9025469Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:04:20.9025581Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:04:20.9025713Z | ^~~~ 2025-07-17T10:04:20.9025948Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:04:20.9026147Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:04:20.9026347Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:04:20.9026595Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:04:20.9026785Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:04:20.9027026Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:04:20.9027255Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:04:20.9027470Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:04:20.9027688Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:04:20.9027931Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:04:20.9028166Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:04:20.9028345Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.9028505Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.9029009Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:04:20.9029511Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:04:20.9030035Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:04:20.9030540Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:04:20.9031128Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:04:20.9031730Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:04:20.9034697Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:04:20.9038007Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:04:20.9038545Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:04:20.9038651Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:04:20.9038743Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:04:20.9042071Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:04:20.9042831Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:04:20.9042976Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:04:20.9043101Z | ^~~~~~~~ 2025-07-17T10:04:20.9043385Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:04:20.9043618Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:04:20.9043881Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:04:20.9044084Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:04:20.9044263Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:04:20.9044507Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:04:20.9044811Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:04:20.9045100Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:04:20.9045400Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:04:20.9045683Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:04:20.9045921Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:04:20.9046159Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:04:20.9046399Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:04:20.9046565Z from /var/lib/jenkins/pytorch/test/cpp_extensions/rng_extension.cpp:1: 2025-07-17T10:04:20.9046913Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:04:20.9047020Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:04:20.9047090Z | ^~~~ 2025-07-17T10:04:20.9048944Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib /var/lib/jenkins/pytorch/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/rng_extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/rng.cpython-312-x86_64-linux-gnu.so 2025-07-17T10:04:21.3509436Z building 'torch_test_cpp_extension.cuda' extension 2025-07-17T10:04:40.1435865Z [1/3] /opt/rocm/bin/hipcc -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THH -I/opt/rocm/include -I/var/lib/jenkins/pytorch/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c -c /var/lib/jenkins/pytorch/test/cpp_extensions/hip_extension_kernel2.hip -o /var/lib/jenkins/pytorch/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/hip_extension_kernel2.o -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -DCUDA_HAS_FP16=1 -D__HIP_NO_HALF_OPERATORS__=1 -D__HIP_NO_HALF_CONVERSIONS__=1 -DHIP_ENABLE_WARP_SYNC_BUILTINS=1 -O2 -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1016"' -DTORCH_EXTENSION_NAME=cuda --offload-arch=gfx90a --offload-arch=gfx942 -fno-gpu-rdc -std=c++17 2025-07-17T10:04:40.2536593Z [2/3] /opt/rocm/bin/hipcc -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THH -I/opt/rocm/include -I/var/lib/jenkins/pytorch/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c -c /var/lib/jenkins/pytorch/test/cpp_extensions/hip_extension_kernel.hip -o /var/lib/jenkins/pytorch/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/hip_extension_kernel.o -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -DCUDA_HAS_FP16=1 -D__HIP_NO_HALF_OPERATORS__=1 -D__HIP_NO_HALF_CONVERSIONS__=1 -DHIP_ENABLE_WARP_SYNC_BUILTINS=1 -O2 -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1016"' -DTORCH_EXTENSION_NAME=cuda --offload-arch=gfx90a --offload-arch=gfx942 -fno-gpu-rdc -std=c++17 2025-07-17T10:04:40.3927232Z [3/3] c++ -MMD -MF /var/lib/jenkins/pytorch/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/cuda_extension.o.d -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THH -I/opt/rocm/include -I/var/lib/jenkins/pytorch/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c -c /var/lib/jenkins/pytorch/test/cpp_extensions/cuda_extension.cpp -o /var/lib/jenkins/pytorch/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/cuda_extension.o -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -g -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1016"' -DTORCH_EXTENSION_NAME=cuda -std=c++17 2025-07-17T10:04:40.3973851Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib /var/lib/jenkins/pytorch/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/cuda_extension.o /var/lib/jenkins/pytorch/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/hip_extension_kernel.o /var/lib/jenkins/pytorch/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/hip_extension_kernel2.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -L/opt/rocm/lib -L/opt/rocm/hip/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -lamdhip64 -lc10_hip -ltorch_hip -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cuda.cpython-312-x86_64-linux-gnu.so 2025-07-17T10:04:40.9370442Z building 'torch_test_cpp_extension.torch_library' extension 2025-07-17T10:05:22.0691953Z [1/1] /opt/rocm/bin/hipcc -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THH -I/opt/rocm/include -I/var/lib/jenkins/pytorch/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c -c /var/lib/jenkins/pytorch/test/cpp_extensions/torch_library.cu -o /var/lib/jenkins/pytorch/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/torch_library.o -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -DCUDA_HAS_FP16=1 -D__HIP_NO_HALF_OPERATORS__=1 -D__HIP_NO_HALF_CONVERSIONS__=1 -DHIP_ENABLE_WARP_SYNC_BUILTINS=1 -O2 -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1016"' -DTORCH_EXTENSION_NAME=torch_library --offload-arch=gfx90a --offload-arch=gfx942 -fno-gpu-rdc -std=c++17 2025-07-17T10:05:22.0737484Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib /var/lib/jenkins/pytorch/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/torch_library.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -L/opt/rocm/lib -L/opt/rocm/hip/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -lamdhip64 -lc10_hip -ltorch_hip -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/torch_library.cpython-312-x86_64-linux-gnu.so 2025-07-17T10:05:22.4243513Z running install_lib 2025-07-17T10:05:22.4327079Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/torch_library.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2025-07-17T10:05:22.4334663Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cpp.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2025-07-17T10:05:22.4396540Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/rng.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2025-07-17T10:05:22.4464477Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/maia.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2025-07-17T10:05:22.4531415Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cuda.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2025-07-17T10:05:22.4598829Z running install_egg_info 2025-07-17T10:05:22.4766268Z running egg_info 2025-07-17T10:05:22.4833918Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2025-07-17T10:05:22.4837263Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2025-07-17T10:05:22.4838422Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt 2025-07-17T10:05:22.4840260Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2025-07-17T10:05:22.4914666Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-07-17T10:05:22.4921277Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-07-17T10:05:22.4922223Z removing './install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension-0.0.0-py3.12.egg-info' (and everything under it) 2025-07-17T10:05:22.4927455Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension-0.0.0-py3.12.egg-info 2025-07-17T10:05:22.4937793Z running install_scripts 2025-07-17T10:05:24.7183366Z running install 2025-07-17T10:05:24.7184033Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:90: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-07-17T10:05:24.7185149Z !! 2025-07-17T10:05:24.7185438Z 2025-07-17T10:05:24.7185565Z ******************************************************************************** 2025-07-17T10:05:24.7185949Z Please avoid running ``setup.py`` directly. 2025-07-17T10:05:24.7186334Z Instead, use pypa/build, pypa/installer or other 2025-07-17T10:05:24.7186695Z standards-based tools. 2025-07-17T10:05:24.7187039Z 2025-07-17T10:05:24.7187194Z By 2025-Oct-31, you need to update your project and remove deprecated calls 2025-07-17T10:05:24.7187508Z or your builds will no longer be supported. 2025-07-17T10:05:24.7187663Z 2025-07-17T10:05:24.7187881Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-07-17T10:05:24.7188222Z ******************************************************************************** 2025-07-17T10:05:24.7188378Z 2025-07-17T10:05:24.7188458Z !! 2025-07-17T10:05:24.7188622Z self.initialize_options() 2025-07-17T10:05:24.7294815Z running build 2025-07-17T10:05:24.7295042Z running build_ext 2025-07-17T10:05:24.7585865Z building 'no_python_abi_suffix_test' extension 2025-07-17T10:05:24.7587608Z creating /var/lib/jenkins/pytorch/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-312 2025-07-17T10:05:24.9174494Z [1/1] c++ -MMD -MF /var/lib/jenkins/pytorch/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-312/no_python_abi_suffix_test.o.d -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/include/python3.12 -c -c /var/lib/jenkins/pytorch/test/cpp_extensions/no_python_abi_suffix_test/no_python_abi_suffix_test.cpp -o /var/lib/jenkins/pytorch/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-312/no_python_abi_suffix_test.o -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1016"' -DTORCH_EXTENSION_NAME=no_python_abi_suffix_test -std=c++17 2025-07-17T10:05:24.9217518Z creating build/lib.linux-x86_64-cpython-312 2025-07-17T10:05:24.9221138Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib /var/lib/jenkins/pytorch/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-312/no_python_abi_suffix_test.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/no_python_abi_suffix_test.so 2025-07-17T10:05:25.0263175Z running install_lib 2025-07-17T10:05:25.0339181Z creating install/opt/conda/envs/py_3.12/lib/python3.12/site-packages 2025-07-17T10:05:25.0344660Z copying build/lib.linux-x86_64-cpython-312/no_python_abi_suffix_test.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages 2025-07-17T10:05:25.0350689Z running install_egg_info 2025-07-17T10:05:25.0511066Z running egg_info 2025-07-17T10:05:25.0575707Z creating no_python_abi_suffix_test.egg-info 2025-07-17T10:05:25.0576122Z writing no_python_abi_suffix_test.egg-info/PKG-INFO 2025-07-17T10:05:25.0579846Z writing dependency_links to no_python_abi_suffix_test.egg-info/dependency_links.txt 2025-07-17T10:05:25.0581670Z writing top-level names to no_python_abi_suffix_test.egg-info/top_level.txt 2025-07-17T10:05:25.0583351Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2025-07-17T10:05:25.0652946Z reading manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2025-07-17T10:05:25.0658326Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2025-07-17T10:05:25.0659194Z Copying no_python_abi_suffix_test.egg-info to ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/no_python_abi_suffix_test-0.0.0-py3.12.egg-info 2025-07-17T10:05:25.0668649Z running install_scripts 2025-07-17T10:05:27.0374078Z /var/lib/jenkins/pytorch/test/cpp_extensions/python_agnostic_extension/python_agnostic/csrc/ultra_norm.cu -> /var/lib/jenkins/pytorch/test/cpp_extensions/python_agnostic_extension/python_agnostic/csrc/ultra_norm.cu [skipped, no changes] 2025-07-17T10:05:27.0375234Z Successfully preprocessed all matching files. 2025-07-17T10:05:27.0375534Z Total number of unsupported CUDA function calls: 0 2025-07-17T10:05:27.0375699Z 2025-07-17T10:05:27.0375702Z 2025-07-17T10:05:27.0375805Z Total number of replaced kernel launches: 0 2025-07-17T10:05:27.0703671Z running bdist_wheel 2025-07-17T10:05:27.1241607Z running build 2025-07-17T10:05:27.1241852Z running build_ext 2025-07-17T10:05:27.1253959Z building 'python_agnostic._C' extension 2025-07-17T10:05:27.1259325Z creating /var/lib/jenkins/pytorch/test/cpp_extensions/python_agnostic_extension/build/temp.linux-x86_64-cpython-312/python_agnostic/csrc 2025-07-17T10:05:39.0649085Z [1/1] /opt/rocm/bin/hipcc -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THH -I/opt/rocm/include -I/opt/conda/envs/py_3.12/include/python3.12 -c -c /var/lib/jenkins/pytorch/test/cpp_extensions/python_agnostic_extension/python_agnostic/csrc/ultra_norm.cu -o /var/lib/jenkins/pytorch/test/cpp_extensions/python_agnostic_extension/build/temp.linux-x86_64-cpython-312/python_agnostic/csrc/ultra_norm.o -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -DCUDA_HAS_FP16=1 -D__HIP_NO_HALF_OPERATORS__=1 -D__HIP_NO_HALF_CONVERSIONS__=1 -DHIP_ENABLE_WARP_SYNC_BUILTINS=1 -DTORCH_API_INCLUDE_EXTENSION_H -DPy_LIMITED_API=0x03090000 -DTORCH_EXTENSION_NAME=_C --offload-arch=gfx90a --offload-arch=gfx942 -fno-gpu-rdc -std=c++17 2025-07-17T10:05:39.0690174Z creating build/lib.linux-x86_64-cpython-312/python_agnostic 2025-07-17T10:05:39.0694569Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib /var/lib/jenkins/pytorch/test/cpp_extensions/python_agnostic_extension/build/temp.linux-x86_64-cpython-312/python_agnostic/csrc/ultra_norm.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -L/opt/rocm/lib -L/opt/rocm/hip/lib -lc10 -ltorch -ltorch_cpu -lamdhip64 -lc10_hip -ltorch_hip -o build/lib.linux-x86_64-cpython-312/python_agnostic/_C.so 2025-07-17T10:05:39.4696054Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:90: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-07-17T10:05:39.4696596Z !! 2025-07-17T10:05:39.4696699Z 2025-07-17T10:05:39.4697522Z ******************************************************************************** 2025-07-17T10:05:39.4698837Z Please avoid running ``setup.py`` directly. 2025-07-17T10:05:39.4699246Z Instead, use pypa/build, pypa/installer or other 2025-07-17T10:05:39.4699754Z standards-based tools. 2025-07-17T10:05:39.4699881Z 2025-07-17T10:05:39.4700032Z By 2025-Oct-31, you need to update your project and remove deprecated calls 2025-07-17T10:05:39.4700341Z or your builds will no longer be supported. 2025-07-17T10:05:39.4700500Z 2025-07-17T10:05:39.4700704Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-07-17T10:05:39.4701186Z ******************************************************************************** 2025-07-17T10:05:39.4701348Z 2025-07-17T10:05:39.4701408Z !! 2025-07-17T10:05:39.4701572Z self.initialize_options() 2025-07-17T10:05:39.4759065Z installing to build/bdist.linux-x86_64/wheel 2025-07-17T10:05:39.4759436Z running install 2025-07-17T10:05:39.4797712Z running install_lib 2025-07-17T10:05:39.4866210Z creating build/bdist.linux-x86_64/wheel 2025-07-17T10:05:39.4868443Z creating build/bdist.linux-x86_64/wheel/python_agnostic 2025-07-17T10:05:39.4869146Z copying build/lib.linux-x86_64-cpython-312/python_agnostic/_C.so -> build/bdist.linux-x86_64/wheel/./python_agnostic 2025-07-17T10:05:39.4870651Z running install_egg_info 2025-07-17T10:05:39.4941782Z running egg_info 2025-07-17T10:05:39.5005582Z creating python_agnostic.egg-info 2025-07-17T10:05:39.5006106Z writing python_agnostic.egg-info/PKG-INFO 2025-07-17T10:05:39.5010711Z writing dependency_links to python_agnostic.egg-info/dependency_links.txt 2025-07-17T10:05:39.5012543Z writing top-level names to python_agnostic.egg-info/top_level.txt 2025-07-17T10:05:39.5013907Z writing manifest file 'python_agnostic.egg-info/SOURCES.txt' 2025-07-17T10:05:39.5084126Z reading manifest file 'python_agnostic.egg-info/SOURCES.txt' 2025-07-17T10:05:39.5089266Z writing manifest file 'python_agnostic.egg-info/SOURCES.txt' 2025-07-17T10:05:39.5089975Z Copying python_agnostic.egg-info to build/bdist.linux-x86_64/wheel/./python_agnostic-0.0-py3.12.egg-info 2025-07-17T10:05:39.5098753Z running install_scripts 2025-07-17T10:05:39.5189809Z creating build/bdist.linux-x86_64/wheel/python_agnostic-0.0.dist-info/WHEEL 2025-07-17T10:05:39.5194444Z creating 'dist/python_agnostic-0.0-cp39-abi3-linux_x86_64.whl' and adding 'build/bdist.linux-x86_64/wheel' to it 2025-07-17T10:05:39.5212096Z adding 'python_agnostic/_C.so' 2025-07-17T10:05:39.5213714Z adding 'python_agnostic-0.0.dist-info/METADATA' 2025-07-17T10:05:39.5214932Z adding 'python_agnostic-0.0.dist-info/WHEEL' 2025-07-17T10:05:39.5215824Z adding 'python_agnostic-0.0.dist-info/top_level.txt' 2025-07-17T10:05:39.5217286Z adding 'python_agnostic-0.0.dist-info/RECORD' 2025-07-17T10:05:39.5217877Z removing build/bdist.linux-x86_64/wheel 2025-07-17T10:05:41.5149057Z running install 2025-07-17T10:05:41.5149722Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:90: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-07-17T10:05:41.5150198Z !! 2025-07-17T10:05:41.5150311Z 2025-07-17T10:05:41.5150402Z ******************************************************************************** 2025-07-17T10:05:41.5150672Z Please avoid running ``setup.py`` directly. 2025-07-17T10:05:41.5150951Z Instead, use pypa/build, pypa/installer or other 2025-07-17T10:05:41.5151192Z standards-based tools. 2025-07-17T10:05:41.5151324Z 2025-07-17T10:05:41.5151474Z By 2025-Oct-31, you need to update your project and remove deprecated calls 2025-07-17T10:05:41.5151771Z or your builds will no longer be supported. 2025-07-17T10:05:41.5151926Z 2025-07-17T10:05:41.5152145Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-07-17T10:05:41.5152462Z ******************************************************************************** 2025-07-17T10:05:41.5152607Z 2025-07-17T10:05:41.5152674Z !! 2025-07-17T10:05:41.5153424Z self.initialize_options() 2025-07-17T10:05:41.5256956Z running build 2025-07-17T10:05:41.5257221Z running build_py 2025-07-17T10:05:41.5330550Z creating build/lib.linux-x86_64-cpython-312/libtorch_agnostic 2025-07-17T10:05:41.5331869Z copying libtorch_agnostic/__init__.py -> build/lib.linux-x86_64-cpython-312/libtorch_agnostic 2025-07-17T10:05:41.5334969Z copying libtorch_agnostic/ops.py -> build/lib.linux-x86_64-cpython-312/libtorch_agnostic 2025-07-17T10:05:41.5341339Z running build_ext 2025-07-17T10:05:41.5636539Z building 'libtorch_agnostic._C' extension 2025-07-17T10:05:41.5637805Z creating /var/lib/jenkins/pytorch/test/cpp_extensions/libtorch_agnostic_extension/build/temp.linux-x86_64-cpython-312/var/lib/jenkins/pytorch/test/cpp_extensions/libtorch_agnostic_extension/libtorch_agnostic/csrc 2025-07-17T10:05:42.3346307Z [1/1] c++ -MMD -MF /var/lib/jenkins/pytorch/test/cpp_extensions/libtorch_agnostic_extension/build/temp.linux-x86_64-cpython-312/var/lib/jenkins/pytorch/test/cpp_extensions/libtorch_agnostic_extension/libtorch_agnostic/csrc/kernel.o.d -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/include/python3.12 -c -c /var/lib/jenkins/pytorch/test/cpp_extensions/libtorch_agnostic_extension/libtorch_agnostic/csrc/kernel.cpp -o /var/lib/jenkins/pytorch/test/cpp_extensions/libtorch_agnostic_extension/build/temp.linux-x86_64-cpython-312/var/lib/jenkins/pytorch/test/cpp_extensions/libtorch_agnostic_extension/libtorch_agnostic/csrc/kernel.o -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -fdiagnostics-color=always -DTORCH_API_INCLUDE_EXTENSION_H -DPy_LIMITED_API=0x03090000 -DTORCH_EXTENSION_NAME=_C -std=c++17 2025-07-17T10:05:42.3394842Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib /var/lib/jenkins/pytorch/test/cpp_extensions/libtorch_agnostic_extension/build/temp.linux-x86_64-cpython-312/var/lib/jenkins/pytorch/test/cpp_extensions/libtorch_agnostic_extension/libtorch_agnostic/csrc/kernel.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -o build/lib.linux-x86_64-cpython-312/libtorch_agnostic/_C.so 2025-07-17T10:05:42.6190784Z running install_lib 2025-07-17T10:05:42.6271584Z creating install/opt/conda/envs/py_3.12/lib/python3.12/site-packages 2025-07-17T10:05:42.6277187Z creating install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/libtorch_agnostic 2025-07-17T10:05:42.6278556Z copying build/lib.linux-x86_64-cpython-312/libtorch_agnostic/ops.py -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/libtorch_agnostic 2025-07-17T10:05:42.6280845Z copying build/lib.linux-x86_64-cpython-312/libtorch_agnostic/_C.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/libtorch_agnostic 2025-07-17T10:05:42.6283066Z copying build/lib.linux-x86_64-cpython-312/libtorch_agnostic/__init__.py -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/libtorch_agnostic 2025-07-17T10:05:42.6288500Z byte-compiling ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/libtorch_agnostic/ops.py to ops.cpython-312.pyc 2025-07-17T10:05:42.6294816Z byte-compiling ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/libtorch_agnostic/__init__.py to __init__.cpython-312.pyc 2025-07-17T10:05:42.6298205Z running install_egg_info 2025-07-17T10:05:42.6462891Z running egg_info 2025-07-17T10:05:42.6526221Z creating libtorch_agnostic.egg-info 2025-07-17T10:05:42.6526537Z writing libtorch_agnostic.egg-info/PKG-INFO 2025-07-17T10:05:42.6530956Z writing dependency_links to libtorch_agnostic.egg-info/dependency_links.txt 2025-07-17T10:05:42.6531697Z writing requirements to libtorch_agnostic.egg-info/requires.txt 2025-07-17T10:05:42.6532951Z writing top-level names to libtorch_agnostic.egg-info/top_level.txt 2025-07-17T10:05:42.6534408Z writing manifest file 'libtorch_agnostic.egg-info/SOURCES.txt' 2025-07-17T10:05:42.6609507Z reading manifest file 'libtorch_agnostic.egg-info/SOURCES.txt' 2025-07-17T10:05:42.6614266Z writing manifest file 'libtorch_agnostic.egg-info/SOURCES.txt' 2025-07-17T10:05:42.6614841Z Copying libtorch_agnostic.egg-info to ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/libtorch_agnostic-0.0-py3.12.egg-info 2025-07-17T10:05:42.6625021Z running install_scripts 2025-07-17T10:05:43.3694491Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:05:43.3695344Z Executing ['/opt/conda/envs/py_3.12/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 10:05:43.369247] 2025-07-17T10:05:47.3474960Z 2025-07-17T10:05:47.3476071Z 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_82dd592316e19238_.log 2025-07-17T10:05:47.3481684Z 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 2025-07-17T10:05:47.3487492Z 2025-07-17T10:05:47.3487679Z Running test_cpp_extensions_aot_no_ninja 1/1 ... [2025-07-17 10:05:47.347758] 2025-07-17T10:05:49.8974990Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T10:05:49.8975940Z import pkg_resources 2025-07-17T10:05:49.9296498Z /var/lib/jenkins/pytorch/test/cpp_extensions/cuda_extension.cpp -> /var/lib/jenkins/pytorch/test/cpp_extensions/cuda_extension.cpp [skipped, no changes] 2025-07-17T10:05:49.9299371Z /var/lib/jenkins/pytorch/test/cpp_extensions/cuda_extension_kernel.cu -> /var/lib/jenkins/pytorch/test/cpp_extensions/hip_extension_kernel.hip [skipped, already hipified] 2025-07-17T10:05:49.9300506Z /var/lib/jenkins/pytorch/test/cpp_extensions/cuda_extension_kernel2.cu -> /var/lib/jenkins/pytorch/test/cpp_extensions/hip_extension_kernel2.hip [skipped, already hipified] 2025-07-17T10:05:49.9301362Z Successfully preprocessed all matching files. 2025-07-17T10:05:49.9301650Z Total number of unsupported CUDA function calls: 0 2025-07-17T10:05:49.9301813Z 2025-07-17T10:05:49.9301963Z 2025-07-17T10:05:49.9302065Z Total number of replaced kernel launches: 2 2025-07-17T10:05:49.9344705Z /var/lib/jenkins/pytorch/test/cpp_extensions/torch_library.cu -> /var/lib/jenkins/pytorch/test/cpp_extensions/torch_library.cu [skipped, no changes] 2025-07-17T10:05:49.9345450Z Successfully preprocessed all matching files. 2025-07-17T10:05:49.9345758Z Total number of unsupported CUDA function calls: 0 2025-07-17T10:05:49.9345922Z 2025-07-17T10:05:49.9345925Z 2025-07-17T10:05:49.9346024Z Total number of replaced kernel launches: 0 2025-07-17T10:05:49.9704845Z running install 2025-07-17T10:05:49.9705563Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:90: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-07-17T10:05:49.9706058Z !! 2025-07-17T10:05:49.9706142Z 2025-07-17T10:05:49.9706230Z ******************************************************************************** 2025-07-17T10:05:49.9706503Z Please avoid running ``setup.py`` directly. 2025-07-17T10:05:49.9706771Z Instead, use pypa/build, pypa/installer or other 2025-07-17T10:05:49.9707015Z standards-based tools. 2025-07-17T10:05:49.9707147Z 2025-07-17T10:05:49.9707299Z By 2025-Oct-31, you need to update your project and remove deprecated calls 2025-07-17T10:05:49.9707607Z or your builds will no longer be supported. 2025-07-17T10:05:49.9707763Z 2025-07-17T10:05:49.9708402Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-07-17T10:05:49.9708731Z ******************************************************************************** 2025-07-17T10:05:49.9708880Z 2025-07-17T10:05:49.9708941Z !! 2025-07-17T10:05:49.9709100Z self.initialize_options() 2025-07-17T10:05:49.9814480Z running build 2025-07-17T10:05:49.9814698Z running build_py 2025-07-17T10:05:49.9890110Z creating build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension 2025-07-17T10:05:49.9892872Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension 2025-07-17T10:05:49.9898518Z running build_ext 2025-07-17T10:05:49.9910408Z building 'torch_test_cpp_extension.cpp' extension 2025-07-17T10:05:49.9911074Z creating build/temp.linux-x86_64-cpython-312 2025-07-17T10:05:49.9915103Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c extension.cpp -o build/temp.linux-x86_64-cpython-312/extension.o -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1016\" -DTORCH_EXTENSION_NAME=cpp -std=c++17 2025-07-17T10:05:50.1703837Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/Exceptions.h:12, 2025-07-17T10:05:50.1704512Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11, 2025-07-17T10:05:50.1705700Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:9, 2025-07-17T10:05:50.1706056Z from extension.cpp:1: 2025-07-17T10:05:50.1707246Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_’: 2025-07-17T10:05:50.1707757Z extension.cpp:45:53: required from here 2025-07-17T10:05:50.1708761Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h:1539:7: warning: ‘pybind11::class_’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes] 2025-07-17T10:05:50.1709508Z 1539 | class class_ : public detail::generic_type { 2025-07-17T10:05:50.1709735Z | ^~~~~~ 2025-07-17T10:05:50.1710675Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]’: 2025-07-17T10:05:50.1711443Z extension.cpp:45:53: required from here 2025-07-17T10:05:50.1712738Z /opt/conda/envs/py_3.12/lib/python3.12/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] 2025-07-17T10:05:50.1713785Z 1599 | with_internals([&](internals &internals) { 2025-07-17T10:05:50.1714043Z | ^~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:05:50.1714362Z 1600 | auto &instances = record.module_local ? get_local_internals().registered_types_cpp 2025-07-17T10:05:50.1714713Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:05:50.1715118Z 1601 | : internals.registered_types_cpp; 2025-07-17T10:05:50.1715382Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:05:50.1715646Z 1602 | instances[std::type_index(typeid(type_alias))] 2025-07-17T10:05:50.1715898Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:05:50.1716146Z 1603 | = instances[std::type_index(typeid(type))]; 2025-07-17T10:05:50.1716404Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:05:50.1716620Z 1604 | }); 2025-07-17T10:05:50.1716794Z | ~ 2025-07-17T10:05:50.1785881Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cpp.cpython-312-x86_64-linux-gnu.so 2025-07-17T10:05:50.7330764Z building 'torch_test_cpp_extension.maia' extension 2025-07-17T10:05:50.7333419Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c maia_extension.cpp -o build/temp.linux-x86_64-cpython-312/maia_extension.o -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1016\" -DTORCH_EXTENSION_NAME=maia -std=c++17 2025-07-17T10:05:50.9202312Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/maia_extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/maia.cpython-312-x86_64-linux-gnu.so 2025-07-17T10:05:51.4127121Z building 'torch_test_cpp_extension.rng' extension 2025-07-17T10:05:51.4129239Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c rng_extension.cpp -o build/temp.linux-x86_64-cpython-312/rng_extension.o -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1016\" -DTORCH_EXTENSION_NAME=rng -std=c++17 2025-07-17T10:05:51.5974772Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-07-17T10:05:51.5975417Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-07-17T10:05:51.5975921Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-07-17T10:05:51.5976469Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-07-17T10:05:51.5976888Z from rng_extension.cpp:6: 2025-07-17T10:05:51.5977754Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1458: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-07-17T10:05:51.5978265Z 1458 | #pragma unroll 2025-07-17T10:05:51.5978490Z | 2025-07-17T10:05:51.5978894Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_convert.h:4, 2025-07-17T10:05:51.5979600Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1510, 2025-07-17T10:05:51.5980145Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-07-17T10:05:51.5980673Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-07-17T10:05:51.5981222Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-07-17T10:05:51.5981761Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-07-17T10:05:51.5982147Z from rng_extension.cpp:6: 2025-07-17T10:05:51.5983182Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:59: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-07-17T10:05:51.5983694Z 59 | #pragma unroll 2025-07-17T10:05:51.5984012Z | 2025-07-17T10:05:51.5984559Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:72: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-07-17T10:05:51.5985042Z 72 | #pragma unroll 2025-07-17T10:05:51.5985209Z | 2025-07-17T10:05:51.5985952Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:87: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-07-17T10:05:51.5986406Z 87 | #pragma unroll 2025-07-17T10:05:51.5986592Z | 2025-07-17T10:05:51.5986911Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1511, 2025-07-17T10:05:51.5987502Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-07-17T10:05:51.5987975Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-07-17T10:05:51.5988425Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-07-17T10:05:51.5988937Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-07-17T10:05:51.5989321Z from rng_extension.cpp:6: 2025-07-17T10:05:51.5989893Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_mask.h:160: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-07-17T10:05:51.5990346Z 160 | #pragma unroll 2025-07-17T10:05:51.5990514Z | 2025-07-17T10:05:51.5990823Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:05:51.5991323Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:05:51.5991914Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:05:51.5992361Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:05:51.5992787Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:05:51.5993264Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:05:51.5993775Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:05:51.5994268Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:05:51.5994755Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:05:51.5995275Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:05:51.5995832Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:05:51.5996314Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.5996644Z from rng_extension.cpp:1: 2025-07-17T10:05:51.5997354Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:05:51.5998344Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:05:51.5999317Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:05:51.6000417Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:05:51.6001508Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:05:51.6002658Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:05:51.6006094Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:05:51.6011918Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:05:51.6015211Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:05:51.6015771Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:05:51.6016023Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:05:51.6019608Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:05:51.6023293Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:05:51.6024008Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:05:51.6024268Z | ^~~~~~~~ 2025-07-17T10:05:51.6024637Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:05:51.6025207Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:05:51.6025855Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:05:51.6026320Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:05:51.6026752Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:05:51.6027231Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:05:51.6027811Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:05:51.6028435Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:05:51.6029073Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:05:51.6029687Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:05:51.6030251Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:05:51.6030779Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:05:51.6031263Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6031587Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6032155Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:05:51.6032601Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:05:51.6032835Z | ^~~~ 2025-07-17T10:05:51.6033272Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:05:51.6033754Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:05:51.6034203Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:05:51.6034711Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:05:51.6035137Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:05:51.6035614Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:05:51.6036133Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:05:51.6036634Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:05:51.6037119Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:05:51.6037636Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:05:51.6038174Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:05:51.6038638Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6038959Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6039547Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:05:51.6040511Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:05:51.6041459Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:05:51.6042454Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:05:51.6043503Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:05:51.6044610Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:05:51.6048032Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:05:51.6053826Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:05:51.6057043Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:05:51.6057632Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:05:51.6057883Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:05:51.6061285Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:05:51.6064769Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:05:51.6065607Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:05:51.6065868Z | ^~~~~~~~ 2025-07-17T10:05:51.6066242Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:05:51.6066875Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:05:51.6067374Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:05:51.6067909Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:05:51.6068341Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:05:51.6068807Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:05:51.6069424Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:05:51.6070058Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:05:51.6070691Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:05:51.6071305Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:05:51.6071866Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:05:51.6072389Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:05:51.6072867Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6073277Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6073772Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:05:51.6074205Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:05:51.6074430Z | ^~~~ 2025-07-17T10:05:51.6074765Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:05:51.6075250Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:05:51.6075696Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:05:51.6076130Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:05:51.6076558Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:05:51.6077037Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:05:51.6077557Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:05:51.6078051Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:05:51.6078541Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:05:51.6079064Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:05:51.6079661Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:05:51.6080134Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6080694Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6081272Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:05:51.6082143Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:05:51.6083164Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:05:51.6084150Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:05:51.6085182Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:05:51.6086285Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:05:51.6089440Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:05:51.6094822Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:05:51.6097870Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:05:51.6098455Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:05:51.6098692Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:05:51.6102003Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:05:51.6105453Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:05:51.6106171Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:05:51.6106515Z | ^~~~~~~~ 2025-07-17T10:05:51.6106890Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:05:51.6107461Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:05:51.6107968Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:05:51.6108439Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:05:51.6108880Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:05:51.6109354Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:05:51.6109937Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:05:51.6110573Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:05:51.6111200Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:05:51.6111815Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:05:51.6112370Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:05:51.6112885Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:05:51.6113426Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6113767Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6114323Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:05:51.6114747Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:05:51.6114971Z | ^~~~ 2025-07-17T10:05:51.6115311Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:05:51.6115863Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:05:51.6116329Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:05:51.6116781Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:05:51.6117204Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:05:51.6117689Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:05:51.6118206Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:05:51.6118703Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:05:51.6119196Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:05:51.6119719Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:05:51.6120255Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:05:51.6120791Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6121120Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6121697Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:05:51.6122572Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:05:51.6123513Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:05:51.6124480Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:05:51.6125503Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:05:51.6126607Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:05:51.6129840Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:05:51.6135221Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:05:51.6138292Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:05:51.6138845Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:05:51.6139097Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:05:51.6142284Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:05:51.6145734Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:05:51.6146515Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:05:51.6146792Z | ^~~~~~~~ 2025-07-17T10:05:51.6147152Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:05:51.6147782Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:05:51.6148286Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:05:51.6148805Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:05:51.6149244Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:05:51.6149719Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:05:51.6150300Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:05:51.6150930Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:05:51.6151553Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:05:51.6152168Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:05:51.6152725Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:05:51.6153246Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:05:51.6153715Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6154136Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6154621Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:05:51.6155043Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:05:51.6155268Z | ^~~~ 2025-07-17T10:05:51.6155614Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:05:51.6156099Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:05:51.6156553Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:05:51.6156997Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:05:51.6157421Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:05:51.6157894Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:05:51.6158409Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:05:51.6158906Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:05:51.6159392Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:05:51.6159911Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:05:51.6160520Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:05:51.6161006Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6161382Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6161947Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:05:51.6162872Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:05:51.6163830Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:05:51.6164802Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:05:51.6165830Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:05:51.6166921Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:05:51.6170245Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:05:51.6175984Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:05:51.6179235Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:05:51.6179769Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:05:51.6180014Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:05:51.6183462Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:05:51.6187012Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:05:51.6187798Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:05:51.6188055Z | ^~~~~~~~ 2025-07-17T10:05:51.6188429Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:05:51.6188992Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:05:51.6189481Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:05:51.6189945Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:05:51.6190377Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:05:51.6190852Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:05:51.6191436Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:05:51.6192058Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:05:51.6192683Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:05:51.6193292Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:05:51.6193928Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:05:51.6194455Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:05:51.6194997Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6195326Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6195808Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:05:51.6196301Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:05:51.6196529Z | ^~~~ 2025-07-17T10:05:51.6196873Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:05:51.6197359Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:05:51.6197824Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:05:51.6198268Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:05:51.6198689Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:05:51.6199156Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:05:51.6199669Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:05:51.6200159Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:05:51.6200645Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:05:51.6201227Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:05:51.6201785Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:05:51.6202256Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6202575Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6203165Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:05:51.6204055Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:05:51.6205022Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:05:51.6205991Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:05:51.6207019Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:05:51.6208116Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:05:51.6211352Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:05:51.6216762Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:05:51.6219930Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:05:51.6220475Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:05:51.6220724Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:05:51.6223961Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:05:51.6227380Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:05:51.6228177Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:05:51.6228435Z | ^~~~~~~~ 2025-07-17T10:05:51.6228801Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:05:51.6229433Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:05:51.6229935Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:05:51.6230401Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:05:51.6230835Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:05:51.6231305Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:05:51.6231889Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:05:51.6232522Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:05:51.6233151Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:05:51.6233766Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:05:51.6234325Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:05:51.6234923Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:05:51.6235409Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6235736Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6236229Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:05:51.6236656Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:05:51.6236877Z | ^~~~ 2025-07-17T10:05:51.6237216Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:05:51.6237698Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:05:51.6238154Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:05:51.6238591Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:05:51.6239006Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:05:51.6239475Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:05:51.6239981Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:05:51.6240473Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:05:51.6241028Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:05:51.6241553Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:05:51.6242153Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:05:51.6242622Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6242948Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6243595Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:05:51.6244481Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:05:51.6245432Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:05:51.6246414Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:05:51.6247440Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:05:51.6248545Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:05:51.6251699Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:05:51.6257277Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:05:51.6260403Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:05:51.6260993Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:05:51.6261237Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:05:51.6264483Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:05:51.6267954Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:05:51.6268666Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:05:51.6268928Z | ^~~~~~~~ 2025-07-17T10:05:51.6269291Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:05:51.6269850Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:05:51.6270350Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:05:51.6270812Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:05:51.6271251Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:05:51.6271816Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:05:51.6272403Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:05:51.6273057Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:05:51.6273699Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:05:51.6274422Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:05:51.6274989Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:05:51.6275514Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:05:51.6275990Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6276317Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6276883Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:05:51.6277320Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:05:51.6277545Z | ^~~~ 2025-07-17T10:05:51.6277884Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:05:51.6278367Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:05:51.6278833Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:05:51.6279286Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:05:51.6279691Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:05:51.6280163Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:05:51.6280673Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:05:51.6281157Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:05:51.6281635Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:05:51.6282229Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:05:51.6282766Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:05:51.6283232Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6283549Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6296102Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:05:51.6297086Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:05:51.6298068Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:05:51.6299196Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:05:51.6300244Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:05:51.6301447Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:05:51.6304889Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:05:51.6310644Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:05:51.6313947Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:05:51.6314506Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:05:51.6314750Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:05:51.6318272Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:05:51.6321869Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:05:51.6322649Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:05:51.6322925Z | ^~~~~~~~ 2025-07-17T10:05:51.6323297Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:05:51.6323884Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:05:51.6324395Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:05:51.6324867Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:05:51.6325302Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:05:51.6325777Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:05:51.6326376Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:05:51.6327031Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:05:51.6327664Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:05:51.6328496Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:05:51.6329072Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:05:51.6329603Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:05:51.6330086Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6330417Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6330921Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:05:51.6331366Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:05:51.6331598Z | ^~~~ 2025-07-17T10:05:51.6331936Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:05:51.6332498Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:05:51.6332962Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:05:51.6333416Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:05:51.6333845Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:05:51.6334331Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:05:51.6334910Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:05:51.6335424Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:05:51.6335919Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:05:51.6336441Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:05:51.6337044Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:05:51.6337536Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6337867Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6338462Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:05:51.6339354Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:05:51.6340314Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:05:51.6341286Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:05:51.6342315Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:05:51.6343517Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:05:51.6346767Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:05:51.6352362Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:05:51.6355408Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:05:51.6355954Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:05:51.6356210Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:05:51.6359429Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:05:51.6362886Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:05:51.6363614Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:05:51.6363881Z | ^~~~~~~~ 2025-07-17T10:05:51.6364257Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:05:51.6364829Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:05:51.6365340Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:05:51.6365898Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:05:51.6366337Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:05:51.6366819Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:05:51.6367413Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:05:51.6368084Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:05:51.6368848Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:05:51.6369491Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:05:51.6370065Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:05:51.6370636Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:05:51.6371208Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6371552Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6372056Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:05:51.6372511Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:05:51.6372737Z | ^~~~ 2025-07-17T10:05:51.6373092Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:05:51.6373585Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:05:51.6374044Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:05:51.6374494Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:05:51.6374927Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:05:51.6375404Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:05:51.6375928Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:05:51.6376517Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:05:51.6377015Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:05:51.6377538Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:05:51.6378080Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:05:51.6378562Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6378893Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6379490Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:05:51.6380388Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:05:51.6381430Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:05:51.6382405Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:05:51.6383489Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:05:51.6384627Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:05:51.6387931Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:05:51.6393243Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:05:51.6396404Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:05:51.6396976Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:05:51.6397222Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:05:51.6400485Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:05:51.6403977Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:05:51.6404687Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:05:51.6404951Z | ^~~~~~~~ 2025-07-17T10:05:51.6405322Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:05:51.6405896Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:05:51.6406406Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:05:51.6406873Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:05:51.6407304Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:05:51.6407784Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:05:51.6408368Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:05:51.6409002Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:05:51.6409634Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:05:51.6410337Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:05:51.6410901Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:05:51.6411428Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:05:51.6411898Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6412226Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6412723Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:05:51.6413161Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:05:51.6413392Z | ^~~~ 2025-07-17T10:05:51.6413807Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:05:51.6414288Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:05:51.6414747Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:05:51.6415189Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:05:51.6415611Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:05:51.6416163Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:05:51.6416690Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:05:51.6417204Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:05:51.6417695Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:05:51.6418211Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:05:51.6418821Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:05:51.6419294Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6419613Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6420202Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:05:51.6421085Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:05:51.6422030Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:05:51.6423008Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:05:51.6424033Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:05:51.6425225Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:05:51.6428472Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:05:51.6434032Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:05:51.6437037Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:05:51.6437569Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:05:51.6437824Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:05:51.6441027Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:05:51.6444412Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:05:51.6445121Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:05:51.6445377Z | ^~~~~~~~ 2025-07-17T10:05:51.6445746Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:05:51.6446323Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:05:51.6446900Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:05:51.6447356Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:05:51.6447787Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:05:51.6448254Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:05:51.6448837Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:05:51.6449537Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:05:51.6450169Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:05:51.6450782Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:05:51.6451334Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:05:51.6451933Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:05:51.6452410Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6452736Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6453228Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:05:51.6453657Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:05:51.6453876Z | ^~~~ 2025-07-17T10:05:51.6454222Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:05:51.6454706Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:05:51.6455164Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:05:51.6455607Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:05:51.6456028Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:05:51.6456492Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:05:51.6457070Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:05:51.6457567Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:05:51.6458054Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:05:51.6458580Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:05:51.6459111Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:05:51.6459580Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6459901Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6460492Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:05:51.6461432Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:05:51.6462384Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:05:51.6463348Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:05:51.6464432Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:05:51.6465659Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:05:51.6468895Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:05:51.6474175Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:05:51.6477279Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:05:51.6477801Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:05:51.6478044Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:05:51.6481444Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:05:51.6484918Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:05:51.6485646Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:05:51.6485903Z | ^~~~~~~~ 2025-07-17T10:05:51.6486270Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:05:51.6486834Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:05:51.6487331Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:05:51.6487787Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:05:51.6488218Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:05:51.6488692Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:05:51.6489276Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:05:51.6489904Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:05:51.6490598Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:05:51.6491208Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:05:51.6491764Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:05:51.6492290Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:05:51.6492753Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6493077Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6493580Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:05:51.6494010Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:05:51.6494309Z | ^~~~ 2025-07-17T10:05:51.6494650Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:05:51.6495127Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:05:51.6495585Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:05:51.6496055Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:05:51.6496475Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:05:51.6497023Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:05:51.6497543Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:05:51.6498037Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:05:51.6498513Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:05:51.6499106Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:05:51.6499637Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:05:51.6500103Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6500426Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6501003Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:05:51.6501887Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:05:51.6502845Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:05:51.6503809Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:05:51.6504836Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:05:51.6506119Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:05:51.6509453Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:05:51.6515265Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:05:51.6518459Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:05:51.6518997Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:05:51.6519234Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:05:51.6522638Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:05:51.6526152Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:05:51.6526860Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:05:51.6527116Z | ^~~~~~~~ 2025-07-17T10:05:51.6527487Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:05:51.6528118Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:05:51.6528615Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:05:51.6529072Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:05:51.6529498Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:05:51.6530020Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:05:51.6530601Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:05:51.6531231Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:05:51.6531855Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:05:51.6532520Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:05:51.6533078Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:05:51.6533601Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:05:51.6534067Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6534391Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6534880Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:05:51.6535307Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:05:51.6535530Z | ^~~~ 2025-07-17T10:05:51.6535866Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:05:51.6536349Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:05:51.6536802Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:05:51.6537245Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:05:51.6537748Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:05:51.6538221Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:05:51.6538735Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:05:51.6539236Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:05:51.6539724Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:05:51.6540246Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:05:51.6540779Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:05:51.6541251Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6541633Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6542207Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:05:51.6543088Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:05:51.6544047Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:05:51.6545074Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:05:51.6546248Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:05:51.6547430Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:05:51.6550827Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:05:51.6556553Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:05:51.6559873Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:05:51.6560405Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:05:51.6560651Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:05:51.6564194Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:05:51.6567734Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:05:51.6568443Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:05:51.6568701Z | ^~~~~~~~ 2025-07-17T10:05:51.6569073Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:05:51.6569625Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:05:51.6570122Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:05:51.6570592Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:05:51.6571087Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:05:51.6571566Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:05:51.6572163Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:05:51.6572802Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:05:51.6573431Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:05:51.6574050Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:05:51.6574609Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:05:51.6575206Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:05:51.6575675Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6576001Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6576491Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:05:51.6576921Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:05:51.6577141Z | ^~~~ 2025-07-17T10:05:51.6577500Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:05:51.6578048Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:05:51.6578514Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:05:51.6578957Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:05:51.6579377Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:05:51.6580037Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:05:51.6580558Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:05:51.6581050Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:05:51.6581536Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:05:51.6582058Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:05:51.6582600Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:05:51.6583072Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6583396Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6583987Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:05:51.6584870Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:05:51.6585952Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:05:51.6586925Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:05:51.6587953Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:05:51.6589137Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:05:51.6592628Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:05:51.6598443Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:05:51.6601632Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:05:51.6602166Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:05:51.6602416Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:05:51.6605960Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:05:51.6609633Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:05:51.6610348Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:05:51.6610611Z | ^~~~~~~~ 2025-07-17T10:05:51.6610982Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:05:51.6611546Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:05:51.6612052Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:05:51.6612522Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:05:51.6612950Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:05:51.6613426Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:05:51.6614004Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:05:51.6614654Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:05:51.6615286Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:05:51.6615906Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:05:51.6616473Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:05:51.6616990Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:05:51.6617462Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6617784Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6618285Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:05:51.6618789Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:05:51.6619019Z | ^~~~ 2025-07-17T10:05:51.6619356Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:05:51.6619841Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:05:51.6620293Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:05:51.6620733Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:05:51.6621156Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:05:51.6621691Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:05:51.6622216Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:05:51.6622788Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:05:51.6623272Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:05:51.6623845Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:05:51.6624385Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:05:51.6624859Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6625187Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6625826Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:05:51.6626713Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:05:51.6627671Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:05:51.6628641Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:05:51.6629674Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:05:51.6630790Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:05:51.6634183Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:05:51.6640117Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:05:51.6643432Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:05:51.6643976Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:05:51.6644220Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:05:51.6647655Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:05:51.6651152Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:05:51.6651980Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:05:51.6652244Z | ^~~~~~~~ 2025-07-17T10:05:51.6652613Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:05:51.6653181Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:05:51.6653680Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:05:51.6654144Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:05:51.6654579Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:05:51.6655146Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:05:51.6655753Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:05:51.6656461Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:05:51.6657090Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:05:51.6657768Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:05:51.6658345Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:05:51.6658880Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:05:51.6659357Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6659690Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6660173Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:05:51.6660602Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:05:51.6660826Z | ^~~~ 2025-07-17T10:05:51.6661172Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:05:51.6661649Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:05:51.6662098Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:05:51.6662544Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:05:51.6662976Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:05:51.6663453Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:05:51.6663965Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:05:51.6664459Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:05:51.6664947Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:05:51.6665548Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:05:51.6666087Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:05:51.6666649Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6666972Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6667555Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:05:51.6668434Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:05:51.6669454Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:05:51.6670432Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:05:51.6671544Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:05:51.6672712Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:05:51.6676766Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:05:51.6684104Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:05:51.6687947Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:05:51.6688497Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:05:51.6688743Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:05:51.6692364Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:05:51.6695959Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:05:51.6696673Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:05:51.6696931Z | ^~~~~~~~ 2025-07-17T10:05:51.6697301Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:05:51.6697859Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:05:51.6698357Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:05:51.6698822Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:05:51.6699257Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:05:51.6699724Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:05:51.6700301Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:05:51.6700938Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:05:51.6701584Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:05:51.6702201Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:05:51.6702832Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:05:51.6703359Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:05:51.6703829Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6704156Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6704649Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:05:51.6705079Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:05:51.6705365Z | ^~~~ 2025-07-17T10:05:51.6705863Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:05:51.6706362Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:05:51.6706888Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:05:51.6707351Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:05:51.6707797Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:05:51.6708352Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:05:51.6708884Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:05:51.6709381Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:05:51.6709870Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:05:51.6710391Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:05:51.6710931Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:05:51.6711405Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6711730Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6712328Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:05:51.6713213Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:05:51.6714171Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:05:51.6715155Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:05:51.6716190Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:05:51.6717301Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:05:51.6720919Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:05:51.6726800Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:05:51.6730033Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:05:51.6730575Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:05:51.6730829Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:05:51.6734314Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:05:51.6737798Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:05:51.6738507Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:05:51.6738775Z | ^~~~~~~~ 2025-07-17T10:05:51.6739211Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:05:51.6739781Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:05:51.6740351Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:05:51.6740809Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:05:51.6741239Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:05:51.6741771Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:05:51.6742387Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:05:51.6743036Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:05:51.6743671Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:05:51.6744288Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:05:51.6744856Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:05:51.6745485Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:05:51.6745980Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6746313Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6746812Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:05:51.6747339Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:05:51.6747576Z | ^~~~ 2025-07-17T10:05:51.6747928Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:05:51.6748411Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:05:51.6748859Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:05:51.6749293Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:05:51.6749719Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:05:51.6750202Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:05:51.6750725Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:05:51.6751222Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:05:51.6751710Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:05:51.6752236Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:05:51.6752777Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:05:51.6753323Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6753662Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6754260Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:05:51.6755241Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:05:51.6756271Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:05:51.6757236Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:05:51.6758273Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:05:51.6759413Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:05:51.6762778Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:05:51.6768574Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:05:51.6771790Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:05:51.6772389Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:05:51.6772645Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:05:51.6776127Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:05:51.6779645Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:05:51.6780451Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:05:51.6780725Z | ^~~~~~~~ 2025-07-17T10:05:51.6781104Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:05:51.6781669Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:05:51.6782180Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:05:51.6782643Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:05:51.6783076Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:05:51.6783556Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:05:51.6784144Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:05:51.6784772Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:05:51.6785493Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:05:51.6786118Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:05:51.6786679Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:05:51.6787275Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:05:51.6787766Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6788163Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6788659Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:05:51.6789082Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:05:51.6789312Z | ^~~~ 2025-07-17T10:05:51.6789732Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:05:51.6790222Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:05:51.6790684Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:05:51.6791132Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:05:51.6791555Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:05:51.6792032Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:05:51.6792536Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:05:51.6793036Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:05:51.6793525Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:05:51.6794052Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:05:51.6794667Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:05:51.6795155Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6795480Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6796068Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:05:51.6796957Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:05:51.6797918Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:05:51.6798897Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:05:51.6799938Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:05:51.6801054Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:05:51.6804573Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:05:51.6810347Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:05:51.6813586Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:05:51.6814118Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:05:51.6814362Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:05:51.6817834Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:05:51.6821369Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:05:51.6822084Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:05:51.6822348Z | ^~~~~~~~ 2025-07-17T10:05:51.6822772Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:05:51.6823339Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:05:51.6823848Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:05:51.6824313Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:05:51.6824756Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:05:51.6825228Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:05:51.6825936Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:05:51.6826561Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:05:51.6827194Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:05:51.6827808Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:05:51.6828453Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:05:51.6828983Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:05:51.6829454Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6829786Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6830276Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:05:51.6830702Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:05:51.6830930Z | ^~~~ 2025-07-17T10:05:51.6831281Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:05:51.6831761Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:05:51.6832220Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:05:51.6832656Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:05:51.6833080Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:05:51.6833556Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:05:51.6834072Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:05:51.6834637Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:05:51.6835140Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:05:51.6835854Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:05:51.6836387Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:05:51.6836933Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6837263Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6837845Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:05:51.6838725Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:05:51.6839690Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:05:51.6840681Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:05:51.6841715Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:05:51.6842825Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:05:51.6846312Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:05:51.6852100Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:05:51.6855373Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:05:51.6855906Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:05:51.6856158Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:05:51.6859612Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:05:51.6863206Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:05:51.6863912Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:05:51.6864169Z | ^~~~~~~~ 2025-07-17T10:05:51.6864536Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:05:51.6865104Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:05:51.6865688Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:05:51.6866152Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:05:51.6866581Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:05:51.6867058Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:05:51.6867722Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:05:51.6868362Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:05:51.6869068Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:05:51.6869683Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:05:51.6870298Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:05:51.6870821Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:05:51.6871294Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6871615Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6872101Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:05:51.6872525Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:05:51.6872744Z | ^~~~ 2025-07-17T10:05:51.6873077Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:05:51.6873549Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:05:51.6874006Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:05:51.6874441Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:05:51.6874862Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:05:51.6875327Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:05:51.6875917Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:05:51.6876404Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:05:51.6876885Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:05:51.6877404Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:05:51.6877929Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:05:51.6878400Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6878718Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6879299Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:05:51.6880181Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:05:51.6881131Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:05:51.6882152Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:05:51.6883182Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:05:51.6884346Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:05:51.6887552Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:05:51.6892829Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:05:51.6895902Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:05:51.6896429Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:05:51.6906524Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:05:51.6910012Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:05:51.6913628Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:05:51.6914355Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:05:51.6914622Z | ^~~~~~~~ 2025-07-17T10:05:51.6914998Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:05:51.6915563Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:05:51.6916068Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:05:51.6916536Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:05:51.6916978Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:05:51.6917458Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:05:51.6918057Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:05:51.6918773Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:05:51.6919409Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:05:51.6920031Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:05:51.6920593Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:05:51.6921119Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:05:51.6921603Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6921939Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6922440Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:05:51.6922883Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:05:51.6923114Z | ^~~~ 2025-07-17T10:05:51.6923337Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:05:51.6923555Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:05:51.6923747Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:05:51.6923942Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:05:51.6924177Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:05:51.6924432Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:05:51.6924716Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:05:51.6924931Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:05:51.6925204Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:05:51.6925453Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:05:51.6925692Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:05:51.6925873Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6925966Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6926413Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:05:51.6926909Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:05:51.6927427Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:05:51.6927941Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:05:51.6928576Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:05:51.6929177Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:05:51.6932025Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:05:51.6935338Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:05:51.6935875Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:05:51.6935996Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:05:51.6936077Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:05:51.6939392Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:05:51.6940163Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:05:51.6940308Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:05:51.6940374Z | ^~~~~~~~ 2025-07-17T10:05:51.6940652Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:05:51.6940896Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:05:51.6941104Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:05:51.6941299Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:05:51.6941541Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:05:51.6941783Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:05:51.6942145Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:05:51.6942427Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:05:51.6942773Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:05:51.6943039Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:05:51.6943287Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:05:51.6943514Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:05:51.6943714Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6943799Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6944157Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:05:51.6944259Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:05:51.6944339Z | ^~~~ 2025-07-17T10:05:51.6944562Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:05:51.6944768Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:05:51.6944965Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:05:51.6945222Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:05:51.6945480Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:05:51.6945724Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:05:51.6945945Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:05:51.6946164Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:05:51.6946374Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:05:51.6946629Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:05:51.6946857Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:05:51.6947043Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6947120Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6947573Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:05:51.6948055Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:05:51.6948675Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:05:51.6949275Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:05:51.6949913Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:05:51.6950539Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:05:51.6953428Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:05:51.6956749Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:05:51.6957246Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:05:51.6957364Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:05:51.6957447Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:05:51.6960867Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:05:51.6961644Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:05:51.6961781Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:05:51.6961857Z | ^~~~~~~~ 2025-07-17T10:05:51.6962126Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:05:51.6962381Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:05:51.6962580Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:05:51.6962851Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:05:51.6963023Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:05:51.6963267Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:05:51.6963560Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:05:51.6963851Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:05:51.6964153Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:05:51.6964418Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:05:51.6964670Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:05:51.6964904Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:05:51.6965103Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6965183Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6965539Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:05:51.6965694Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:05:51.6965774Z | ^~~~ 2025-07-17T10:05:51.6966001Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:05:51.6966268Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:05:51.6966461Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:05:51.6966659Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:05:51.6966893Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:05:51.6967150Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:05:51.6967374Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:05:51.6967600Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:05:51.6967812Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:05:51.6968070Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:05:51.6968300Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:05:51.6968494Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6968576Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6969035Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:05:51.6969594Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:05:51.6970123Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:05:51.6970628Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:05:51.6971219Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:05:51.6971827Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:05:51.6974744Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:05:51.6978094Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:05:51.6978588Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:05:51.6978785Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:05:51.6978871Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:05:51.6982188Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:05:51.6982913Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:05:51.6983107Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:05:51.6983188Z | ^~~~~~~~ 2025-07-17T10:05:51.6983460Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:05:51.6983766Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:05:51.6983966Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:05:51.6984225Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:05:51.6984398Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:05:51.6984641Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:05:51.6984938Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:05:51.6985235Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:05:51.6985607Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:05:51.6985886Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:05:51.6986122Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:05:51.6986359Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:05:51.6986542Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6986705Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6987046Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:05:51.6987154Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:05:51.6987223Z | ^~~~ 2025-07-17T10:05:51.6987459Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:05:51.6987663Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:05:51.6987865Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:05:51.6988046Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:05:51.6988230Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:05:51.6988468Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:05:51.6988698Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:05:51.6988910Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:05:51.6989128Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:05:51.6989373Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:05:51.6989670Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:05:51.6989853Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.6990008Z from rng_extension.cpp:1: 2025-07-17T10:05:51.6990452Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:05:51.6990950Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:05:51.6991527Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:05:51.6992039Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:05:51.6992634Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:05:51.6993253Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:05:51.6995881Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:05:51.6999002Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:05:51.6999479Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:05:51.6999637Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:05:51.6999725Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:05:51.7002862Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:05:51.7003584Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:05:51.7003735Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:05:51.7003854Z | ^~~~~~~~ 2025-07-17T10:05:51.7004129Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:05:51.7004367Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:05:51.7004574Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:05:51.7004769Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:05:51.7004948Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:05:51.7005189Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:05:51.7005479Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:05:51.7005769Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:05:51.7006060Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:05:51.7006328Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:05:51.7006557Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:05:51.7006786Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:05:51.7007022Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.7007112Z from rng_extension.cpp:1: 2025-07-17T10:05:51.7007505Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:05:51.7007607Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:05:51.7007673Z | ^~~~ 2025-07-17T10:05:51.7007904Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T10:05:51.7008172Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T10:05:51.7008379Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T10:05:51.7008562Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T10:05:51.7008742Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T10:05:51.7008977Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T10:05:51.7009201Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T10:05:51.7009410Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T10:05:51.7009629Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T10:05:51.7009869Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T10:05:51.7010103Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T10:05:51.7010342Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.7010426Z from rng_extension.cpp:1: 2025-07-17T10:05:51.7010858Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T10:05:51.7011359Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T10:05:51.7011873Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T10:05:51.7012383Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T10:05:51.7012973Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T10:05:51.7013739Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T10:05:51.7016730Z 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_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T10:05:51.7019970Z 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_3.12/lib/python3.12/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T10:05:51.7020498Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T10:05:51.7020608Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T10:05:51.7020696Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T10:05:51.7024083Z /opt/conda/envs/py_3.12/lib/python3.12/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}]’: 2025-07-17T10:05:51.7024857Z /opt/conda/envs/py_3.12/lib/python3.12/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 2025-07-17T10:05:51.7025003Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T10:05:51.7025067Z | ^~~~~~~~ 2025-07-17T10:05:51.7025420Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T10:05:51.7025738Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T10:05:51.7025940Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T10:05:51.7026147Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T10:05:51.7026320Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T10:05:51.7026563Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T10:05:51.7026854Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T10:05:51.7027142Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T10:05:51.7027429Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T10:05:51.7027703Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T10:05:51.7027932Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T10:05:51.7028236Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T10:05:51.7028418Z from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:5, 2025-07-17T10:05:51.7028508Z from rng_extension.cpp:1: 2025-07-17T10:05:51.7028857Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T10:05:51.7028961Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T10:05:51.7029026Z | ^~~~ 2025-07-17T10:05:51.7030688Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/rng_extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/rng.cpython-312-x86_64-linux-gnu.so 2025-07-17T10:05:52.1417357Z building 'torch_test_cpp_extension.cuda' extension 2025-07-17T10:05:52.1420173Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THH -I/opt/rocm/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c cuda_extension.cpp -o build/temp.linux-x86_64-cpython-312/cuda_extension.o -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1016\" -DTORCH_EXTENSION_NAME=cuda -std=c++17 2025-07-17T10:05:52.3211908Z /opt/rocm/bin/hipcc -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THH -I/opt/rocm/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c hip_extension_kernel.hip -o build/temp.linux-x86_64-cpython-312/hip_extension_kernel.o -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -DCUDA_HAS_FP16=1 -D__HIP_NO_HALF_OPERATORS__=1 -D__HIP_NO_HALF_CONVERSIONS__=1 -DHIP_ENABLE_WARP_SYNC_BUILTINS=1 -O2 -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1016\" -DTORCH_EXTENSION_NAME=cuda --offload-arch=gfx90a --offload-arch=gfx942 -fno-gpu-rdc -std=c++17 2025-07-17T10:05:52.5061310Z /opt/rocm/bin/hipcc -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THH -I/opt/rocm/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c hip_extension_kernel2.hip -o build/temp.linux-x86_64-cpython-312/hip_extension_kernel2.o -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -DCUDA_HAS_FP16=1 -D__HIP_NO_HALF_OPERATORS__=1 -D__HIP_NO_HALF_CONVERSIONS__=1 -DHIP_ENABLE_WARP_SYNC_BUILTINS=1 -O2 -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1016\" -DTORCH_EXTENSION_NAME=cuda --offload-arch=gfx90a --offload-arch=gfx942 -fno-gpu-rdc -std=c++17 2025-07-17T10:05:52.6891901Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/cuda_extension.o build/temp.linux-x86_64-cpython-312/hip_extension_kernel.o build/temp.linux-x86_64-cpython-312/hip_extension_kernel2.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -L/opt/rocm/lib -L/opt/rocm/hip/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -lamdhip64 -lc10_hip -ltorch_hip -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cuda.cpython-312-x86_64-linux-gnu.so 2025-07-17T10:05:53.2353598Z building 'torch_test_cpp_extension.torch_library' extension 2025-07-17T10:05:53.2356420Z /opt/rocm/bin/hipcc -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THH -I/opt/rocm/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c torch_library.cu -o build/temp.linux-x86_64-cpython-312/torch_library.o -D__HIP_PLATFORM_AMD__=1 -DUSE_ROCM=1 -DHIPBLAS_V2 -fPIC -DCUDA_HAS_FP16=1 -D__HIP_NO_HALF_OPERATORS__=1 -D__HIP_NO_HALF_CONVERSIONS__=1 -DHIP_ENABLE_WARP_SYNC_BUILTINS=1 -O2 -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1016\" -DTORCH_EXTENSION_NAME=torch_library --offload-arch=gfx90a --offload-arch=gfx942 -fno-gpu-rdc -std=c++17 2025-07-17T10:05:53.4316725Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/torch_library.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -L/opt/rocm/lib -L/opt/rocm/hip/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -lamdhip64 -lc10_hip -ltorch_hip -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/torch_library.cpython-312-x86_64-linux-gnu.so 2025-07-17T10:05:53.7718382Z running install_lib 2025-07-17T10:05:53.7800031Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/torch_library.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2025-07-17T10:05:53.7805961Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cuda.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2025-07-17T10:05:53.7870358Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/maia.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2025-07-17T10:05:53.7938511Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cpp.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2025-07-17T10:05:53.8004132Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/rng.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension 2025-07-17T10:05:53.8081223Z running install_egg_info 2025-07-17T10:05:53.8247329Z running egg_info 2025-07-17T10:05:53.8311646Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2025-07-17T10:05:53.8315440Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2025-07-17T10:05:53.8316499Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt 2025-07-17T10:05:53.8318312Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2025-07-17T10:05:53.8390950Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-07-17T10:05:53.8398036Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-07-17T10:05:53.8398643Z removing './install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension-0.0.0-py3.12.egg-info' (and everything under it) 2025-07-17T10:05:53.8403858Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension-0.0.0-py3.12.egg-info 2025-07-17T10:05:53.8413913Z running install_scripts 2025-07-17T10:05:55.8589056Z running install 2025-07-17T10:05:55.8589676Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:90: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-07-17T10:05:55.8590162Z !! 2025-07-17T10:05:55.8590252Z 2025-07-17T10:05:55.8590340Z ******************************************************************************** 2025-07-17T10:05:55.8590638Z Please avoid running ``setup.py`` directly. 2025-07-17T10:05:55.8590909Z Instead, use pypa/build, pypa/installer or other 2025-07-17T10:05:55.8591167Z standards-based tools. 2025-07-17T10:05:55.8591298Z 2025-07-17T10:05:55.8591439Z By 2025-Oct-31, you need to update your project and remove deprecated calls 2025-07-17T10:05:55.8592390Z or your builds will no longer be supported. 2025-07-17T10:05:55.8592560Z 2025-07-17T10:05:55.8592763Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-07-17T10:05:55.8593091Z ******************************************************************************** 2025-07-17T10:05:55.8593229Z 2025-07-17T10:05:55.8593299Z !! 2025-07-17T10:05:55.8593460Z self.initialize_options() 2025-07-17T10:05:55.8697052Z running build 2025-07-17T10:05:55.8697260Z running build_ext 2025-07-17T10:05:55.8986950Z building 'no_python_abi_suffix_test' extension 2025-07-17T10:05:55.9495830Z ninja: no work to do. 2025-07-17T10:05:55.9533343Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib /var/lib/jenkins/pytorch/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-312/no_python_abi_suffix_test.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/no_python_abi_suffix_test.so 2025-07-17T10:05:56.0587625Z running install_lib 2025-07-17T10:05:56.0664879Z copying build/lib.linux-x86_64-cpython-312/no_python_abi_suffix_test.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages 2025-07-17T10:05:56.0670688Z running install_egg_info 2025-07-17T10:05:56.0831991Z running egg_info 2025-07-17T10:05:56.0897629Z writing no_python_abi_suffix_test.egg-info/PKG-INFO 2025-07-17T10:05:56.0901705Z writing dependency_links to no_python_abi_suffix_test.egg-info/dependency_links.txt 2025-07-17T10:05:56.0903266Z writing top-level names to no_python_abi_suffix_test.egg-info/top_level.txt 2025-07-17T10:05:56.0972276Z reading manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2025-07-17T10:05:56.0978311Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2025-07-17T10:05:56.0978902Z removing './install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/no_python_abi_suffix_test-0.0.0-py3.12.egg-info' (and everything under it) 2025-07-17T10:05:56.0982846Z Copying no_python_abi_suffix_test.egg-info to ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/no_python_abi_suffix_test-0.0.0-py3.12.egg-info 2025-07-17T10:05:56.0991675Z running install_scripts 2025-07-17T10:05:58.0397236Z /var/lib/jenkins/pytorch/test/cpp_extensions/python_agnostic_extension/python_agnostic/csrc/ultra_norm.cu -> /var/lib/jenkins/pytorch/test/cpp_extensions/python_agnostic_extension/python_agnostic/csrc/ultra_norm.cu [skipped, no changes] 2025-07-17T10:05:58.0398450Z Successfully preprocessed all matching files. 2025-07-17T10:05:58.0398738Z Total number of unsupported CUDA function calls: 0 2025-07-17T10:05:58.0398911Z 2025-07-17T10:05:58.0398914Z 2025-07-17T10:05:58.0410015Z Total number of replaced kernel launches: 0 2025-07-17T10:05:58.0712310Z running bdist_wheel 2025-07-17T10:05:58.1255611Z running build 2025-07-17T10:05:58.1255862Z running build_ext 2025-07-17T10:05:58.1269495Z building 'python_agnostic._C' extension 2025-07-17T10:05:58.1777144Z ninja: no work to do. 2025-07-17T10:05:58.1814184Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib /var/lib/jenkins/pytorch/test/cpp_extensions/python_agnostic_extension/build/temp.linux-x86_64-cpython-312/python_agnostic/csrc/ultra_norm.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -L/opt/rocm/lib -L/opt/rocm/hip/lib -lc10 -ltorch -ltorch_cpu -lamdhip64 -lc10_hip -ltorch_hip -o build/lib.linux-x86_64-cpython-312/python_agnostic/_C.so 2025-07-17T10:05:58.5862226Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:90: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-07-17T10:05:58.5862786Z !! 2025-07-17T10:05:58.5862879Z 2025-07-17T10:05:58.5863619Z ******************************************************************************** 2025-07-17T10:05:58.5863874Z Please avoid running ``setup.py`` directly. 2025-07-17T10:05:58.5864136Z Instead, use pypa/build, pypa/installer or other 2025-07-17T10:05:58.5864388Z standards-based tools. 2025-07-17T10:05:58.5864510Z 2025-07-17T10:05:58.5864677Z By 2025-Oct-31, you need to update your project and remove deprecated calls 2025-07-17T10:05:58.5864990Z or your builds will no longer be supported. 2025-07-17T10:05:58.5865147Z 2025-07-17T10:05:58.5865531Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-07-17T10:05:58.5865855Z ******************************************************************************** 2025-07-17T10:05:58.5865998Z 2025-07-17T10:05:58.5866066Z !! 2025-07-17T10:05:58.5866229Z self.initialize_options() 2025-07-17T10:05:58.5926564Z installing to build/bdist.linux-x86_64/wheel 2025-07-17T10:05:58.5926878Z running install 2025-07-17T10:05:58.5965364Z running install_lib 2025-07-17T10:05:58.6035517Z creating build/bdist.linux-x86_64/wheel 2025-07-17T10:05:58.6036994Z creating build/bdist.linux-x86_64/wheel/python_agnostic 2025-07-17T10:05:58.6037938Z copying build/lib.linux-x86_64-cpython-312/python_agnostic/_C.so -> build/bdist.linux-x86_64/wheel/./python_agnostic 2025-07-17T10:05:58.6040138Z running install_egg_info 2025-07-17T10:05:58.6113137Z running egg_info 2025-07-17T10:05:58.6176412Z writing python_agnostic.egg-info/PKG-INFO 2025-07-17T10:05:58.6181046Z writing dependency_links to python_agnostic.egg-info/dependency_links.txt 2025-07-17T10:05:58.6182268Z writing top-level names to python_agnostic.egg-info/top_level.txt 2025-07-17T10:05:58.6252552Z reading manifest file 'python_agnostic.egg-info/SOURCES.txt' 2025-07-17T10:05:58.6258422Z writing manifest file 'python_agnostic.egg-info/SOURCES.txt' 2025-07-17T10:05:58.6258972Z Copying python_agnostic.egg-info to build/bdist.linux-x86_64/wheel/./python_agnostic-0.0-py3.12.egg-info 2025-07-17T10:05:58.6268487Z running install_scripts 2025-07-17T10:05:58.6359271Z creating build/bdist.linux-x86_64/wheel/python_agnostic-0.0.dist-info/WHEEL 2025-07-17T10:05:58.6362662Z creating 'dist/python_agnostic-0.0-cp39-abi3-linux_x86_64.whl' and adding 'build/bdist.linux-x86_64/wheel' to it 2025-07-17T10:05:58.6380066Z adding 'python_agnostic/_C.so' 2025-07-17T10:05:58.6381885Z adding 'python_agnostic-0.0.dist-info/METADATA' 2025-07-17T10:05:58.6382925Z adding 'python_agnostic-0.0.dist-info/WHEEL' 2025-07-17T10:05:58.6383873Z adding 'python_agnostic-0.0.dist-info/top_level.txt' 2025-07-17T10:05:58.6385126Z adding 'python_agnostic-0.0.dist-info/RECORD' 2025-07-17T10:05:58.6385819Z removing build/bdist.linux-x86_64/wheel 2025-07-17T10:06:00.5521534Z running install 2025-07-17T10:06:00.5522146Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:90: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-07-17T10:06:00.5522630Z !! 2025-07-17T10:06:00.5522715Z 2025-07-17T10:06:00.5522863Z ******************************************************************************** 2025-07-17T10:06:00.5523120Z Please avoid running ``setup.py`` directly. 2025-07-17T10:06:00.5523395Z Instead, use pypa/build, pypa/installer or other 2025-07-17T10:06:00.5523648Z standards-based tools. 2025-07-17T10:06:00.5524359Z 2025-07-17T10:06:00.5524511Z By 2025-Oct-31, you need to update your project and remove deprecated calls 2025-07-17T10:06:00.5524826Z or your builds will no longer be supported. 2025-07-17T10:06:00.5525008Z 2025-07-17T10:06:00.5525218Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-07-17T10:06:00.5525541Z ******************************************************************************** 2025-07-17T10:06:00.5525697Z 2025-07-17T10:06:00.5525753Z !! 2025-07-17T10:06:00.5525908Z self.initialize_options() 2025-07-17T10:06:00.5630546Z running build 2025-07-17T10:06:00.5631074Z running build_py 2025-07-17T10:06:00.5703889Z copying libtorch_agnostic/__init__.py -> build/lib.linux-x86_64-cpython-312/libtorch_agnostic 2025-07-17T10:06:00.5707333Z copying libtorch_agnostic/ops.py -> build/lib.linux-x86_64-cpython-312/libtorch_agnostic 2025-07-17T10:06:00.5714062Z running build_ext 2025-07-17T10:06:00.6004969Z building 'libtorch_agnostic._C' extension 2025-07-17T10:06:00.6532041Z ninja: no work to do. 2025-07-17T10:06:00.6567885Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib /var/lib/jenkins/pytorch/test/cpp_extensions/libtorch_agnostic_extension/build/temp.linux-x86_64-cpython-312/var/lib/jenkins/pytorch/test/cpp_extensions/libtorch_agnostic_extension/libtorch_agnostic/csrc/kernel.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -o build/lib.linux-x86_64-cpython-312/libtorch_agnostic/_C.so 2025-07-17T10:06:00.9426200Z running install_lib 2025-07-17T10:06:00.9508342Z copying build/lib.linux-x86_64-cpython-312/libtorch_agnostic/_C.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/libtorch_agnostic 2025-07-17T10:06:00.9515181Z running install_egg_info 2025-07-17T10:06:00.9669803Z running egg_info 2025-07-17T10:06:00.9733262Z writing libtorch_agnostic.egg-info/PKG-INFO 2025-07-17T10:06:00.9737589Z writing dependency_links to libtorch_agnostic.egg-info/dependency_links.txt 2025-07-17T10:06:00.9738438Z writing requirements to libtorch_agnostic.egg-info/requires.txt 2025-07-17T10:06:00.9739633Z writing top-level names to libtorch_agnostic.egg-info/top_level.txt 2025-07-17T10:06:00.9814344Z reading manifest file 'libtorch_agnostic.egg-info/SOURCES.txt' 2025-07-17T10:06:00.9820746Z writing manifest file 'libtorch_agnostic.egg-info/SOURCES.txt' 2025-07-17T10:06:00.9821316Z removing './install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/libtorch_agnostic-0.0-py3.12.egg-info' (and everything under it) 2025-07-17T10:06:00.9826812Z Copying libtorch_agnostic.egg-info to ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/libtorch_agnostic-0.0-py3.12.egg-info 2025-07-17T10:06:00.9837214Z running install_scripts 2025-07-17T10:06:01.5544515Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:06:01.5545470Z Executing ['/opt/conda/envs/py_3.12/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 10:06:01.554245] 2025-07-17T10:06:05.4567863Z 2025-07-17T10:06:05.4568952Z 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_9c36e273b9e48596_.log 2025-07-17T10:06:05.4574183Z 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 2025-07-17T10:06:05.4578363Z 2025-07-17T10:06:05.4578520Z Running test_cpp_extensions_jit 1/1 ... [2025-07-17 10:06:05.456947] 2025-07-17T10:06:05.4578794Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:06:05.4579425Z Executing ['/opt/conda/envs/py_3.12/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 10:06:05.457233] 2025-07-17T10:10:58.4374720Z 2025-07-17T10:10:58.4375681Z 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_ad9271d21e8f1f7c_.log 2025-07-17T10:10:58.4384055Z 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 2025-07-17T10:10:58.4391743Z 2025-07-17T10:10:58.4391909Z GITHUB_RUN_ID, GITHUB_RUN_ATTEMPT, or ARTIFACTS_FILE_SUFFIX not set, not uploading 2025-07-17T10:10:58.4392217Z Uploading artifacts took 0.00 seconds 2025-07-17T10:10:58.4392517Z Running test_cpp_extensions_mtia_backend 1/1 ... [2025-07-17 10:10:58.437399] 2025-07-17T10:10:58.4392818Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:10:58.4393477Z Executing ['/opt/conda/envs/py_3.12/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 10:10:58.437689] 2025-07-17T10:11:01.5092135Z 2025-07-17T10:11:01.5093238Z 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_32c5cd26bcf2f20d_.log 2025-07-17T10:11:01.5094985Z 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 2025-07-17T10:11:01.5096228Z 2025-07-17T10:11:01.5096426Z Running test_cpp_extensions_open_device_registration 1/1 ... [2025-07-17 10:11:01.509153] 2025-07-17T10:11:03.2835749Z -- The CXX compiler identification is GNU 11.4.0 2025-07-17T10:11:03.4337350Z -- The C compiler identification is GNU 11.4.0 2025-07-17T10:11:03.4758967Z -- Detecting CXX compiler ABI info 2025-07-17T10:11:04.0386395Z -- Detecting CXX compiler ABI info - done 2025-07-17T10:11:04.0532266Z -- Check for working CXX compiler: /opt/cache/bin/c++ - skipped 2025-07-17T10:11:04.0535263Z -- Detecting CXX compile features 2025-07-17T10:11:04.0539909Z -- Detecting CXX compile features - done 2025-07-17T10:11:04.0664330Z -- Detecting C compiler ABI info 2025-07-17T10:11:04.6160850Z -- Detecting C compiler ABI info - done 2025-07-17T10:11:04.6301948Z -- Check for working C compiler: /opt/cache/bin/cc - skipped 2025-07-17T10:11:04.6305175Z -- Detecting C compile features 2025-07-17T10:11:04.6308398Z -- Detecting C compile features - done 2025-07-17T10:11:04.7619021Z Building PyTorch for GPU arch: gfx90a;gfx942 2025-07-17T10:11:04.9385078Z -- Found HIP: /opt/rocm (found suitable version "6.4.43483-a187df25c", minimum required is "1.0") 2025-07-17T10:11:04.9385958Z HIP VERSION: 6.4.43483-a187df25c 2025-07-17T10:11:05.1530762Z -- Performing Test CMAKE_HAVE_LIBC_PTHREAD 2025-07-17T10:11:05.7066558Z -- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Success 2025-07-17T10:11:05.7072688Z -- Found Threads: TRUE 2025-07-17T10:11:05.7528987Z hip VERSION: 6.4.43483 2025-07-17T10:11:05.7549215Z -- Reading ROCM version from: /opt/rocm/include/rocm-core/rocm_version.h 2025-07-17T10:11:05.7549550Z -- Content: 2025-07-17T10:11:05.7549998Z  2025-07-17T10:11:05.7550180Z ***** ROCm version from rocm_version.h **** 2025-07-17T10:11:05.7551108Z  2025-07-17T10:11:05.7551327Z ROCM_VERSION_DEV: 6.4.1 2025-07-17T10:11:05.7553967Z ROCM_VERSION_DEV_MAJOR: 6 2025-07-17T10:11:05.7554272Z ROCM_VERSION_DEV_MINOR: 4 2025-07-17T10:11:05.7554524Z ROCM_VERSION_DEV_PATCH: 1 2025-07-17T10:11:05.7554776Z ROCM_VERSION_DEV_INT: 60401 2025-07-17T10:11:05.7555037Z HIP_VERSION_MAJOR: 6 2025-07-17T10:11:05.7555258Z HIP_VERSION_MINOR: 4 2025-07-17T10:11:05.7555476Z TORCH_HIP_VERSION: 604 2025-07-17T10:11:05.7555666Z  2025-07-17T10:11:05.7555860Z ***** Library versions from cmake find_package ***** 2025-07-17T10:11:05.7556095Z  2025-07-17T10:11:05.7556291Z amd_comgr VERSION: 3.0.0 2025-07-17T10:11:05.8047827Z rocrand VERSION: 3.3.0 2025-07-17T10:11:05.8078226Z hiprand VERSION: 2.12.0 2025-07-17T10:11:05.8097196Z rocblas VERSION: 4.4.0 2025-07-17T10:11:05.8135208Z hipblas VERSION: 2.4.0 2025-07-17T10:11:05.8159026Z miopen VERSION: 3.4.0 2025-07-17T10:11:05.8177935Z hipfft VERSION: 1.0.18 2025-07-17T10:11:05.8197077Z hipsparse VERSION: 3.2.0 2025-07-17T10:11:05.8216206Z rocprim VERSION: 3.4.0 2025-07-17T10:11:05.8244045Z hipcub VERSION: 3.4.0 2025-07-17T10:11:05.8262844Z rocthrust VERSION: 3.3.0 2025-07-17T10:11:05.8282072Z hipsolver VERSION: 2.4.0 2025-07-17T10:11:05.8308969Z rocsolver VERSION: 3.28.0 2025-07-17T10:11:05.8310034Z CMake Warning at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Caffe2/public/LoadHIP.cmake:175 (message): 2025-07-17T10:11:05.8310518Z Work around hiprtc cmake failure for cmake >= 4 2025-07-17T10:11:05.8310760Z Call Stack (most recent call first): 2025-07-17T10:11:05.8311148Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Caffe2/Caffe2Config.cmake:74 (include) 2025-07-17T10:11:05.8311716Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Torch/TorchConfig.cmake:68 (find_package) 2025-07-17T10:11:05.8312111Z CMakeLists.txt:27 (find_package) 2025-07-17T10:11:05.8312245Z 2025-07-17T10:11:05.8312329Z  2025-07-17T10:11:05.8323251Z CMake Deprecation Warning at /opt/rocm/lib/cmake/hiprtc/hiprtc-config.cmake:21 (cmake_minimum_required): 2025-07-17T10:11:05.8323758Z Compatibility with CMake < 3.10 will be removed from a future version of 2025-07-17T10:11:05.8324045Z CMake. 2025-07-17T10:11:05.8324136Z 2025-07-17T10:11:05.8324304Z Update the VERSION argument value. Or, use the ... syntax 2025-07-17T10:11:05.8324835Z to tell CMake that the project requires at least but has been updated 2025-07-17T10:11:05.8325145Z to work with policies introduced by or earlier. 2025-07-17T10:11:05.8325387Z Call Stack (most recent call first): 2025-07-17T10:11:05.8325797Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Caffe2/public/LoadHIP.cmake:67 (find_package) 2025-07-17T10:11:05.8326454Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Caffe2/public/LoadHIP.cmake:177 (find_package_and_print_version) 2025-07-17T10:11:05.8327056Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Caffe2/Caffe2Config.cmake:74 (include) 2025-07-17T10:11:05.8327703Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Torch/TorchConfig.cmake:68 (find_package) 2025-07-17T10:11:05.8328125Z CMakeLists.txt:27 (find_package) 2025-07-17T10:11:05.8328319Z 2025-07-17T10:11:05.8328394Z  2025-07-17T10:11:05.8328612Z hiprtc VERSION: 6.4.43483 2025-07-17T10:11:05.8347728Z hipblaslt VERSION: 0.12.1 2025-07-17T10:11:05.8880595Z rccl VERSION: 2.22.3 2025-07-17T10:11:05.8885720Z hsa-runtime64 VERSION: 1.15.60401 2025-07-17T10:11:05.8906073Z hipsparselt VERSION: 0.2.3 2025-07-17T10:11:08.1799840Z hipblaslt is using scale pointer vec ext 2025-07-17T10:11:08.3029346Z CMake Warning at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Torch/TorchConfig.cmake:22 (message): 2025-07-17T10:11:08.3029876Z static library kineto_LIBRARY-NOTFOUND not found. 2025-07-17T10:11:08.3030129Z Call Stack (most recent call first): 2025-07-17T10:11:08.3030572Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Torch/TorchConfig.cmake:125 (append_torchlib_if_found) 2025-07-17T10:11:08.3031017Z CMakeLists.txt:27 (find_package) 2025-07-17T10:11:08.3031172Z 2025-07-17T10:11:08.3031256Z  2025-07-17T10:11:08.3036051Z -- Found Torch: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch.so 2025-07-17T10:11:08.3072891Z -- Configuring done (5.5s) 2025-07-17T10:11:08.3227959Z -- Generating done (0.0s) 2025-07-17T10:11:08.3232928Z -- Build files have been written to: /var/lib/jenkins/pytorch/test/cpp_extensions/open_registration_extension/torch_openreg/build 2025-07-17T10:11:08.4573942Z [ 11%] Building CXX object third_party/openreg/CMakeFiles/openreg.dir/csrc/device.cpp.o 2025-07-17T10:11:08.4574555Z [ 11%] Building CXX object third_party/openreg/CMakeFiles/openreg.dir/csrc/memory.cpp.o 2025-07-17T10:11:08.8549105Z [ 17%] Linking CXX shared library libopenreg.so 2025-07-17T10:11:08.9399421Z [ 17%] Built target openreg 2025-07-17T10:11:08.9500078Z [ 35%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/OpenRegMinimal.cpp.o 2025-07-17T10:11:08.9500690Z [ 35%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/OpenRegExtra.cpp.o 2025-07-17T10:11:08.9501781Z [ 35%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/native/Minimal.cpp.o 2025-07-17T10:11:08.9503079Z [ 47%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/native/Extra.cpp.o 2025-07-17T10:11:08.9503580Z [ 47%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegGenerator.cpp.o 2025-07-17T10:11:08.9506161Z [ 58%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegDeviceAllocator.cpp.o 2025-07-17T10:11:08.9506712Z [ 58%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegGuard.cpp.o 2025-07-17T10:11:08.9510900Z [ 70%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegHostAllocator.cpp.o 2025-07-17T10:11:08.9511441Z [ 70%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegFunctions.cpp.o 2025-07-17T10:11:08.9513476Z [ 76%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegHooks.cpp.o 2025-07-17T10:11:08.9514938Z [ 82%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegSerialization.cpp.o 2025-07-17T10:11:16.9564565Z [ 88%] Linking CXX shared library libtorch_openreg.so 2025-07-17T10:11:17.3869489Z [ 88%] Built target torch_openreg 2025-07-17T10:11:17.3971758Z [ 94%] Building CXX object torch_openreg/csrc/CMakeFiles/torch_bindings.dir/Module.cpp.o 2025-07-17T10:11:23.9571866Z [100%] Linking CXX shared library libtorch_bindings.so 2025-07-17T10:11:24.1200274Z [100%] Built target torch_bindings 2025-07-17T10:11:24.1287671Z Install the project... 2025-07-17T10:11:24.1316571Z -- Install configuration: "" 2025-07-17T10:11:24.1839174Z running install 2025-07-17T10:11:24.1840423Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:90: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-07-17T10:11:24.1840916Z !! 2025-07-17T10:11:24.1841011Z 2025-07-17T10:11:24.1841117Z ******************************************************************************** 2025-07-17T10:11:24.1841382Z Please avoid running ``setup.py`` directly. 2025-07-17T10:11:24.1841647Z Instead, use pypa/build, pypa/installer or other 2025-07-17T10:11:24.1841888Z standards-based tools. 2025-07-17T10:11:24.1842011Z 2025-07-17T10:11:24.1842168Z By 2025-Oct-31, you need to update your project and remove deprecated calls 2025-07-17T10:11:24.1842467Z or your builds will no longer be supported. 2025-07-17T10:11:24.1842763Z 2025-07-17T10:11:24.1842977Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-07-17T10:11:24.1843315Z ******************************************************************************** 2025-07-17T10:11:24.1843464Z 2025-07-17T10:11:24.1843534Z !! 2025-07-17T10:11:24.1843695Z self.initialize_options() 2025-07-17T10:11:24.1956095Z running build 2025-07-17T10:11:24.1956328Z running build_py 2025-07-17T10:11:24.2027538Z creating build/lib.linux-x86_64-cpython-312/torch_openreg 2025-07-17T10:11:24.2029401Z copying torch_openreg/__init__.py -> build/lib.linux-x86_64-cpython-312/torch_openreg 2025-07-17T10:11:24.2032074Z creating build/lib.linux-x86_64-cpython-312/torch_openreg/openreg 2025-07-17T10:11:24.2033296Z copying torch_openreg/openreg/random.py -> build/lib.linux-x86_64-cpython-312/torch_openreg/openreg 2025-07-17T10:11:24.2035422Z copying torch_openreg/openreg/__init__.py -> build/lib.linux-x86_64-cpython-312/torch_openreg/openreg 2025-07-17T10:11:24.2041240Z creating build/lib.linux-x86_64-cpython-312/torch_openreg/lib 2025-07-17T10:11:24.2042540Z copying torch_openreg/lib/libtorch_openreg.so -> build/lib.linux-x86_64-cpython-312/torch_openreg/lib 2025-07-17T10:11:24.2058918Z copying torch_openreg/lib/libtorch_bindings.so -> build/lib.linux-x86_64-cpython-312/torch_openreg/lib 2025-07-17T10:11:24.2064768Z copying torch_openreg/lib/libopenreg.so -> build/lib.linux-x86_64-cpython-312/torch_openreg/lib 2025-07-17T10:11:24.2068198Z running build_ext 2025-07-17T10:11:24.2154796Z building 'torch_openreg._C' extension 2025-07-17T10:11:24.2155432Z creating build/temp.linux-x86_64-cpython-312/torch_openreg/csrc 2025-07-17T10:11:24.2159155Z gcc -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/include/python3.12 -c torch_openreg/csrc/stub.c -o build/temp.linux-x86_64-cpython-312/torch_openreg/csrc/stub.o -g -Wall -Werror 2025-07-17T10:11:24.4317000Z gcc -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/torch_openreg/csrc/stub.o -L/var/lib/jenkins/pytorch/test/cpp_extensions/open_registration_extension/torch_openreg/torch_openreg/lib -ltorch_bindings -o build/lib.linux-x86_64-cpython-312/torch_openreg/_C.cpython-312-x86_64-linux-gnu.so -Wl,-rpath,$ORIGIN/lib 2025-07-17T10:11:24.4825998Z running install_lib 2025-07-17T10:11:24.4902049Z creating install/opt/conda/envs/py_3.12/lib/python3.12/site-packages 2025-07-17T10:11:24.4908041Z creating install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg 2025-07-17T10:11:24.4908846Z creating install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg/openreg 2025-07-17T10:11:24.4910032Z copying build/lib.linux-x86_64-cpython-312/torch_openreg/openreg/__init__.py -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg/openreg 2025-07-17T10:11:24.4912611Z copying build/lib.linux-x86_64-cpython-312/torch_openreg/openreg/random.py -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg/openreg 2025-07-17T10:11:24.4914021Z copying build/lib.linux-x86_64-cpython-312/torch_openreg/_C.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg 2025-07-17T10:11:24.4916183Z creating install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg/lib 2025-07-17T10:11:24.4917592Z copying build/lib.linux-x86_64-cpython-312/torch_openreg/lib/libtorch_bindings.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg/lib 2025-07-17T10:11:24.4924110Z copying build/lib.linux-x86_64-cpython-312/torch_openreg/lib/libtorch_openreg.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg/lib 2025-07-17T10:11:24.4939515Z copying build/lib.linux-x86_64-cpython-312/torch_openreg/lib/libopenreg.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg/lib 2025-07-17T10:11:24.4941385Z copying build/lib.linux-x86_64-cpython-312/torch_openreg/__init__.py -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg 2025-07-17T10:11:24.4946971Z byte-compiling ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg/openreg/__init__.py to __init__.cpython-312.pyc 2025-07-17T10:11:24.4953299Z byte-compiling ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg/openreg/random.py to random.cpython-312.pyc 2025-07-17T10:11:24.4958405Z byte-compiling ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg/__init__.py to __init__.cpython-312.pyc 2025-07-17T10:11:24.4961431Z running install_egg_info 2025-07-17T10:11:24.5118012Z running egg_info 2025-07-17T10:11:24.5183771Z creating torch_openreg.egg-info 2025-07-17T10:11:24.5184053Z writing torch_openreg.egg-info/PKG-INFO 2025-07-17T10:11:24.5188152Z writing dependency_links to torch_openreg.egg-info/dependency_links.txt 2025-07-17T10:11:24.5189386Z writing requirements to torch_openreg.egg-info/requires.txt 2025-07-17T10:11:24.5190518Z writing top-level names to torch_openreg.egg-info/top_level.txt 2025-07-17T10:11:24.5191962Z writing manifest file 'torch_openreg.egg-info/SOURCES.txt' 2025-07-17T10:11:24.5265375Z reading manifest file 'torch_openreg.egg-info/SOURCES.txt' 2025-07-17T10:11:24.5270705Z writing manifest file 'torch_openreg.egg-info/SOURCES.txt' 2025-07-17T10:11:24.5271455Z Copying torch_openreg.egg-info to ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg-0.0.1-py3.12.egg-info 2025-07-17T10:11:24.5282308Z running install_scripts 2025-07-17T10:11:24.9983203Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:11:24.9984708Z Executing ['/opt/conda/envs/py_3.12/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 10:11:24.998238] 2025-07-17T10:11:46.3576127Z 2025-07-17T10:11:46.3577185Z 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_acf58236ee0aa451_.log 2025-07-17T10:11:46.3580005Z 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 2025-07-17T10:11:46.3581073Z 2025-07-17T10:11:46.3581250Z Running test_cpp_extensions_stream_and_event 1/1 ... [2025-07-17 10:11:46.357605] 2025-07-17T10:11:46.3581560Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:11:46.3582387Z Executing ['/opt/conda/envs/py_3.12/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 10:11:46.357886] 2025-07-17T10:11:49.3792597Z 2025-07-17T10:11:49.3793751Z 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_886eb2ad250b8273_.log 2025-07-17T10:11:49.3794559Z Running 1 items in this shard: test/test_cpp_extensions_stream_and_event.py::TestCppExtensionStreamAndEvent::test_stream_event 2025-07-17T10:11:49.3794907Z 2025-07-17T10:11:49.3795662Z Running test_cuda_primary_ctx 1/1 ... [2025-07-17 10:11:49.379210] 2025-07-17T10:11:49.3796071Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:11:49.3796779Z Executing ['/opt/conda/envs/py_3.12/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 10:11:49.379517] 2025-07-17T10:12:06.3310570Z 2025-07-17T10:12:06.3312309Z 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_b6e384a789b8e280_.log 2025-07-17T10:12:06.3313956Z Running 4 items in this shard: test/test_cuda_primary_ctx.py::TestCudaPrimaryCtx::test_copy, test/test_cuda_primary_ctx.py::TestCudaPrimaryCtx::test_pin_memory, test/test_cuda_primary_ctx.py::TestCudaPrimaryCtx::test_set_device_0, test/test_cuda_primary_ctx.py::TestCudaPrimaryCtx::test_str_repr 2025-07-17T10:12:06.3315314Z Running 1 items in this shard: test/test_cuda_primary_ctx.py::TestCudaPrimaryCtx::test_copy 2025-07-17T10:12:06.3315916Z Running 1 items in this shard: test/test_cuda_primary_ctx.py::TestCudaPrimaryCtx::test_pin_memory 2025-07-17T10:12:06.3316566Z Running 1 items in this shard: test/test_cuda_primary_ctx.py::TestCudaPrimaryCtx::test_set_device_0 2025-07-17T10:12:06.3317194Z Running 1 items in this shard: test/test_cuda_primary_ctx.py::TestCudaPrimaryCtx::test_str_repr 2025-07-17T10:12:06.3318407Z 2025-07-17T10:12:06.3318576Z Running test_cuda_trace 1/1 ... [2025-07-17 10:12:06.331109] 2025-07-17T10:12:06.3318899Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:12:06.3320306Z Executing ['/opt/conda/envs/py_3.12/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 10:12:06.331396] 2025-07-17T10:12:49.0914977Z 2025-07-17T10:12:49.0915964Z test_cuda_trace 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_trace_1.1_e19b038b5060dad3_.log 2025-07-17T10:12:49.0918358Z Running 12 items in this shard: test/test_cuda_trace.py::TestCudaTrace::test_all_trace_callbacks_called, test/test_cuda_trace.py::TestCudaTrace::test_device_synchronization_callback, test/test_cuda_trace.py::TestCudaTrace::test_event_creation_callback, test/test_cuda_trace.py::TestCudaTrace::test_event_deletion_callback, test/test_cuda_trace.py::TestCudaTrace::test_event_record_callback, test/test_cuda_trace.py::TestCudaTrace::test_event_synchronization_callback, test/test_cuda_trace.py::TestCudaTrace::test_event_wait_callback, test/test_cuda_trace.py::TestCudaTrace::test_memcpy_synchronization, test/test_cuda_trace.py::TestCudaTrace::test_memory_allocation_callback, test/test_cuda_trace.py::TestCudaTrace::test_memory_deallocation_callback, test/test_cuda_trace.py::TestCudaTrace::test_stream_creation_callback, test/test_cuda_trace.py::TestCudaTrace::test_stream_synchronization_callback 2025-07-17T10:12:49.0921170Z Running 1 items in this shard: test/test_cuda_trace.py::TestCudaTrace::test_all_trace_callbacks_called 2025-07-17T10:12:49.0921633Z Running 1 items in this shard: test/test_cuda_trace.py::TestCudaTrace::test_device_synchronization_callback 2025-07-17T10:12:49.0922233Z Running 1 items in this shard: test/test_cuda_trace.py::TestCudaTrace::test_event_creation_callback 2025-07-17T10:12:49.0922863Z Running 1 items in this shard: test/test_cuda_trace.py::TestCudaTrace::test_event_deletion_callback 2025-07-17T10:12:49.0923558Z Running 1 items in this shard: test/test_cuda_trace.py::TestCudaTrace::test_event_record_callback 2025-07-17T10:12:49.0932301Z Running 1 items in this shard: test/test_cuda_trace.py::TestCudaTrace::test_event_synchronization_callback 2025-07-17T10:12:49.0932803Z Running 1 items in this shard: test/test_cuda_trace.py::TestCudaTrace::test_event_wait_callback 2025-07-17T10:12:49.0933435Z Running 1 items in this shard: test/test_cuda_trace.py::TestCudaTrace::test_memcpy_synchronization 2025-07-17T10:12:49.0933894Z Running 1 items in this shard: test/test_cuda_trace.py::TestCudaTrace::test_memory_allocation_callback 2025-07-17T10:12:49.0934351Z Running 1 items in this shard: test/test_cuda_trace.py::TestCudaTrace::test_memory_deallocation_callback 2025-07-17T10:12:49.0934810Z Running 1 items in this shard: test/test_cuda_trace.py::TestCudaTrace::test_stream_creation_callback 2025-07-17T10:12:49.0935272Z Running 1 items in this shard: test/test_cuda_trace.py::TestCudaTrace::test_stream_synchronization_callback 2025-07-17T10:12:49.0935555Z 2025-07-17T10:12:49.0935673Z Running test_dispatch 1/1 ... [2025-07-17 10:12:49.091450] 2025-07-17T10:12:49.0935940Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:12:49.0936573Z Executing ['/opt/conda/envs/py_3.12/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 10:12:49.091738] 2025-07-17T10:13:21.7756087Z 2025-07-17T10:13:21.7757041Z test_dispatch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_dispatch_1.1_63f2f8292e3a88fe_.log 2025-07-17T10:13:21.7763306Z 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 2025-07-17T10:13:21.7769047Z 2025-07-17T10:13:21.7769180Z Running test_extension_utils 1/1 ... [2025-07-17 10:13:21.775560] 2025-07-17T10:13:21.7769456Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:13:21.7770221Z Executing ['/opt/conda/envs/py_3.12/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 10:13:21.775849] 2025-07-17T10:13:25.1478260Z 2025-07-17T10:13:25.1479343Z test_extension_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_extension_utils_1.1_61854f1c36b95718_.log 2025-07-17T10:13:25.1480341Z 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 2025-07-17T10:13:25.1480880Z 2025-07-17T10:13:25.1481030Z Running test_fake_tensor 1/1 ... [2025-07-17 10:13:25.147753] 2025-07-17T10:13:25.1481312Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:13:25.1482153Z Executing ['/opt/conda/envs/py_3.12/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 10:13:25.148039] 2025-07-17T10:13:38.7914162Z 2025-07-17T10:13:38.7915290Z test_fake_tensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_fake_tensor_1.1_f7e65959552ff9fc_.log 2025-07-17T10:13:38.7973997Z Running 273 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::FakeTensorOpInfoTestCUDA::test_fake_NumpyCatCustomOp_cuda_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCUDA::test_fake_NumpyCubeCustomOp_cuda_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCUDA::test_fake_NumpyMulCustomOp_cuda_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCUDA::test_fake_NumpyMulScalarCustomOp_cuda_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCUDA::test_fake_NumpyNMSCustomOp_cuda_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCUDA::test_fake_NumpyNonzeroCustomOp_cuda_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCUDA::test_fake_NumpySortCustomOp_cuda_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCUDA::test_fake_NumpySplitCopyCustomOp_cuda_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCUDA::test_fake_NumpySplitCopyWithIntCustomOp_cuda_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCUDA::test_fake_NumpyTakeCustomOp_cuda_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCUDA::test_fake_NumpyViewCopyCustomOp_cuda_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 2025-07-17T10:13:38.8031974Z 2025-07-17T10:13:38.8032099Z Running test_fx 1/1 ... [2025-07-17 10:13:38.791791] 2025-07-17T10:13:38.8032366Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:13:38.8032966Z Executing ['/opt/conda/envs/py_3.12/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 10:13:38.792087] 2025-07-17T10:16:23.2576068Z 2025-07-17T10:16:23.2576987Z test_fx 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_fx_1.1_4bf35a643e876af7_.log 2025-07-17T10:16:23.2859839Z Running 1267 items in this shard: test/test_fx.py::TestCommonPass::test_correctness_CSEPass_MutationInput_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_MutationInput_cuda, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_MutationMetadata_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_MutationMetadata_cuda, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_MutationTorchTensorCall_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_MutationTorchTensorCall_cuda, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_Mutation_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_Mutation_cuda, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_ReturnList_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_ReturnList_cuda, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_TakeList_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_TakeList_cuda, test/test_fx.py::TestCommonPass::test_correctness_factory_CSEPass_FactoryFunctionCall_cpu, test/test_fx.py::TestCommonPass::test_correctness_factory_CSEPass_FactoryFunctionCall_cuda, test/test_fx.py::TestCommonPass::test_correctness_factory_CSEPass_MutationFactory_cpu, test/test_fx.py::TestCommonPass::test_correctness_factory_CSEPass_MutationFactory_cuda, 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::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_H_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_T_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive___getitem___cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive___radd___cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive___rdiv___cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive___rmatmul___cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive___rmod___cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive___rmul___cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive___rpow___cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive___rsub___cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive__batch_norm_with_update_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive__chunk_cat_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive__native_batch_norm_legit_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive__segment_reduce_lengths_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive__segment_reduce_offsets_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive__softmax_backward_data_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive__unsafe_masked_index_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive__unsafe_masked_index_put_accumulate_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive__upsample_bilinear2d_aa_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_abs_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_acos_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_acosh_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_add_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_addbmm_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_addcdiv_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_addcmul_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_addmm_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_addmm_decomposed_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_addmv_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_addr_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_alias_copy_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_all_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_allclose_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_amax_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_amin_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_aminmax_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_angle_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_any_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_arange_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_argmax_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_argmin_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_argsort_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_argwhere_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_as_strided_copy_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_as_strided_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_as_strided_partial_views_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_as_strided_scatter_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_asin_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_asinh_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_atan2_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_atan_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_atanh_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_atleast_1d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_atleast_2d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_atleast_3d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_baddbmm_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_bernoulli_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_bfloat16_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_block_diag_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_bmm_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_bool_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_broadcast_shapes_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_broadcast_tensors_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_broadcast_to_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_bucketize_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_byte_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_cartesian_prod_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_cat_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_cauchy_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_cdist_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_cdouble_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_ceil_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_cfloat_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_chalf_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_char_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_cholesky_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_cholesky_inverse_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_cholesky_solve_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_chunk_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_clamp_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_clamp_max_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_clamp_min_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_clone_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_column_stack_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_combinations_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_complex_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_conj_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_conj_physical_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_constant_pad_nd_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_contiguous_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_copysign_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_corrcoef_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_cos_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_cosh_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_count_nonzero_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_cov_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_cross_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_cummax_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_cummin_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_cumprod_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_cumsum_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_cumulative_trapezoid_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_deg2rad_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_diag_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_diag_embed_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_diagflat_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_diagonal_copy_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_diagonal_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_diagonal_scatter_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_diff_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_digamma_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_dist_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_div_floor_rounding_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_div_no_rounding_mode_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_div_trunc_rounding_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_dot_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_double_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_dsplit_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_dstack_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_einsum_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_empty_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_empty_like_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_empty_permuted_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_empty_strided_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_eq_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_equal_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_erf_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_erfc_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_erfinv_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_exp2_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_exp_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_expand_as_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_expand_copy_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_expand_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_expm1_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_exponential_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_eye_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fft_fft2_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fft_fft_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fft_fftn_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fft_fftshift_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fft_hfft2_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fft_hfft_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fft_hfftn_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fft_ifft2_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fft_ifft_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fft_ifftn_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fft_ifftshift_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fft_ihfft2_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fft_ihfft_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fft_ihfftn_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fft_irfft2_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fft_irfft_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fft_irfftn_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fft_rfft2_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fft_rfft_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fft_rfftn_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fill_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_flatten_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_flip_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fliplr_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_flipud_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_float_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_float_power_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_floor_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_floor_divide_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fmax_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fmin_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_fmod_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_frac_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_frexp_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_full_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_full_like_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_gather_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_ge_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_geometric_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_geqrf_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_gradient_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_grid_sampler_2d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_gt_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_half_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_heaviside_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_histc_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_hsplit_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_hstack_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_hypot_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_i0_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_igamma_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_igammac_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_index_add_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_index_copy_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_index_fill_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_index_put_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_index_reduce_amax_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_index_reduce_amin_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_index_reduce_mean_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_index_reduce_prod_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_index_select_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_inner_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_int_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_isclose_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_isfinite_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_isin_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_isinf_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_isnan_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_isneginf_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_isposinf_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_isreal_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_item_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_jiterator_2inputs_2outputs_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_jiterator_4inputs_with_extra_args_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_jiterator_binary_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_jiterator_binary_return_by_ref_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_jiterator_unary_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_kron_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_kthvalue_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_ldexp_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_le_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_lerp_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_lgamma_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_cholesky_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_cholesky_ex_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_cond_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_cross_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_det_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_diagonal_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_eig_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_eigh_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_eigvals_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_eigvalsh_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_householder_product_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_inv_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_inv_ex_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_ldl_factor_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_ldl_factor_ex_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_ldl_solve_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_lstsq_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_lstsq_grad_oriented_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_lu_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_lu_factor_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_lu_factor_ex_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_lu_solve_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_matrix_norm_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_matrix_power_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_matrix_rank_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_matrix_rank_hermitian_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_multi_dot_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_norm_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_norm_subgradients_at_zero_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_pinv_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_pinv_hermitian_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_pinv_singular_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_qr_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_slogdet_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_solve_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_solve_ex_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_solve_triangular_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_svd_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_svdvals_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_tensorinv_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_tensorsolve_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_vander_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_vecdot_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linalg_vector_norm_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linspace_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_linspace_tensor_overload_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_log10_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_log1p_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_log2_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_log_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_log_normal_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_log_softmax_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_log_softmax_with_dtype_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_logaddexp2_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_logaddexp_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_logcumsumexp_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_logdet_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_logical_and_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_logical_not_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_logical_or_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_logical_xor_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_logit_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_logspace_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_logspace_tensor_overload_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_logsumexp_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_long_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_lt_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_lu_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_lu_solve_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_lu_unpack_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_mH_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_mT_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_masked_amax_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_masked_amin_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_masked_argmax_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_masked_argmin_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_masked_cumprod_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_masked_cumsum_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_masked_fill_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_masked_log_softmax_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_masked_logaddexp_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_masked_logsumexp_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_masked_mean_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_masked_median_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_masked_norm_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_masked_normalize_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_masked_prod_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_masked_scatter_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_masked_select_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_masked_softmax_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_masked_softmin_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_masked_std_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_masked_sum_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_masked_var_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_matmul_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_matrix_exp_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_max_binary_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_max_pool2d_with_indices_backward_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_max_reduction_no_dim_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_max_reduction_with_dim_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_maximum_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_mean_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_median_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_meshgrid_list_of_tensors_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_meshgrid_variadic_tensors_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_min_binary_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_min_reduction_no_dim_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_min_reduction_with_dim_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_minimum_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_mm_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_mode_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_movedim_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_msort_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_mul_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_multinomial_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_mv_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_mvlgamma_mvlgamma_p_1_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_mvlgamma_mvlgamma_p_3_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_mvlgamma_mvlgamma_p_5_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nan_to_num_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nanmean_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nanmedian_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nanquantile_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nansum_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_narrow_copy_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_narrow_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_native_batch_norm_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_native_dropout_backward_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_native_layer_norm_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_ne_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_neg_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_new_empty_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_new_empty_strided_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_new_full_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_new_ones_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_new_zeros_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nextafter_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_adaptive_avg_pool1d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_adaptive_avg_pool2d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_adaptive_avg_pool3d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_adaptive_max_pool1d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_adaptive_max_pool2d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_adaptive_max_pool3d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_alpha_dropout_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_avg_pool1d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_avg_pool2d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_avg_pool3d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_batch_norm_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_batch_norm_without_cudnn_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_bilinear_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_binary_cross_entropy_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_binary_cross_entropy_with_logits_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_celu_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_channel_shuffle_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_conv1d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_conv2d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_conv3d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_conv_transpose1d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_conv_transpose2d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_conv_transpose3d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_cosine_embedding_loss_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_cosine_similarity_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_cross_entropy_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_ctc_loss_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_dropout2d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_dropout3d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_dropout_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_elu_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_embedding_bag_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_embedding_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_feature_alpha_dropout_with_train_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_feature_alpha_dropout_without_train_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_fractional_max_pool2d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_fractional_max_pool3d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_gaussian_nll_loss_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_gelu_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_glu_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_grid_sample_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_group_norm_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_hardshrink_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_hardsigmoid_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_hardswish_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_hardtanh_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_hinge_embedding_loss_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_huber_loss_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_instance_norm_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_area_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_bicubic_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_bilinear_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_linear_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_nearest-exact_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_nearest_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_trilinear_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_kl_div_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_l1_loss_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_layer_norm_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_leaky_relu_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_linear_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_local_response_norm_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_logsigmoid_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_margin_ranking_loss_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_max_pool1d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_max_pool2d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_max_pool3d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_max_unpool1d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_max_unpool1d_grad_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_max_unpool2d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_max_unpool2d_grad_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_max_unpool3d_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_max_unpool3d_grad_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_mish_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_mse_loss_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_multi_head_attention_forward_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_multi_margin_loss_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_multilabel_margin_loss_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_multilabel_soft_margin_loss_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_nll_loss_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_normalize_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_pad_circular_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_pad_constant_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_pad_reflect_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_pad_replicate_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_pad_replicate_negative_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_pairwise_distance_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_pdist_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_pixel_shuffle_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_pixel_unshuffle_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_poisson_nll_loss_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_prelu_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_relu6_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_relu_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_rms_norm_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_rrelu_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_scaled_dot_product_attention_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_selu_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_silu_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_smooth_l1_loss_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_soft_margin_loss_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_softmin_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_softmin_with_dtype_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_softplus_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_softshrink_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_softsign_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_tanhshrink_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_threshold_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_triplet_margin_loss_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_triplet_margin_with_distance_loss_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_unfold_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_upsample_bilinear_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nn_functional_upsample_nearest_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nonzero_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_nonzero_static_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_norm_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_norm_fro_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_norm_inf_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_norm_nuc_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_normal_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_normal_in_place_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_normal_number_mean_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_ones_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_ones_like_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_ormqr_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_outer_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_pca_lowrank_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_permute_copy_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_permute_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_pinverse_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_polar_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_polygamma_polygamma_n_0_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_polygamma_polygamma_n_1_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_polygamma_polygamma_n_2_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_polygamma_polygamma_n_3_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_polygamma_polygamma_n_4_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_positive_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_pow_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_prod_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_put_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_qr_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_quantile_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_rad2deg_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_rand_like_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_randint_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_randint_like_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_randn_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_randn_like_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_ravel_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_real_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_reciprocal_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_remainder_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_renorm_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_repeat_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_repeat_interleave_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_reshape_as_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_reshape_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_resize__cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_resize_as__cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_resolve_conj_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_resolve_neg_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_roll_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_rot90_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_round_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_round_decimals_0_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_round_decimals_3_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_round_decimals_neg_3_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_rsqrt_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_rsub_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_scalar_tensor_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_scatter_add_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_scatter_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_scatter_reduce_amax_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_scatter_reduce_amin_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_scatter_reduce_mean_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_scatter_reduce_prod_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_scatter_reduce_sum_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_searchsorted_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_select_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_select_scatter_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_sgn_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_short_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_sigmoid_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_sign_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_signal_windows_bartlett_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_signal_windows_blackman_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_signal_windows_cosine_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_signal_windows_exponential_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_signal_windows_gaussian_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_signal_windows_general_cosine_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_signal_windows_general_hamming_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_signal_windows_hamming_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_signal_windows_hann_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_signal_windows_kaiser_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_signal_windows_nuttall_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_signbit_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_sin_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_sinc_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_sinh_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_slice_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_slice_scatter_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_softmax_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_softmax_with_dtype_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_sort_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_sparse_mm_reduce_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_sparse_sampled_addmm_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_airy_ai_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_bessel_j0_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_bessel_j1_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_bessel_y0_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_bessel_y1_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_chebyshev_polynomial_t_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_chebyshev_polynomial_u_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_chebyshev_polynomial_v_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_chebyshev_polynomial_w_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_entr_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_erfcx_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_hermite_polynomial_h_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_hermite_polynomial_he_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_i0e_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_i1_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_i1e_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_laguerre_polynomial_l_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_legendre_polynomial_p_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_log_ndtr_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_modified_bessel_i0_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_modified_bessel_i1_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_modified_bessel_k0_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_modified_bessel_k1_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_ndtr_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_ndtri_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_polygamma_special_polygamma_n_0_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_scaled_modified_bessel_k0_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_scaled_modified_bessel_k1_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_shifted_chebyshev_polynomial_t_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_shifted_chebyshev_polynomial_u_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_shifted_chebyshev_polynomial_v_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_shifted_chebyshev_polynomial_w_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_spherical_bessel_j0_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_xlog1py_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_special_zeta_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_split_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_split_list_args_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_split_with_sizes_copy_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_split_with_sizes_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_sqrt_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_square_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_squeeze_copy_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_squeeze_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_squeeze_multiple_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_stack_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_std_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_std_mean_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_std_mean_unbiased_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_std_unbiased_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_stft_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_sub_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_sum_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_sum_to_size_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_svd_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_svd_lowrank_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_t_copy_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_t_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_take_along_dim_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_take_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_tan_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_tanh_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_tensor_split_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_tensordot_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_tile_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_to_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_to_sparse_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_topk_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_torch_ops_aten__efficient_attention_forward_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_torch_ops_aten__safe_softmax_default_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_trace_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_transpose_copy_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_transpose_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_trapezoid_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_trapz_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_triangular_solve_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_tril_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_triu_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_true_divide_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_trunc_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_unbind_copy_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_unbind_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_unflatten_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_unfold_copy_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_unfold_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_uniform_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_unique_consecutive_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_unique_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_unsafe_chunk_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_unsafe_split_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_unsqueeze_copy_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_unsqueeze_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_var_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_var_mean_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_var_mean_unbiased_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_var_unbiased_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_vdot_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_view_as_complex_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_view_as_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_view_copy_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_view_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_vsplit_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_vstack_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_where_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_xlogy_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_zero__cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_zeros_cuda_float32, test/test_fx.py::TestOperatorSignaturesCUDA::test_get_torch_func_signature_exhaustive_zeros_like_cuda_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 2025-07-17T10:16:23.3118410Z 2025-07-17T10:16:23.3118569Z Running test_jit_disabled 1/1 ... [2025-07-17 10:16:23.259098] 2025-07-17T10:16:23.3118852Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:16:23.3119480Z Executing ['/opt/conda/envs/py_3.12/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 10:16:23.259405] 2025-07-17T10:16:26.8817128Z 2025-07-17T10:16:26.8818154Z test_jit_disabled 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_disabled_1.1_a9ee769930f7c047_.log 2025-07-17T10:16:26.8819137Z 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 2025-07-17T10:16:26.8819723Z 2025-07-17T10:16:26.8819904Z Running test_mobile_optimizer 1/1 ... [2025-07-17 10:16:26.881718] 2025-07-17T10:16:26.8820229Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:16:26.8821556Z Executing ['/opt/conda/envs/py_3.12/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 10:16:26.881998] 2025-07-17T10:16:31.9568845Z 2025-07-17T10:16:31.9569884Z test_mobile_optimizer 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mobile_optimizer_1.1_038ef7a72e985ef9_.log 2025-07-17T10:16:31.9571689Z 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 2025-07-17T10:16:31.9586782Z 2025-07-17T10:16:31.9586940Z Running test_multiprocessing 1/1 ... [2025-07-17 10:16:31.956833] 2025-07-17T10:16:31.9587226Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:16:31.9587851Z Executing ['/opt/conda/envs/py_3.12/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 10:16:31.957123] 2025-07-17T10:17:36.2079464Z 2025-07-17T10:17:36.2080411Z test_multiprocessing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_1.1_9f32f3734254d119_.log 2025-07-17T10:17:36.2089137Z 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 2025-07-17T10:17:36.2097039Z 2025-07-17T10:17:36.2097195Z Running test_multiprocessing_spawn 1/1 ... [2025-07-17 10:17:36.207850] 2025-07-17T10:17:36.2097504Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:17:36.2098304Z Executing ['/opt/conda/envs/py_3.12/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 10:17:36.208137] 2025-07-17T10:19:46.4372183Z 2025-07-17T10:19:46.4373249Z test_multiprocessing_spawn 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_spawn_1.1_c0754191c4fba05a_.log 2025-07-17T10:19:46.4380347Z 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 2025-07-17T10:19:46.4386730Z 2025-07-17T10:19:46.4386860Z Running test_native_mha 1/1 ... [2025-07-17 10:19:46.437188] 2025-07-17T10:19:46.4387127Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:19:46.4387752Z Executing ['/opt/conda/envs/py_3.12/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 10:19:46.437475] 2025-07-17T10:19:57.0744502Z 2025-07-17T10:19:57.0745630Z test_native_mha 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_native_mha_1.1_c51fde92c7ef3da4_.log 2025-07-17T10:19:57.0768753Z Running 54 items in this shard: test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_attention_cuda_float16, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_attention_cuda_float32, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_encoder_decoder_attention_cuda_float16, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_encoder_decoder_attention_cuda_float32, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cuda_float16, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cuda_float32, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cuda_float16, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cuda_float32, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cuda_float16, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cuda_float32, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cuda_float16, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cuda_float32, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cuda_float16, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cuda_float32, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cuda_float16, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cuda_float32, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cuda_float16, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cuda_float32, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cuda_float16, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cuda_float32, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_False_cuda_float16, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_False_cuda_float32, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_True_cuda_float16, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_True_cuda_float32, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_False_cuda_float16, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_False_cuda_float32, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_True_cuda_float16, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_True_cuda_float32, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cuda_float16, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cuda_float32, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cuda_float16, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cuda_float32, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cuda_float16, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cuda_float32, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cuda_float16, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cuda_float32, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cuda_float16, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cuda_float32, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cuda_float16, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cuda_float32, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cuda_float16, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cuda_float32, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cuda_float16, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cuda_float32, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_False_cuda_float16, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_False_cuda_float32, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_True_cuda_float16, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_True_cuda_float32, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_False_cuda_float16, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_False_cuda_float32, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_True_cuda_float16, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_True_cuda_float32, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_transform_bias_rescale_qkv_cuda_float32, test/test_native_mha.py::TestMHADeviceTypeCUDA::test_transform_bias_rescale_qkv_nested_cuda_float32 2025-07-17T10:19:57.0793193Z 2025-07-17T10:19:57.0793320Z Running test_openreg 1/1 ... [2025-07-17 10:19:57.074434] 2025-07-17T10:19:58.6896682Z -- The CXX compiler identification is GNU 11.4.0 2025-07-17T10:19:58.8025221Z -- The C compiler identification is GNU 11.4.0 2025-07-17T10:19:58.8443049Z -- Detecting CXX compiler ABI info 2025-07-17T10:19:59.2924655Z -- Detecting CXX compiler ABI info - done 2025-07-17T10:19:59.3070730Z -- Check for working CXX compiler: /opt/cache/bin/c++ - skipped 2025-07-17T10:19:59.3073275Z -- Detecting CXX compile features 2025-07-17T10:19:59.3077614Z -- Detecting CXX compile features - done 2025-07-17T10:19:59.3204519Z -- Detecting C compiler ABI info 2025-07-17T10:19:59.7553477Z -- Detecting C compiler ABI info - done 2025-07-17T10:19:59.7696252Z -- Check for working C compiler: /opt/cache/bin/cc - skipped 2025-07-17T10:19:59.7699083Z -- Detecting C compile features 2025-07-17T10:19:59.7702603Z -- Detecting C compile features - done 2025-07-17T10:19:59.8395598Z Building PyTorch for GPU arch: gfx90a;gfx942 2025-07-17T10:20:00.0273597Z -- Found HIP: /opt/rocm (found suitable version "6.4.43483-a187df25c", minimum required is "1.0") 2025-07-17T10:20:00.0288332Z HIP VERSION: 6.4.43483-a187df25c 2025-07-17T10:20:00.2654402Z -- Performing Test CMAKE_HAVE_LIBC_PTHREAD 2025-07-17T10:20:00.7898798Z -- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Success 2025-07-17T10:20:00.7905025Z -- Found Threads: TRUE 2025-07-17T10:20:00.8365100Z hip VERSION: 6.4.43483 2025-07-17T10:20:00.8386427Z -- Reading ROCM version from: /opt/rocm/include/rocm-core/rocm_version.h 2025-07-17T10:20:00.8386781Z -- Content: 2025-07-17T10:20:00.8389277Z  2025-07-17T10:20:00.8389541Z ***** ROCm version from rocm_version.h **** 2025-07-17T10:20:00.8389775Z  2025-07-17T10:20:00.8389978Z ROCM_VERSION_DEV: 6.4.1 2025-07-17T10:20:00.8390216Z ROCM_VERSION_DEV_MAJOR: 6 2025-07-17T10:20:00.8390440Z ROCM_VERSION_DEV_MINOR: 4 2025-07-17T10:20:00.8390655Z ROCM_VERSION_DEV_PATCH: 1 2025-07-17T10:20:00.8390894Z ROCM_VERSION_DEV_INT: 60401 2025-07-17T10:20:00.8391141Z HIP_VERSION_MAJOR: 6 2025-07-17T10:20:00.8391362Z HIP_VERSION_MINOR: 4 2025-07-17T10:20:00.8391573Z TORCH_HIP_VERSION: 604 2025-07-17T10:20:00.8391761Z  2025-07-17T10:20:00.8391950Z ***** Library versions from cmake find_package ***** 2025-07-17T10:20:00.8392587Z  2025-07-17T10:20:00.8394293Z amd_comgr VERSION: 3.0.0 2025-07-17T10:20:00.8891970Z rocrand VERSION: 3.3.0 2025-07-17T10:20:00.8925359Z hiprand VERSION: 2.12.0 2025-07-17T10:20:00.8945234Z rocblas VERSION: 4.4.0 2025-07-17T10:20:00.8987214Z hipblas VERSION: 2.4.0 2025-07-17T10:20:00.9012450Z miopen VERSION: 3.4.0 2025-07-17T10:20:00.9033461Z hipfft VERSION: 1.0.18 2025-07-17T10:20:00.9054201Z hipsparse VERSION: 3.2.0 2025-07-17T10:20:00.9074373Z rocprim VERSION: 3.4.0 2025-07-17T10:20:00.9103355Z hipcub VERSION: 3.4.0 2025-07-17T10:20:00.9124015Z rocthrust VERSION: 3.3.0 2025-07-17T10:20:00.9144058Z hipsolver VERSION: 2.4.0 2025-07-17T10:20:00.9173663Z rocsolver VERSION: 3.28.0 2025-07-17T10:20:00.9174292Z CMake Warning at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Caffe2/public/LoadHIP.cmake:175 (message): 2025-07-17T10:20:00.9174805Z Work around hiprtc cmake failure for cmake >= 4 2025-07-17T10:20:00.9175057Z Call Stack (most recent call first): 2025-07-17T10:20:00.9175448Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Caffe2/Caffe2Config.cmake:74 (include) 2025-07-17T10:20:00.9175991Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Torch/TorchConfig.cmake:68 (find_package) 2025-07-17T10:20:00.9176550Z CMakeLists.txt:27 (find_package) 2025-07-17T10:20:00.9176692Z 2025-07-17T10:20:00.9176770Z  2025-07-17T10:20:00.9188966Z CMake Deprecation Warning at /opt/rocm/lib/cmake/hiprtc/hiprtc-config.cmake:21 (cmake_minimum_required): 2025-07-17T10:20:00.9189476Z Compatibility with CMake < 3.10 will be removed from a future version of 2025-07-17T10:20:00.9189767Z CMake. 2025-07-17T10:20:00.9189871Z 2025-07-17T10:20:00.9190033Z Update the VERSION argument value. Or, use the ... syntax 2025-07-17T10:20:00.9190385Z to tell CMake that the project requires at least but has been updated 2025-07-17T10:20:00.9190716Z to work with policies introduced by or earlier. 2025-07-17T10:20:00.9190964Z Call Stack (most recent call first): 2025-07-17T10:20:00.9191375Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Caffe2/public/LoadHIP.cmake:67 (find_package) 2025-07-17T10:20:00.9192029Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Caffe2/public/LoadHIP.cmake:177 (find_package_and_print_version) 2025-07-17T10:20:00.9192625Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Caffe2/Caffe2Config.cmake:74 (include) 2025-07-17T10:20:00.9193154Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Torch/TorchConfig.cmake:68 (find_package) 2025-07-17T10:20:00.9193539Z CMakeLists.txt:27 (find_package) 2025-07-17T10:20:00.9193901Z 2025-07-17T10:20:00.9193979Z  2025-07-17T10:20:00.9194182Z hiprtc VERSION: 6.4.43483 2025-07-17T10:20:00.9214888Z hipblaslt VERSION: 0.12.1 2025-07-17T10:20:00.9749410Z rccl VERSION: 2.22.3 2025-07-17T10:20:00.9754193Z hsa-runtime64 VERSION: 1.15.60401 2025-07-17T10:20:00.9776998Z hipsparselt VERSION: 0.2.3 2025-07-17T10:20:02.3829349Z hipblaslt is using scale pointer vec ext 2025-07-17T10:20:02.5038748Z CMake Warning at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Torch/TorchConfig.cmake:22 (message): 2025-07-17T10:20:02.5039326Z static library kineto_LIBRARY-NOTFOUND not found. 2025-07-17T10:20:02.5039586Z Call Stack (most recent call first): 2025-07-17T10:20:02.5040022Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Torch/TorchConfig.cmake:125 (append_torchlib_if_found) 2025-07-17T10:20:02.5040459Z CMakeLists.txt:27 (find_package) 2025-07-17T10:20:02.5040593Z 2025-07-17T10:20:02.5040690Z  2025-07-17T10:20:02.5043997Z -- Found Torch: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch.so 2025-07-17T10:20:02.5077626Z -- Configuring done (4.3s) 2025-07-17T10:20:02.5228981Z -- Generating done (0.0s) 2025-07-17T10:20:02.5233570Z -- Build files have been written to: /var/lib/jenkins/pytorch/test/cpp_extensions/open_registration_extension/torch_openreg/build 2025-07-17T10:20:02.6568891Z [ 5%] Building CXX object third_party/openreg/CMakeFiles/openreg.dir/csrc/memory.cpp.o 2025-07-17T10:20:02.6569514Z [ 11%] Building CXX object third_party/openreg/CMakeFiles/openreg.dir/csrc/device.cpp.o 2025-07-17T10:20:02.7745876Z [ 17%] Linking CXX shared library libopenreg.so 2025-07-17T10:20:02.8596047Z [ 17%] Built target openreg 2025-07-17T10:20:02.8698152Z [ 23%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/OpenRegExtra.cpp.o 2025-07-17T10:20:02.8704922Z [ 29%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/OpenRegMinimal.cpp.o 2025-07-17T10:20:02.8706863Z [ 41%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/native/Extra.cpp.o 2025-07-17T10:20:02.8707401Z [ 41%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegFunctions.cpp.o 2025-07-17T10:20:02.8710310Z [ 52%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/native/Minimal.cpp.o 2025-07-17T10:20:02.8710870Z [ 52%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegDeviceAllocator.cpp.o 2025-07-17T10:20:02.8716565Z [ 76%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegGenerator.cpp.o 2025-07-17T10:20:02.8717106Z [ 76%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegHostAllocator.cpp.o 2025-07-17T10:20:02.8717620Z [ 76%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegHooks.cpp.o 2025-07-17T10:20:02.8718131Z [ 76%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegSerialization.cpp.o 2025-07-17T10:20:02.8720659Z [ 82%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegGuard.cpp.o 2025-07-17T10:20:03.5700362Z [ 88%] Linking CXX shared library libtorch_openreg.so 2025-07-17T10:20:03.9765968Z [ 88%] Built target torch_openreg 2025-07-17T10:20:03.9866283Z [ 94%] Building CXX object torch_openreg/csrc/CMakeFiles/torch_bindings.dir/Module.cpp.o 2025-07-17T10:20:04.6668879Z [100%] Linking CXX shared library libtorch_bindings.so 2025-07-17T10:20:04.8272836Z [100%] Built target torch_bindings 2025-07-17T10:20:04.8357346Z Install the project... 2025-07-17T10:20:04.8390147Z -- Install configuration: "" 2025-07-17T10:20:04.8922156Z running install 2025-07-17T10:20:04.8922788Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:90: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-07-17T10:20:04.8923307Z !! 2025-07-17T10:20:04.8923397Z 2025-07-17T10:20:04.8924119Z ******************************************************************************** 2025-07-17T10:20:04.8924387Z Please avoid running ``setup.py`` directly. 2025-07-17T10:20:04.8924652Z Instead, use pypa/build, pypa/installer or other 2025-07-17T10:20:04.8924887Z standards-based tools. 2025-07-17T10:20:04.8925005Z 2025-07-17T10:20:04.8925153Z By 2025-Oct-31, you need to update your project and remove deprecated calls 2025-07-17T10:20:04.8925443Z or your builds will no longer be supported. 2025-07-17T10:20:04.8925613Z 2025-07-17T10:20:04.8925828Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-07-17T10:20:04.8926151Z ******************************************************************************** 2025-07-17T10:20:04.8926298Z 2025-07-17T10:20:04.8926364Z !! 2025-07-17T10:20:04.8926523Z self.initialize_options() 2025-07-17T10:20:04.9040252Z running build 2025-07-17T10:20:04.9040465Z running build_py 2025-07-17T10:20:04.9114448Z creating build/lib.linux-x86_64-cpython-312/torch_openreg 2025-07-17T10:20:04.9115984Z copying torch_openreg/__init__.py -> build/lib.linux-x86_64-cpython-312/torch_openreg 2025-07-17T10:20:04.9118545Z creating build/lib.linux-x86_64-cpython-312/torch_openreg/openreg 2025-07-17T10:20:04.9119772Z copying torch_openreg/openreg/random.py -> build/lib.linux-x86_64-cpython-312/torch_openreg/openreg 2025-07-17T10:20:04.9121787Z copying torch_openreg/openreg/__init__.py -> build/lib.linux-x86_64-cpython-312/torch_openreg/openreg 2025-07-17T10:20:04.9127298Z creating build/lib.linux-x86_64-cpython-312/torch_openreg/lib 2025-07-17T10:20:04.9128558Z copying torch_openreg/lib/libtorch_openreg.so -> build/lib.linux-x86_64-cpython-312/torch_openreg/lib 2025-07-17T10:20:04.9144572Z copying torch_openreg/lib/libtorch_bindings.so -> build/lib.linux-x86_64-cpython-312/torch_openreg/lib 2025-07-17T10:20:04.9150623Z copying torch_openreg/lib/libopenreg.so -> build/lib.linux-x86_64-cpython-312/torch_openreg/lib 2025-07-17T10:20:04.9153560Z running build_ext 2025-07-17T10:20:04.9240820Z building 'torch_openreg._C' extension 2025-07-17T10:20:04.9242055Z creating build/temp.linux-x86_64-cpython-312/torch_openreg/csrc 2025-07-17T10:20:04.9244981Z gcc -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/include/python3.12 -c torch_openreg/csrc/stub.c -o build/temp.linux-x86_64-cpython-312/torch_openreg/csrc/stub.o -g -Wall -Werror 2025-07-17T10:20:04.9613545Z gcc -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/torch_openreg/csrc/stub.o -L/var/lib/jenkins/pytorch/test/cpp_extensions/open_registration_extension/torch_openreg/torch_openreg/lib -ltorch_bindings -o build/lib.linux-x86_64-cpython-312/torch_openreg/_C.cpython-312-x86_64-linux-gnu.so -Wl,-rpath,$ORIGIN/lib 2025-07-17T10:20:05.0119970Z running install_lib 2025-07-17T10:20:05.0198997Z copying build/lib.linux-x86_64-cpython-312/torch_openreg/_C.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg 2025-07-17T10:20:05.0203713Z copying build/lib.linux-x86_64-cpython-312/torch_openreg/lib/libtorch_openreg.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg/lib 2025-07-17T10:20:05.0224087Z copying build/lib.linux-x86_64-cpython-312/torch_openreg/lib/libtorch_bindings.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg/lib 2025-07-17T10:20:05.0231563Z copying build/lib.linux-x86_64-cpython-312/torch_openreg/lib/libopenreg.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg/lib 2025-07-17T10:20:05.0236882Z running install_egg_info 2025-07-17T10:20:05.0397318Z running egg_info 2025-07-17T10:20:05.0464485Z writing torch_openreg.egg-info/PKG-INFO 2025-07-17T10:20:05.0468410Z writing dependency_links to torch_openreg.egg-info/dependency_links.txt 2025-07-17T10:20:05.0469628Z writing requirements to torch_openreg.egg-info/requires.txt 2025-07-17T10:20:05.0470711Z writing top-level names to torch_openreg.egg-info/top_level.txt 2025-07-17T10:20:05.0545437Z reading manifest file 'torch_openreg.egg-info/SOURCES.txt' 2025-07-17T10:20:05.0552421Z writing manifest file 'torch_openreg.egg-info/SOURCES.txt' 2025-07-17T10:20:05.0554189Z removing './install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg-0.0.1-py3.12.egg-info' (and everything under it) 2025-07-17T10:20:05.0558788Z Copying torch_openreg.egg-info to ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg-0.0.1-py3.12.egg-info 2025-07-17T10:20:05.0568545Z running install_scripts 2025-07-17T10:20:05.5305873Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:20:05.5307244Z Executing ['/opt/conda/envs/py_3.12/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 10:20:05.530460] 2025-07-17T10:20:09.7536913Z 2025-07-17T10:20:09.7537960Z test_openreg 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_openreg_1.1_b7d48348491a3d5c_.log 2025-07-17T10:20:09.7544343Z 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 2025-07-17T10:20:09.7550255Z 2025-07-17T10:20:09.7550384Z Running test_overrides 1/1 ... [2025-07-17 10:20:09.753711] 2025-07-17T10:20:09.7550645Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:20:09.7551265Z Executing ['/opt/conda/envs/py_3.12/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 10:20:09.754017] 2025-07-17T10:20:15.4806145Z 2025-07-17T10:20:15.4807110Z test_overrides 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_overrides_1.1_db331badefbf9f21_.log 2025-07-17T10:20:15.5086849Z Running 1467 items in this shard: test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_H___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_T___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__backward_hooks___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__base___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__cdata___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__grad___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__grad_fn___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__post_accumulate_grad_hooks___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__version___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_data___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_device___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_dtype___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_imag___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_cpu___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_cuda___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_ipu___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_leaf___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_maia___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_meta___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_mkldnn___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_mps___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_mtia___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_nested___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_quantized___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_sparse___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_sparse_csr___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_vulkan___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_xla___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_xpu___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_itemsize___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_layout___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_mH___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_mT___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_name___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_names___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_nbytes___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_ndim___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_output_nr___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_real___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_requires_grad___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_retains_grad___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_shape___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_volatile___get__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___add__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___and__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___array__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___array_wrap__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___bool__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___complex__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___contains__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___cuda_array_interface_____get__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___deepcopy__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___div__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___dlpack__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___dlpack_device__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___eq__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___float__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___floordiv__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___format__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___ge__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___getitem__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___gt__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___iadd__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___iand__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___idiv__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___ifloordiv__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___ilshift__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___imod__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___imul__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___index__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___int__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___invert__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___ior__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___irshift__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___isub__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___ixor__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___le__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___len__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___long__, 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test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___reversed__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rfloordiv__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rlshift__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rmatmul__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rmod__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rmul__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___ror__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rpow__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rrshift__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rshift__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rsub__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rxor__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___setitem__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___setstate__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___sub__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___truediv__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___xor__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__autocast_to_full_precision, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__autocast_to_reduced_precision, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__clear_non_serializable_cached_data, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__coalesced_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__dimI, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__dimV, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__indices, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__is_view, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__nested_tensor_size, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__nested_tensor_storage_offsets, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__nested_tensor_strides, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__nnz, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__sparse_mask_projection, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__to_dense, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__update_names, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__values, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_abs, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_abs_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_absolute, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_absolute_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_acos, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_acos_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_acosh, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_acosh_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_add, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_add_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_addbmm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_addbmm_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_addcdiv, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_addcdiv_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_addcmul, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_addcmul_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_addmm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_addmm_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_addmv, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_addmv_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_addr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_addr_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_adjoint, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_align_as, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_align_to, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_all, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_allclose, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_amax, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_amin, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_aminmax, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_angle, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_any, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_apply_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_arccos, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_arccos_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_arccosh, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_arccosh_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_arcsin, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_arcsin_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_arcsinh, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_arcsinh_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_arctan, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_arctan2, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_arctan2_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_arctan_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_arctanh, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_arctanh_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_argmax, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_argmin, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_argsort, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_argwhere, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_as_strided, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_as_strided_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_as_strided_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_asin, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_asin_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_asinh, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_asinh_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_atan, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_atan2, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_atan2_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_atan_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_atanh, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_atanh_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_backward, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_baddbmm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_baddbmm_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bernoulli, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bernoulli_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bfloat16, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bincount, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bitwise_and, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bitwise_and_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bitwise_left_shift, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bitwise_left_shift_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bitwise_not, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bitwise_not_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bitwise_or, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bitwise_or_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bitwise_right_shift, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bitwise_right_shift_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bitwise_xor, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bitwise_xor_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bmm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bool, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_broadcast_to, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_byte, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cauchy_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ccol_indices, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cdouble, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ceil, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ceil_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cfloat, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_chalf, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_char, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cholesky, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cholesky_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cholesky_solve, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_chunk, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_clamp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_clamp_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_clamp_max, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_clamp_max_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_clamp_min, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_clamp_min_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_clip, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_clip_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_clone, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_coalesce, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_col_indices, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_conj, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_conj_physical, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_conj_physical_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_contiguous, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_copy_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_copysign, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_copysign_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_corrcoef, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cos, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cos_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cosh, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cosh_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_count_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cov, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cpu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cross, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_crow_indices, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cuda, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cummax, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cummin, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cumprod, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cumprod_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cumsum, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cumsum_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_data_ptr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_deg2rad, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_deg2rad_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_dense_dim, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_dequantize, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_det, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_detach, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_detach_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_diag, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_diag_embed, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_diagflat, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_diagonal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_diagonal_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_diff, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_digamma, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_digamma_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_dim, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_dim_order, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_dist, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_div, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_div_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_divide, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_divide_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_dot, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_double, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_dsplit, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_element_size, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_eq, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_eq_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_equal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_erf, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_erf_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_erfc, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_erfc_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_erfinv, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_erfinv_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_exp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_exp2, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_exp2_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_exp_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_expand, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_expand_as, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_expm1, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_expm1_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_exponential_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fill_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fill_diagonal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fix, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fix_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_flatten, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_flip, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fliplr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_flipud, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_float, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_float_power, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_float_power_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_floor, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_floor_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_floor_divide, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_floor_divide_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fmax, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fmin, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fmod, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fmod_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_frac, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_frac_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_frexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_gather, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_gcd, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_gcd_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ge, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ge_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_geometric_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_geqrf, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ger, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_get_device, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_greater, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_greater_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_greater_equal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_greater_equal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_gt, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_gt_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_half, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_hardshrink, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_has_names, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_heaviside, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_heaviside_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_histc, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_histogram, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_hsplit, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_hypot, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_hypot_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_i0, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_i0_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_igamma, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_igamma_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_igammac, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_igammac_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_add, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_add_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_copy, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_copy_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_fill, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_fill_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_put, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_put_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_reduce_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_select, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_indices, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_inner, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_int, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_int_repr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ipu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_coalesced, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_complex, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_conj, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_contiguous, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_distributed, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_floating_point, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_inference, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_neg, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_pinned, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_same_size, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_set_to, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_shared, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_signed, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isclose, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isfinite, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isinf, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isnan, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isneginf, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isposinf, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isreal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_istft, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_item, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_kron, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_kthvalue, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lcm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lcm_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ldexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ldexp_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_le, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_le_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lerp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lerp_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_less, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_less_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_less_equal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_less_equal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lgamma, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lgamma_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log10, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log10_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log1p, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log1p_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log2, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log2_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log_normal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logaddexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logaddexp2, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logcumsumexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logdet, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_and, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_and_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_not, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_not_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_or, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_or_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_xor, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_xor_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logit, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logit_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logsumexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_long, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lt, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lt_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lu_solve, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_map2_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_map_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_fill, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_fill_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_scatter_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_select, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_matmul, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_matrix_exp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_matrix_power, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_max, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_maximum, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mean, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_median, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_min, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_minimum, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mode, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_module_load, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_moveaxis, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_movedim, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_msort, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mtia, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mul, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mul_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_multinomial, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_multiply, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_multiply_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mv, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mvlgamma, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mvlgamma_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nan_to_num, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nan_to_num_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nanmean, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nanmedian, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nanquantile, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nansum, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_narrow, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_narrow_copy, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ndimension, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ne, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ne_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_neg, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_neg_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_negative, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_negative_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nelement, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nextafter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nextafter_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nonzero_static, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_norm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_normal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_not_equal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_not_equal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_numel, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_numpy, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_orgqr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ormqr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_outer, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_permute, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_pin_memory, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_pinverse, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_polygamma, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_polygamma_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_positive, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_pow, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_pow_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_prelu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_prod, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_put, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_put_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_per_channel_axis, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_per_channel_scales, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_per_channel_zero_points, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_scale, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_zero_point, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_qr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_qscheme, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_quantile, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rad2deg, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rad2deg_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_random_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ravel, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_reciprocal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_reciprocal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_record_stream, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_refine_names, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_register_hook, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_register_post_accumulate_grad_hook, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_relu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_relu_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_remainder, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_remainder_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rename, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rename_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_renorm, 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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_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 2025-07-17T10:20:15.5344482Z 2025-07-17T10:20:15.5344631Z Running test_python_dispatch 1/1 ... [2025-07-17 10:20:15.482077] 2025-07-17T10:20:15.5344913Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:20:15.5345976Z Executing ['/opt/conda/envs/py_3.12/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 10:20:15.482366] 2025-07-17T10:20:21.1082094Z 2025-07-17T10:20:21.1083112Z test_python_dispatch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_python_dispatch_1.1_9a4fa44790d634de_.log 2025-07-17T10:20:21.1108479Z 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::TestWrapperSubclassAliasingCUDA::test_wrapper_subclass_aliasing_cat_cuda_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCUDA::test_wrapper_subclass_aliasing_conv2d_cuda, test/test_python_dispatch.py::TestWrapperSubclassAliasingCUDA::test_wrapper_subclass_aliasing_custom_NumpyCatCustomOp_cuda_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCUDA::test_wrapper_subclass_aliasing_custom_NumpyCubeCustomOp_cuda_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCUDA::test_wrapper_subclass_aliasing_custom_NumpyMulCustomOp_cuda_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCUDA::test_wrapper_subclass_aliasing_custom_NumpyMulScalarCustomOp_cuda_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCUDA::test_wrapper_subclass_aliasing_custom_NumpyNMSCustomOp_cuda_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCUDA::test_wrapper_subclass_aliasing_custom_NumpyNonzeroCustomOp_cuda_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCUDA::test_wrapper_subclass_aliasing_custom_NumpySortCustomOp_cuda_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCUDA::test_wrapper_subclass_aliasing_custom_NumpySplitCopyCustomOp_cuda_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCUDA::test_wrapper_subclass_aliasing_custom_NumpySplitCopyWithIntCustomOp_cuda_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCUDA::test_wrapper_subclass_aliasing_custom_NumpyTakeCustomOp_cuda_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCUDA::test_wrapper_subclass_aliasing_custom_NumpyViewCopyCustomOp_cuda_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCUDA::test_wrapper_subclass_aliasing_fft_fft2_cuda, test/test_python_dispatch.py::TestWrapperSubclassAliasingCUDA::test_wrapper_subclass_aliasing_mul_cuda_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCUDA::test_wrapper_subclass_aliasing_native_batch_norm_cuda_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCUDA::test_wrapper_subclass_aliasing_out_op_cuda, test/test_python_dispatch.py::TestWrapperSubclassAliasingCUDA::test_wrapper_subclass_aliasing_split_cuda_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCUDA::test_wrapper_subclass_aliasing_split_list_args_cuda_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCUDA::test_wrapper_subclass_aliasing_view_cuda_float32 2025-07-17T10:20:21.1132871Z 2025-07-17T10:20:21.1133003Z Running test_reductions 1/1 ... [2025-07-17 10:20:21.108229] 2025-07-17T10:20:21.1133269Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:20:21.1133906Z Executing ['/opt/conda/envs/py_3.12/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 10:20:21.108521] 2025-07-17T10:22:08.8535244Z 2025-07-17T10:22:08.8536171Z test_reductions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_reductions_1.1_c46a582c2653c56b_.log 2025-07-17T10:22:08.9539955Z Running 4647 items in this shard: test/test_reductions.py::TestReductionsCUDA::test_accreal_type_cuda, test/test_reductions.py::TestReductionsCUDA::test_all_any_cuda, test/test_reductions.py::TestReductionsCUDA::test_all_any_empty_cuda, test/test_reductions.py::TestReductionsCUDA::test_all_any_vs_numpy_cuda_bool, test/test_reductions.py::TestReductionsCUDA::test_all_any_vs_numpy_cuda_complex128, test/test_reductions.py::TestReductionsCUDA::test_all_any_vs_numpy_cuda_complex64, test/test_reductions.py::TestReductionsCUDA::test_all_any_vs_numpy_cuda_float16, test/test_reductions.py::TestReductionsCUDA::test_all_any_vs_numpy_cuda_float32, test/test_reductions.py::TestReductionsCUDA::test_all_any_vs_numpy_cuda_float64, test/test_reductions.py::TestReductionsCUDA::test_all_any_vs_numpy_cuda_int16, test/test_reductions.py::TestReductionsCUDA::test_all_any_vs_numpy_cuda_int32, test/test_reductions.py::TestReductionsCUDA::test_all_any_vs_numpy_cuda_int64, test/test_reductions.py::TestReductionsCUDA::test_all_any_vs_numpy_cuda_int8, test/test_reductions.py::TestReductionsCUDA::test_all_any_vs_numpy_cuda_uint8, test/test_reductions.py::TestReductionsCUDA::test_all_any_with_dim_cuda, test/test_reductions.py::TestReductionsCUDA::test_all_issue117215_cuda, test/test_reductions.py::TestReductionsCUDA::test_amax_cuda_bool, test/test_reductions.py::TestReductionsCUDA::test_amax_cuda_float16, test/test_reductions.py::TestReductionsCUDA::test_amax_cuda_float32, test/test_reductions.py::TestReductionsCUDA::test_amax_cuda_int32, test/test_reductions.py::TestReductionsCUDA::test_amax_cuda_int64, test/test_reductions.py::TestReductionsCUDA::test_amin_amax_some_dims_cuda, test/test_reductions.py::TestReductionsCUDA::test_amin_cuda_bool, test/test_reductions.py::TestReductionsCUDA::test_amin_cuda_float16, test/test_reductions.py::TestReductionsCUDA::test_amin_cuda_float32, test/test_reductions.py::TestReductionsCUDA::test_amin_cuda_int32, test/test_reductions.py::TestReductionsCUDA::test_amin_cuda_int64, test/test_reductions.py::TestReductionsCUDA::test_aminmax_cuda_bfloat16, test/test_reductions.py::TestReductionsCUDA::test_aminmax_cuda_float16, test/test_reductions.py::TestReductionsCUDA::test_aminmax_cuda_float32, test/test_reductions.py::TestReductionsCUDA::test_argminmax_axis_with_dim_one_cuda, test/test_reductions.py::TestReductionsCUDA::test_argminmax_large_axis_cuda, test/test_reductions.py::TestReductionsCUDA::test_argminmax_multiple_cuda_float16, test/test_reductions.py::TestReductionsCUDA::test_argminmax_multiple_cuda_float32, test/test_reductions.py::TestReductionsCUDA::test_argminmax_multiple_cuda_float64, 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test/test_reductions.py::TestReductionsCUDA::test_result_dtype_mean_cuda_bfloat16, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_mean_cuda_complex128, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_mean_cuda_complex64, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_mean_cuda_float16, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_mean_cuda_float32, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_mean_cuda_float64, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_nanmean_cuda_bfloat16, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_nanmean_cuda_complex128, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_nanmean_cuda_complex32, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_nanmean_cuda_complex64, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_nanmean_cuda_float16, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_nanmean_cuda_float32, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_nanmean_cuda_float64, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_nansum_cuda_bfloat16, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_nansum_cuda_bool, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_nansum_cuda_complex128, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_nansum_cuda_complex32, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_nansum_cuda_complex64, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_nansum_cuda_float16, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_nansum_cuda_float32, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_nansum_cuda_float64, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_nansum_cuda_int16, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_nansum_cuda_int32, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_nansum_cuda_int64, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_nansum_cuda_int8, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_nansum_cuda_uint8, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_prod_cuda_bfloat16, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_prod_cuda_bool, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_prod_cuda_complex128, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_prod_cuda_complex32, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_prod_cuda_complex64, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_prod_cuda_float16, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_prod_cuda_float32, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_prod_cuda_float64, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_prod_cuda_int16, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_prod_cuda_int32, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_prod_cuda_int64, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_prod_cuda_int8, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_prod_cuda_uint8, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_std_cuda_bfloat16, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_std_cuda_complex128, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_std_cuda_complex64, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_std_cuda_float16, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_std_cuda_float32, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_std_cuda_float64, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_std_unbiased_cuda_bfloat16, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_std_unbiased_cuda_complex128, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_std_unbiased_cuda_complex64, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_std_unbiased_cuda_float16, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_std_unbiased_cuda_float32, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_std_unbiased_cuda_float64, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_sum_cuda_bfloat16, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_sum_cuda_bool, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_sum_cuda_complex128, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_sum_cuda_complex32, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_sum_cuda_complex64, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_sum_cuda_float16, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_sum_cuda_float32, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_sum_cuda_float64, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_sum_cuda_int16, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_sum_cuda_int32, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_sum_cuda_int64, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_sum_cuda_int8, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_sum_cuda_uint8, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_var_cuda_bfloat16, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_var_cuda_complex128, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_var_cuda_complex64, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_var_cuda_float16, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_var_cuda_float32, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_var_cuda_float64, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_var_unbiased_cuda_bfloat16, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_var_unbiased_cuda_complex128, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_var_unbiased_cuda_complex64, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_var_unbiased_cuda_float16, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_var_unbiased_cuda_float32, test/test_reductions.py::TestReductionsCUDA::test_result_dtype_var_unbiased_cuda_float64, test/test_reductions.py::TestReductionsCUDA::test_std_correction_vs_numpy_cuda_complex128, test/test_reductions.py::TestReductionsCUDA::test_std_correction_vs_numpy_cuda_complex64, test/test_reductions.py::TestReductionsCUDA::test_std_correction_vs_numpy_cuda_float32, test/test_reductions.py::TestReductionsCUDA::test_std_correction_vs_numpy_cuda_float64, test/test_reductions.py::TestReductionsCUDA::test_std_dim_cuda, test/test_reductions.py::TestReductionsCUDA::test_std_mean_all_dims_cuda, test/test_reductions.py::TestReductionsCUDA::test_std_mean_correction_cuda_complex128, test/test_reductions.py::TestReductionsCUDA::test_std_mean_correction_cuda_complex64, test/test_reductions.py::TestReductionsCUDA::test_std_mean_correction_cuda_float32, test/test_reductions.py::TestReductionsCUDA::test_std_mean_correction_cuda_float64, test/test_reductions.py::TestReductionsCUDA::test_std_mean_cuda, test/test_reductions.py::TestReductionsCUDA::test_std_mean_some_dims_cuda, test/test_reductions.py::TestReductionsCUDA::test_std_vs_numpy_cuda_complex128, test/test_reductions.py::TestReductionsCUDA::test_std_vs_numpy_cuda_complex64, test/test_reductions.py::TestReductionsCUDA::test_std_vs_numpy_cuda_float32, test/test_reductions.py::TestReductionsCUDA::test_std_vs_numpy_cuda_float64, test/test_reductions.py::TestReductionsCUDA::test_sum_all_cuda_bool, test/test_reductions.py::TestReductionsCUDA::test_sum_all_cuda_float64, test/test_reductions.py::TestReductionsCUDA::test_sum_cpu_device_mismatch_cuda, test/test_reductions.py::TestReductionsCUDA::test_sum_dim_cuda, test/test_reductions.py::TestReductionsCUDA::test_sum_dim_reduction_uint8_overflow_cuda, test/test_reductions.py::TestReductionsCUDA::test_sum_integer_upcast_cuda, test/test_reductions.py::TestReductionsCUDA::test_sum_noncontig_cuda_float64, test/test_reductions.py::TestReductionsCUDA::test_sum_noncontig_lowp_cuda_bfloat16, test/test_reductions.py::TestReductionsCUDA::test_sum_noncontig_lowp_cuda_float16, test/test_reductions.py::TestReductionsCUDA::test_sum_out_cuda_float64, test/test_reductions.py::TestReductionsCUDA::test_sum_parallel_cuda, test/test_reductions.py::TestReductionsCUDA::test_sum_vs_numpy_cuda_float16, test/test_reductions.py::TestReductionsCUDA::test_sum_vs_numpy_cuda_float32, test/test_reductions.py::TestReductionsCUDA::test_sum_vs_numpy_cuda_float64, test/test_reductions.py::TestReductionsCUDA::test_sum_vs_numpy_cuda_int16, test/test_reductions.py::TestReductionsCUDA::test_sum_vs_numpy_cuda_int32, test/test_reductions.py::TestReductionsCUDA::test_sum_vs_numpy_cuda_int64, test/test_reductions.py::TestReductionsCUDA::test_sum_vs_numpy_cuda_int8, test/test_reductions.py::TestReductionsCUDA::test_tensor_compare_ops_argmax_argmix_kthvalue_dim_empty_cuda, test/test_reductions.py::TestReductionsCUDA::test_tensor_compare_ops_empty_cuda, test/test_reductions.py::TestReductionsCUDA::test_tensor_reduce_ops_empty_cuda, test/test_reductions.py::TestReductionsCUDA::test_var_correction_vs_numpy_cuda_complex128, test/test_reductions.py::TestReductionsCUDA::test_var_correction_vs_numpy_cuda_complex64, test/test_reductions.py::TestReductionsCUDA::test_var_correction_vs_numpy_cuda_float32, test/test_reductions.py::TestReductionsCUDA::test_var_correction_vs_numpy_cuda_float64, test/test_reductions.py::TestReductionsCUDA::test_var_cuda, test/test_reductions.py::TestReductionsCUDA::test_var_dim_cuda, test/test_reductions.py::TestReductionsCUDA::test_var_large_input_cuda, test/test_reductions.py::TestReductionsCUDA::test_var_mean_all_dims_cuda, test/test_reductions.py::TestReductionsCUDA::test_var_mean_correction_cuda_complex128, test/test_reductions.py::TestReductionsCUDA::test_var_mean_correction_cuda_complex64, test/test_reductions.py::TestReductionsCUDA::test_var_mean_correction_cuda_float32, test/test_reductions.py::TestReductionsCUDA::test_var_mean_correction_cuda_float64, test/test_reductions.py::TestReductionsCUDA::test_var_mean_cuda, test/test_reductions.py::TestReductionsCUDA::test_var_mean_some_dims_cuda, test/test_reductions.py::TestReductionsCUDA::test_var_stability2_cuda, test/test_reductions.py::TestReductionsCUDA::test_var_stability_cuda, test/test_reductions.py::TestReductionsCUDA::test_var_unbiased_cuda, test/test_reductions.py::TestReductionsCUDA::test_var_vs_numpy_cuda_complex128, test/test_reductions.py::TestReductionsCUDA::test_var_vs_numpy_cuda_complex64, test/test_reductions.py::TestReductionsCUDA::test_var_vs_numpy_cuda_float32, test/test_reductions.py::TestReductionsCUDA::test_var_vs_numpy_cuda_float64, test/test_reductions.py::TestReductionsCUDA::test_warn_invalid_degrees_of_freedom_cuda_complex128, test/test_reductions.py::TestReductionsCUDA::test_warn_invalid_degrees_of_freedom_cuda_complex64, test/test_reductions.py::TestReductionsCUDA::test_warn_invalid_degrees_of_freedom_cuda_float32, test/test_reductions.py::TestReductionsCUDA::test_warn_invalid_degrees_of_freedom_cuda_float64 2025-07-17T10:22:09.0478088Z 2025-07-17T10:22:09.0478286Z Running test_show_pickle 1/1 ... [2025-07-17 10:22:08.858386] 2025-07-17T10:22:09.0478598Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:22:09.0479229Z Executing ['/opt/conda/envs/py_3.12/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 10:22:08.858693] 2025-07-17T10:22:12.4805127Z 2025-07-17T10:22:12.4806654Z test_show_pickle 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_show_pickle_1.1_d50773f9bdf21556_.log 2025-07-17T10:22:12.4807315Z Running 1 items in this shard: test/test_show_pickle.py::TestShowPickle::test_scripted_model 2025-07-17T10:22:12.4807574Z 2025-07-17T10:22:12.4807702Z Running test_sort_and_select 1/1 ... [2025-07-17 10:22:12.480466] 2025-07-17T10:22:12.4807983Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:22:12.4808734Z Executing ['/opt/conda/envs/py_3.12/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 10:22:12.480738] 2025-07-17T10:22:20.1611072Z 2025-07-17T10:22:20.1612087Z 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_91e97e0e8a25aefd_.log 2025-07-17T10:22:20.1633875Z Running 111 items in this shard: test/test_sort_and_select.py::TestSortAndSelectCUDA::test_complex_unsupported_cpu_cuda, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_isin_cuda_float16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_isin_cuda_float32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_isin_cuda_float64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_isin_cuda_int16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_isin_cuda_int32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_isin_cuda_int64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_isin_cuda_int8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_isin_cuda_uint8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_isin_different_devices_cuda_float32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_isin_different_devices_cuda_float64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_isin_different_devices_cuda_int16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_isin_different_devices_cuda_int32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_isin_different_devices_cuda_int64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_isin_different_devices_cuda_int8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_isin_different_devices_cuda_uint8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_isin_different_dtypes_cuda, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_kthvalue_cuda_float64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_kthvalue_scalar_cuda_float32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_msort_cuda_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_msort_cuda_float16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_msort_cuda_float32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_msort_cuda_float64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_msort_cuda_int16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_msort_cuda_int32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_msort_cuda_int64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_msort_cuda_int8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_msort_cuda_uint8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_1d_output_discontiguous_cuda_float32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_1d_parallel_cuda_int16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_1d_parallel_cuda_int32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_1d_parallel_cuda_int64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_1d_parallel_cuda_int8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_1d_parallel_cuda_uint8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_cuda, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_discontiguous_cuda_float32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_discontiguous_slow_cuda_float32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_expanded_tensor_cuda_float32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_large_cuda_uint8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_large_slice_cuda, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_overflow_cuda_int16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_overflow_cuda_int32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_overflow_cuda_int64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_overflow_cuda_int8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_overflow_cuda_uint8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_restride_cuda_float32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_sort_stable_none_cuda, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_against_numpy_cuda_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_against_numpy_cuda_bool, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_against_numpy_cuda_float16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_against_numpy_cuda_float32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_against_numpy_cuda_float64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_against_numpy_cuda_int16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_against_numpy_cuda_int32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_against_numpy_cuda_int64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_against_numpy_cuda_int8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_against_numpy_cuda_uint8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_cuda_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_cuda_bool, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_cuda_float16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_cuda_float32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_cuda_float64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_cuda_int16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_cuda_int32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_cuda_int64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_cuda_int8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_stable_sort_cuda_uint8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_1d_output_discontiguous_cuda_float32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_4d_cuda, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_arguments_cuda, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_cuda, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_integral_cuda_int16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_integral_cuda_int32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_integral_cuda_int64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_integral_cuda_int8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_integral_cuda_uint8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_lower_precision_cuda_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_lower_precision_cuda_float16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_noncontiguous_gpu_cuda, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_nonfinite_cuda_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_nonfinite_cuda_float16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_nonfinite_cuda_float32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_nonfinite_cuda_float64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_quantized_scalar_input_cuda, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_zero_cuda_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_zero_cuda_float32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_zero_cuda_float64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_zero_cuda_int16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_zero_cuda_int32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_zero_cuda_int64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_zero_cuda_int8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_topk_zero_cuda_uint8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_consecutive_cuda_bool, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_consecutive_cuda_float16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_consecutive_cuda_float32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_consecutive_cuda_float64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_consecutive_cuda_int16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_consecutive_cuda_int32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_consecutive_cuda_int64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_consecutive_cuda_int8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_consecutive_cuda_uint8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_cuda_bool, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_cuda_float16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_cuda_float32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_cuda_float64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_cuda_int16, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_cuda_int32, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_cuda_int64, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_cuda_int8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_cuda_uint8, test/test_sort_and_select.py::TestSortAndSelectCUDA::test_unique_dim_cuda 2025-07-17T10:22:20.1654943Z 2025-07-17T10:22:20.1655102Z Running test_tensor_creation_ops 1/1 ... [2025-07-17 10:22:20.161433] 2025-07-17T10:22:20.1655386Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:22:20.1656097Z Executing ['/opt/conda/envs/py_3.12/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 10:22:20.161792] 2025-07-17T10:24:23.9254233Z 2025-07-17T10:24:23.9255194Z 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_9b5e44fd6fdbd650_.log 2025-07-17T10:24:23.9378801Z Running 526 items in this shard: test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_arange_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_arange_device_vs_cpu_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_arange_device_vs_cpu_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_arange_device_vs_cpu_cuda_int32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_arange_device_vs_cpu_cuda_int64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_arange_inference_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_arange_lowp_cuda_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_arange_lowp_cuda_float16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_as_strided_neg_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_as_tensor_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_block_diag_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_block_diag_scipy_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_cartesian_prod_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_cat2_cuda_float16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_cat2_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_cat2_cuda_int32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_cat_all_dtypes_and_devices_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_cat_big_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_cat_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_cat_empty_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_cat_empty_legacy_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_cat_in_channels_last_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_cat_mem_overlap_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_cat_out_channels_last_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_cat_out_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_cat_out_fast_path_dim0_dim1_cuda_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_cat_out_fast_path_dim0_dim1_cuda_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_cat_out_fast_path_dim0_dim1_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_cat_out_fast_path_dim0_dim1_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_cat_out_fast_path_dim0_dim1_cuda_int16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_cat_out_fast_path_dim0_dim1_cuda_int32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_cat_out_fast_path_dim0_dim1_cuda_int64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_cat_out_fast_path_dim0_dim1_cuda_int8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_cat_out_fast_path_dim0_dim1_cuda_uint16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_cat_out_fast_path_dim0_dim1_cuda_uint32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_cat_out_fast_path_dim0_dim1_cuda_uint64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_cat_out_fast_path_dim0_dim1_cuda_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_cat_out_memory_format_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_cat_preserve_channels_last_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_cat_stack_cross_devices_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_combinations_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_complex_type_conversions_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_concat_empty_list_error_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_constructor_device_legacy_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_constructor_dtypes_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_ctor_with_numpy_array_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_device_rounding_cuda_float16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_device_rounding_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_device_rounding_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_diag_embed_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_diagflat_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_dsplit_cuda_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_dsplit_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_dsplit_cuda_int64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_dstack_cuda_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_dstack_cuda_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_dstack_cuda_float16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_dstack_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_dstack_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_dstack_cuda_int16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_dstack_cuda_int32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_dstack_cuda_int64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_dstack_cuda_int8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_dstack_cuda_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_empty_full_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_empty_overflow_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_empty_strided_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_empty_tensor_props_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_eye_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_fill_all_dtypes_and_devices_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_float_to_int_conversion_finite_cuda_bool, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_float_to_int_conversion_finite_cuda_int16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_float_to_int_conversion_finite_cuda_int32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_float_to_int_conversion_finite_cuda_int64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_float_to_int_conversion_finite_cuda_int8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_float_to_int_conversion_finite_cuda_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_float_to_int_conversion_nonfinite_cuda_bool, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_float_to_int_conversion_nonfinite_cuda_int16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_float_to_int_conversion_nonfinite_cuda_int32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_float_to_int_conversion_nonfinite_cuda_int64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_float_to_int_conversion_nonfinite_cuda_int8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_float_to_int_conversion_nonfinite_cuda_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_from_file_shared_False_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_from_file_shared_True_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_full_inference_cuda_float16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_full_inference_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_full_inference_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_full_out_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_hsplit_cuda_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_hsplit_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_hsplit_cuda_int64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_hstack_column_stack_cuda_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_hstack_column_stack_cuda_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_hstack_column_stack_cuda_float16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_hstack_column_stack_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_hstack_column_stack_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_hstack_column_stack_cuda_int16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_hstack_column_stack_cuda_int32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_hstack_column_stack_cuda_int64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_hstack_column_stack_cuda_int8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_hstack_column_stack_cuda_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_kaiser_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_kaiser_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_kaiser_window_cuda_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_kaiser_window_cuda_float16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_kaiser_window_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_kaiser_window_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_kaiser_window_cuda_int64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_large_linspace_cuda_int32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_large_linspace_cuda_int64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_like_fn_stride_proparation_vs_tensoriterator_unary_op_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linlogspace_mem_overlap_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linspace_cuda_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linspace_cuda_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linspace_cuda_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linspace_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linspace_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linspace_cuda_int16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linspace_cuda_int32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linspace_cuda_int64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linspace_cuda_int8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linspace_cuda_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linspace_deduction_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linspace_device_vs_cpu_cuda_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linspace_device_vs_cpu_cuda_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linspace_device_vs_cpu_cuda_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linspace_device_vs_cpu_cuda_float16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linspace_device_vs_cpu_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linspace_device_vs_cpu_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linspace_special_steps_cuda_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linspace_special_steps_cuda_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linspace_special_steps_cuda_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linspace_special_steps_cuda_float16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linspace_special_steps_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linspace_special_steps_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linspace_vs_numpy_complex_cuda_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linspace_vs_numpy_cuda_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linspace_vs_numpy_cuda_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linspace_vs_numpy_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linspace_vs_numpy_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linspace_vs_numpy_integral_cuda_int16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linspace_vs_numpy_integral_cuda_int32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linspace_vs_numpy_integral_cuda_int64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linspace_vs_numpy_integral_cuda_int8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_linspace_vs_numpy_integral_cuda_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_logspace_base2_cuda_float16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_logspace_base2_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_logspace_base2_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_logspace_cuda_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_logspace_cuda_float16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_logspace_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_logspace_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_logspace_cuda_int16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_logspace_cuda_int32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_logspace_cuda_int64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_logspace_cuda_int8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_logspace_cuda_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_logspace_deduction_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_logspace_device_vs_cpu_cuda_float16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_logspace_device_vs_cpu_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_logspace_device_vs_cpu_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_logspace_special_steps_cuda_float16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_logspace_special_steps_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_logspace_special_steps_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_logspace_vs_numpy_complex_cuda_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_logspace_vs_numpy_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_logspace_vs_numpy_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_meshgrid_default_indexing_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_meshgrid_empty_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_meshgrid_ij_indexing_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_meshgrid_ij_indexing_is_default_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_meshgrid_inconsistent_device_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_meshgrid_inconsistent_dtype_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_meshgrid_non_1d_tensor_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_meshgrid_unsupported_indexing_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_meshgrid_vs_numpy_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_meshgrid_warns_if_no_indexing_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_meshgrid_xy_indexing_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_new_empty_strided_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_new_methods_requires_grad_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_new_tensor_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_new_tensor_device_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_offset_scalar_cast_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_ones_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_bool_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_cuda_int16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_cuda_int32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_cuda_int64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_cuda_int8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_default_cuda_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_default_cuda_float16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_default_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_default_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_default_cuda_int16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_default_cuda_int32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_default_cuda_int64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_default_cuda_int8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_default_cuda_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_from_to_bool_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_from_to_cuda_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_from_to_cuda_float16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_from_to_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_from_to_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_from_to_cuda_int16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_from_to_cuda_int32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_from_to_cuda_int64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_from_to_cuda_int8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_from_to_cuda_uint16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_from_to_cuda_uint32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_from_to_cuda_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_full_range_cuda_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_full_range_cuda_float16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_full_range_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_full_range_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_full_range_cuda_int16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_full_range_cuda_int32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_full_range_cuda_int64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_full_range_cuda_int8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_full_range_cuda_uint16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_full_range_cuda_uint32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_full_range_cuda_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_to_cuda_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_to_cuda_float16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_to_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_to_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_to_cuda_int16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_to_cuda_int32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_to_cuda_int64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_to_cuda_int8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_to_cuda_uint16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_to_cuda_uint32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_random_to_cuda_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_range_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_range_factories_64bit_indexing_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_range_warning_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_refs_tensor_cuda_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_refs_tensor_cuda_bool, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_refs_tensor_cuda_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_refs_tensor_cuda_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_refs_tensor_cuda_float16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_refs_tensor_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_refs_tensor_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_refs_tensor_cuda_int16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_refs_tensor_cuda_int32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_refs_tensor_cuda_int64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_refs_tensor_cuda_int8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_refs_tensor_cuda_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_repeat_interleave_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_roll_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_signal_window_functions_window_bartlett_cuda_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_signal_window_functions_window_bartlett_cuda_float16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_signal_window_functions_window_bartlett_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_signal_window_functions_window_bartlett_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_signal_window_functions_window_bartlett_cuda_int64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_signal_window_functions_window_blackman_cuda_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_signal_window_functions_window_blackman_cuda_float16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_signal_window_functions_window_blackman_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_signal_window_functions_window_blackman_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_signal_window_functions_window_blackman_cuda_int64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_signal_window_functions_window_hamming_cuda_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_signal_window_functions_window_hamming_cuda_float16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_signal_window_functions_window_hamming_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_signal_window_functions_window_hamming_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_signal_window_functions_window_hamming_cuda_int64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_signal_window_functions_window_hann_cuda_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_signal_window_functions_window_hann_cuda_float16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_signal_window_functions_window_hann_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_signal_window_functions_window_hann_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_signal_window_functions_window_hann_cuda_int64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_signal_windows_functions_window_bartlett_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_signal_windows_functions_window_bartlett_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_signal_windows_functions_window_blackman_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_signal_windows_functions_window_blackman_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_signal_windows_functions_window_cosine_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_signal_windows_functions_window_cosine_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_signal_windows_functions_window_hamming_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_signal_windows_functions_window_hamming_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_signal_windows_functions_window_hann_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_signal_windows_functions_window_hann_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_signal_windows_functions_window_nuttall_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_signal_windows_functions_window_nuttall_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_simple_scalar_cast_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_stack_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_stack_out_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_storage_filename_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_strided_mismatched_stride_shape_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_tensor_ctor_device_inference_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_tensor_device_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_tensor_factories_empty_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_tensor_factory_copy_var_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_tensor_factory_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_tensor_factory_gpu_type_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_tensor_factory_gpu_type_inference_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_tensor_factory_type_inference_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_tensor_from_non_writable_numpy_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_tensor_from_sequence_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_torch_complex_cuda_float16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_torch_complex_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_torch_complex_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_torch_complex_floating_dtype_error_cuda_bool, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_torch_complex_floating_dtype_error_cuda_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_torch_complex_floating_dtype_error_cuda_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_torch_complex_floating_dtype_error_cuda_int16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_torch_complex_floating_dtype_error_cuda_int32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_torch_complex_floating_dtype_error_cuda_int64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_torch_complex_floating_dtype_error_cuda_int8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_torch_complex_floating_dtype_error_cuda_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_torch_complex_out_dtype_error_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_torch_complex_out_dtype_error_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_torch_complex_same_dtype_error_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_torch_complex_same_dtype_error_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_torch_polar_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_torch_polar_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_unpack_double_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_unpack_double_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_vander_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_vander_types_cuda_bool, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_vander_types_cuda_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_vander_types_cuda_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_vander_types_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_vander_types_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_vander_types_cuda_int16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_vander_types_cuda_int32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_vander_types_cuda_int64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_vander_types_cuda_int8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_vander_types_cuda_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_vsplit_cuda_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_vsplit_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_vsplit_cuda_int64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_vstack_row_stack_cuda_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_vstack_row_stack_cuda_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_vstack_row_stack_cuda_float16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_vstack_row_stack_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_vstack_row_stack_cuda_float64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_vstack_row_stack_cuda_int16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_vstack_row_stack_cuda_int32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_vstack_row_stack_cuda_int64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_vstack_row_stack_cuda_int8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_vstack_row_stack_cuda_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_zeros_cuda, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_zeros_dtype_layout_device_match_cuda_bool, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_zeros_dtype_layout_device_match_cuda_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_zeros_dtype_layout_device_match_cuda_float16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_zeros_dtype_layout_device_match_cuda_float32, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_zeros_dtype_layout_device_match_cuda_int16, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_zeros_dtype_layout_device_match_cuda_int64, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_zeros_dtype_layout_device_match_cuda_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCUDA::test_zeros_out_cuda, test/test_tensor_creation_ops.py::TestRandomTensorCreationCUDA::test_normal_cuda_float32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCUDA::test_normal_cuda_float64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCUDA::test_normal_std_error_cuda, test/test_tensor_creation_ops.py::TestRandomTensorCreationCUDA::test_rand_cuda_complex128, test/test_tensor_creation_ops.py::TestRandomTensorCreationCUDA::test_rand_cuda_complex32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCUDA::test_rand_cuda_complex64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCUDA::test_rand_cuda_float32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCUDA::test_rand_cuda_float64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCUDA::test_randint_cuda, test/test_tensor_creation_ops.py::TestRandomTensorCreationCUDA::test_randint_distribution_cuda, test/test_tensor_creation_ops.py::TestRandomTensorCreationCUDA::test_randint_inference_cuda, test/test_tensor_creation_ops.py::TestRandomTensorCreationCUDA::test_randn_cuda_bfloat16, test/test_tensor_creation_ops.py::TestRandomTensorCreationCUDA::test_randn_cuda_complex128, test/test_tensor_creation_ops.py::TestRandomTensorCreationCUDA::test_randn_cuda_complex32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCUDA::test_randn_cuda_complex64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCUDA::test_randn_cuda_float16, test/test_tensor_creation_ops.py::TestRandomTensorCreationCUDA::test_randn_cuda_float32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCUDA::test_randn_cuda_float64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCUDA::test_random_neg_values_cuda, test/test_tensor_creation_ops.py::TestRandomTensorCreationCUDA::test_randperm_cuda, test/test_tensor_creation_ops.py::TestRandomTensorCreationCUDA::test_randperm_device_compatibility_cuda, test/test_tensor_creation_ops.py::TestRandomTensorCreationCUDA::test_randperm_large_cuda, test/test_tensor_creation_ops.py::TestRandomTensorCreationCUDA::test_uniform_from_to_cuda_bfloat16, test/test_tensor_creation_ops.py::TestRandomTensorCreationCUDA::test_uniform_from_to_cuda_float16, test/test_tensor_creation_ops.py::TestRandomTensorCreationCUDA::test_uniform_from_to_cuda_float32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCUDA::test_uniform_from_to_cuda_float64, test/test_tensor_creation_ops.py::TestLikeTensorCreationCUDA::test_empty_like_cuda, test/test_tensor_creation_ops.py::TestLikeTensorCreationCUDA::test_full_like_inference_cuda, test/test_tensor_creation_ops.py::TestLikeTensorCreationCUDA::test_ones_like_cuda, test/test_tensor_creation_ops.py::TestLikeTensorCreationCUDA::test_ones_like_multiple_device_cuda, test/test_tensor_creation_ops.py::TestLikeTensorCreationCUDA::test_zeros_like_cuda, test/test_tensor_creation_ops.py::TestLikeTensorCreationCUDA::test_zeros_like_multiple_device_cuda, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_buffer_cuda_bool, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_buffer_cuda_complex128, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_buffer_cuda_complex64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_buffer_cuda_float16, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_buffer_cuda_float32, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_buffer_cuda_float64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_buffer_cuda_int16, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_buffer_cuda_int32, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_buffer_cuda_int64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_buffer_cuda_int8, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_buffer_cuda_uint16, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_buffer_cuda_uint32, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_buffer_cuda_uint64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_buffer_cuda_uint8, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_dlpack_cuda_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_dlpack_cuda_complex128, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_dlpack_cuda_complex64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_dlpack_cuda_float16, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_dlpack_cuda_float32, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_dlpack_cuda_float64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_dlpack_cuda_int16, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_dlpack_cuda_int32, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_dlpack_cuda_int64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_dlpack_cuda_int8, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_dlpack_cuda_uint8, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_numpy_cuda_bool, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_numpy_cuda_complex128, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_numpy_cuda_complex64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_numpy_cuda_float16, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_numpy_cuda_float32, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_numpy_cuda_float64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_numpy_cuda_int16, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_numpy_cuda_int32, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_numpy_cuda_int64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_numpy_cuda_int8, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_numpy_cuda_uint16, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_numpy_cuda_uint32, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_numpy_cuda_uint64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_numpy_cuda_uint8, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_tensor_cuda_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_tensor_cuda_bool, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_tensor_cuda_complex128, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_tensor_cuda_complex64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_tensor_cuda_float16, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_tensor_cuda_float32, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_tensor_cuda_float64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_tensor_cuda_int16, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_tensor_cuda_int32, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_tensor_cuda_int64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_tensor_cuda_int8, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_alias_from_tensor_cuda_uint8, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_astensor_consistency_cuda, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_buffer_cuda_bool, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_buffer_cuda_complex128, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_buffer_cuda_complex64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_buffer_cuda_float16, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_buffer_cuda_float32, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_buffer_cuda_float64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_buffer_cuda_int16, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_buffer_cuda_int32, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_buffer_cuda_int64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_buffer_cuda_int8, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_buffer_cuda_uint16, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_buffer_cuda_uint32, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_buffer_cuda_uint64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_buffer_cuda_uint8, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_dlpack_cuda_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_dlpack_cuda_complex128, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_dlpack_cuda_complex64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_dlpack_cuda_float16, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_dlpack_cuda_float32, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_dlpack_cuda_float64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_dlpack_cuda_int16, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_dlpack_cuda_int32, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_dlpack_cuda_int64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_dlpack_cuda_int8, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_dlpack_cuda_uint8, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_dlpack_mult_devices_cuda_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_dlpack_mult_devices_cuda_complex128, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_dlpack_mult_devices_cuda_complex64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_dlpack_mult_devices_cuda_float16, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_dlpack_mult_devices_cuda_float32, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_dlpack_mult_devices_cuda_float64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_dlpack_mult_devices_cuda_int16, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_dlpack_mult_devices_cuda_int32, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_dlpack_mult_devices_cuda_int64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_dlpack_mult_devices_cuda_int8, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_dlpack_mult_devices_cuda_uint8, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_numpy_cuda_bool, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_numpy_cuda_complex128, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_numpy_cuda_complex64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_numpy_cuda_float16, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_numpy_cuda_float32, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_numpy_cuda_float64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_numpy_cuda_int16, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_numpy_cuda_int32, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_numpy_cuda_int64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_numpy_cuda_int8, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_numpy_cuda_uint16, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_numpy_cuda_uint32, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_numpy_cuda_uint64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_numpy_cuda_uint8, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_tensor_mult_devices_cuda_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_tensor_mult_devices_cuda_complex128, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_tensor_mult_devices_cuda_complex64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_tensor_mult_devices_cuda_float16, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_tensor_mult_devices_cuda_float32, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_tensor_mult_devices_cuda_float64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_tensor_mult_devices_cuda_int16, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_tensor_mult_devices_cuda_int32, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_tensor_mult_devices_cuda_int64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_tensor_mult_devices_cuda_int8, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_from_tensor_mult_devices_cuda_uint8, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_list_cuda_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_list_cuda_bool, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_list_cuda_complex128, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_list_cuda_complex64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_list_cuda_float16, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_list_cuda_float32, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_list_cuda_float64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_list_cuda_int16, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_list_cuda_int32, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_list_cuda_int64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_list_cuda_int8, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_list_cuda_uint8, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_tensor_cuda_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_tensor_cuda_bool, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_tensor_cuda_complex128, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_tensor_cuda_complex64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_tensor_cuda_float16, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_tensor_cuda_float32, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_tensor_cuda_float64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_tensor_cuda_int16, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_tensor_cuda_int32, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_tensor_cuda_int64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_tensor_cuda_int8, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_copy_tensor_cuda_uint8, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_default_device_cuda, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_device_without_index_cuda, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_numpy_scalars_cuda, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_retain_autograd_history_cuda_complex64, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_retain_autograd_history_cuda_float32, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_unsupported_alias_cuda_float32, test/test_tensor_creation_ops.py::TestAsArrayCUDA::test_unsupported_alias_mult_devices_cuda_float32 2025-07-17T10:24:23.9493373Z 2025-07-17T10:24:23.9493510Z Running test_tensorexpr 1/1 ... [2025-07-17 10:24:23.926081] 2025-07-17T10:24:23.9493997Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:24:23.9494607Z Executing ['/opt/conda/envs/py_3.12/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 10:24:23.926376] 2025-07-17T10:25:52.2760910Z 2025-07-17T10:25:52.2761895Z test_tensorexpr 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_tensorexpr_1.1_a31c4df9a53ba156_.log 2025-07-17T10:25:52.2773965Z 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 2025-07-17T10:25:52.2785036Z 2025-07-17T10:25:52.2785150Z Running test_torch 1/1 ... [2025-07-17 10:25:52.276009] 2025-07-17T10:25:52.2785579Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:25:52.2786198Z Executing ['/opt/conda/envs/py_3.12/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 10:25:52.276314] 2025-07-17T10:28:00.9105720Z 2025-07-17T10:28:00.9106656Z test_torch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_torch_1.1_24d9191d17f4da9d_.log 2025-07-17T10:28:00.9294213Z Running 1068 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, 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test/test_torch.py::TestTorchDeviceTypeCUDA::test_lazy_clone_view_materialize_cuda_int64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_lazy_clone_view_materialize_cuda_int8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_lazy_clone_view_materialize_cuda_uint8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_log_normal_cuda_bfloat16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_log_normal_cuda_float16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_log_normal_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_log_normal_cuda_float64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_logcumsumexp_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_lognormal_kstest_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_fill_bool_tensor_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_fill_cuda_bfloat16_bool, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_fill_cuda_bfloat16_uint8, 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test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_fill_cuda_int16_bool, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_fill_cuda_int16_uint8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_fill_cuda_int32_bool, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_fill_cuda_int32_uint8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_fill_cuda_int64_bool, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_fill_cuda_int64_uint8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_fill_cuda_int8_bool, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_fill_cuda_int8_uint8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_fill_cuda_uint8_bool, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_fill_cuda_uint8_uint8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_fill_mem_overlap_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_scatter_bool_tensor_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_scatter_cuda_bfloat16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_scatter_cuda_complex128, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_scatter_cuda_complex64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_scatter_cuda_float16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_scatter_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_scatter_cuda_float64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_scatter_cuda_int16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_scatter_cuda_int32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_scatter_cuda_int64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_scatter_cuda_int8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_scatter_cuda_uint8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_scatter_inplace_noncontiguous_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_scatter_large_tensor_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_scatter_mem_overlap_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_select_cuda_bfloat16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_select_cuda_bool, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_select_cuda_complex128, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_select_cuda_complex64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_select_cuda_float16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_select_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_select_cuda_float64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_select_cuda_int16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_select_cuda_int32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_masked_select_cuda_int64, 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test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_setitem_cuda_quint8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_setitem_cuda_uint8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_storage_use_count_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_strides_propagation_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_sync_warning_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_take_cuda_bfloat16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_take_cuda_bool, test/test_torch.py::TestTorchDeviceTypeCUDA::test_take_cuda_complex128, test/test_torch.py::TestTorchDeviceTypeCUDA::test_take_cuda_complex64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_take_cuda_float16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_take_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_take_cuda_float64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_take_cuda_int16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_take_cuda_int32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_take_cuda_int64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_take_cuda_int8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_take_cuda_uint8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_take_empty_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_tensor_from_storage_cuda_bfloat16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_tensor_from_storage_cuda_bool, test/test_torch.py::TestTorchDeviceTypeCUDA::test_tensor_from_storage_cuda_complex128, test/test_torch.py::TestTorchDeviceTypeCUDA::test_tensor_from_storage_cuda_complex64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_tensor_from_storage_cuda_float16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_tensor_from_storage_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_tensor_from_storage_cuda_float64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_tensor_from_storage_cuda_int16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_tensor_from_storage_cuda_int32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_tensor_from_storage_cuda_int64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_tensor_from_storage_cuda_int8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_tensor_from_storage_cuda_uint16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_tensor_from_storage_cuda_uint32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_tensor_from_storage_cuda_uint64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_tensor_from_storage_cuda_uint8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_tensor_set_errors_multigpu_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_tensor_shape_empty_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_tensor_storage_type_cuda_bfloat16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_tensor_storage_type_cuda_bool, test/test_torch.py::TestTorchDeviceTypeCUDA::test_tensor_storage_type_cuda_complex128, test/test_torch.py::TestTorchDeviceTypeCUDA::test_tensor_storage_type_cuda_complex64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_tensor_storage_type_cuda_float16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_tensor_storage_type_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_tensor_storage_type_cuda_float64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_tensor_storage_type_cuda_int16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_tensor_storage_type_cuda_int32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_tensor_storage_type_cuda_int64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_tensor_storage_type_cuda_int8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_tensor_storage_type_cuda_uint8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_tensor_type_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_ternary_op_mem_overlap_cuda_float64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_typed_storage_meta_cuda_bfloat16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_typed_storage_meta_cuda_bool, test/test_torch.py::TestTorchDeviceTypeCUDA::test_typed_storage_meta_cuda_complex128, test/test_torch.py::TestTorchDeviceTypeCUDA::test_typed_storage_meta_cuda_complex64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_typed_storage_meta_cuda_float16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_typed_storage_meta_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_typed_storage_meta_cuda_float64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_typed_storage_meta_cuda_int16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_typed_storage_meta_cuda_int32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_typed_storage_meta_cuda_int64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_typed_storage_meta_cuda_int8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_typed_storage_meta_cuda_uint8, test/test_torch.py::TestTorchDeviceTypeCUDA::test_uniform_kstest_cuda_bfloat16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_uniform_kstest_cuda_float16, test/test_torch.py::TestTorchDeviceTypeCUDA::test_uniform_kstest_cuda_float32, test/test_torch.py::TestTorchDeviceTypeCUDA::test_uniform_kstest_cuda_float64, test/test_torch.py::TestTorchDeviceTypeCUDA::test_untyped_storage_meta_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_warn_always_caught_cuda, test/test_torch.py::TestTorchDeviceTypeCUDA::test_where_scalar_handcrafted_values_cuda, test/test_torch.py::TestDevicePrecisionCUDA::test_advancedindex_mixed_cpu_devices_cuda, test/test_torch.py::TestDevicePrecisionCUDA::test_advancedindex_mixed_devices_error_cuda, test/test_torch.py::TestDevicePrecisionCUDA::test_clamp_cuda_float32, test/test_torch.py::TestDevicePrecisionCUDA::test_clamp_cuda_float64, test/test_torch.py::TestDevicePrecisionCUDA::test_clamp_cuda_int64, test/test_torch.py::TestDevicePrecisionCUDA::test_copy_broadcast_cuda, test/test_torch.py::TestDevicePrecisionCUDA::test_copy_noncontig_cuda, test/test_torch.py::TestDevicePrecisionCUDA::test_cuda_device_idx_cuda, test/test_torch.py::TestDevicePrecisionCUDA::test_device_serialization_cuda, test/test_torch.py::TestDevicePrecisionCUDA::test_from_sequence_cuda_float16, test/test_torch.py::TestDevicePrecisionCUDA::test_from_sequence_cuda_float32, test/test_torch.py::TestDevicePrecisionCUDA::test_from_sequence_cuda_float64, test/test_torch.py::TestDevicePrecisionCUDA::test_from_sequence_cuda_int16, test/test_torch.py::TestDevicePrecisionCUDA::test_from_sequence_cuda_int32, test/test_torch.py::TestDevicePrecisionCUDA::test_from_sequence_cuda_int64, test/test_torch.py::TestDevicePrecisionCUDA::test_from_sequence_cuda_int8, test/test_torch.py::TestDevicePrecisionCUDA::test_from_sequence_cuda_uint8, test/test_torch.py::TestDevicePrecisionCUDA::test_index_add_bfloat16_cuda, test/test_torch.py::TestDevicePrecisionCUDA::test_multidevice_serialization_cuda, test/test_torch.py::TestDevicePrecisionCUDA::test_type_conversions_same_device_cuda 2025-07-17T10:28:01.1172565Z 2025-07-17T10:28:01.1172785Z Running test_transformers_privateuse1 1/1 ... [2025-07-17 10:28:00.911711] 2025-07-17T10:28:02.6100582Z -- The CXX compiler identification is GNU 11.4.0 2025-07-17T10:28:02.7217810Z -- The C compiler identification is GNU 11.4.0 2025-07-17T10:28:02.7634457Z -- Detecting CXX compiler ABI info 2025-07-17T10:28:03.2324796Z -- Detecting CXX compiler ABI info - done 2025-07-17T10:28:03.2469969Z -- Check for working CXX compiler: /opt/cache/bin/c++ - skipped 2025-07-17T10:28:03.2474653Z -- Detecting CXX compile features 2025-07-17T10:28:03.2477769Z -- Detecting CXX compile features - done 2025-07-17T10:28:03.2604609Z -- Detecting C compiler ABI info 2025-07-17T10:28:03.7104183Z -- Detecting C compiler ABI info - done 2025-07-17T10:28:03.7248158Z -- Check for working C compiler: /opt/cache/bin/cc - skipped 2025-07-17T10:28:03.7251142Z -- Detecting C compile features 2025-07-17T10:28:03.7256118Z -- Detecting C compile features - done 2025-07-17T10:28:03.7952210Z Building PyTorch for GPU arch: gfx90a;gfx942 2025-07-17T10:28:03.9813328Z -- Found HIP: /opt/rocm (found suitable version "6.4.43483-a187df25c", minimum required is "1.0") 2025-07-17T10:28:03.9814046Z HIP VERSION: 6.4.43483-a187df25c 2025-07-17T10:28:04.2053851Z -- Performing Test CMAKE_HAVE_LIBC_PTHREAD 2025-07-17T10:28:04.7376284Z -- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Success 2025-07-17T10:28:04.7382896Z -- Found Threads: TRUE 2025-07-17T10:28:04.7839550Z hip VERSION: 6.4.43483 2025-07-17T10:28:04.7860680Z -- Reading ROCM version from: /opt/rocm/include/rocm-core/rocm_version.h 2025-07-17T10:28:04.7861042Z -- Content: 2025-07-17T10:28:04.7871345Z  2025-07-17T10:28:04.7871622Z ***** ROCm version from rocm_version.h **** 2025-07-17T10:28:04.7871872Z  2025-07-17T10:28:04.7872074Z ROCM_VERSION_DEV: 6.4.1 2025-07-17T10:28:04.7872325Z ROCM_VERSION_DEV_MAJOR: 6 2025-07-17T10:28:04.7872554Z ROCM_VERSION_DEV_MINOR: 4 2025-07-17T10:28:04.7872792Z ROCM_VERSION_DEV_PATCH: 1 2025-07-17T10:28:04.7873027Z ROCM_VERSION_DEV_INT: 60401 2025-07-17T10:28:04.7873286Z HIP_VERSION_MAJOR: 6 2025-07-17T10:28:04.7873506Z HIP_VERSION_MINOR: 4 2025-07-17T10:28:04.7873726Z TORCH_HIP_VERSION: 604 2025-07-17T10:28:04.7873929Z  2025-07-17T10:28:04.7874118Z ***** Library versions from cmake find_package ***** 2025-07-17T10:28:04.7874354Z  2025-07-17T10:28:04.7874542Z amd_comgr VERSION: 3.0.0 2025-07-17T10:28:04.8375228Z rocrand VERSION: 3.3.0 2025-07-17T10:28:04.8407623Z hiprand VERSION: 2.12.0 2025-07-17T10:28:04.8428040Z rocblas VERSION: 4.4.0 2025-07-17T10:28:04.8469106Z hipblas VERSION: 2.4.0 2025-07-17T10:28:04.8494480Z miopen VERSION: 3.4.0 2025-07-17T10:28:04.8515368Z hipfft VERSION: 1.0.18 2025-07-17T10:28:04.8536082Z hipsparse VERSION: 3.2.0 2025-07-17T10:28:04.8556682Z rocprim VERSION: 3.4.0 2025-07-17T10:28:04.8585499Z hipcub VERSION: 3.4.0 2025-07-17T10:28:04.8606180Z rocthrust VERSION: 3.3.0 2025-07-17T10:28:04.8626182Z hipsolver VERSION: 2.4.0 2025-07-17T10:28:04.8655113Z rocsolver VERSION: 3.28.0 2025-07-17T10:28:04.8655673Z CMake Warning at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Caffe2/public/LoadHIP.cmake:175 (message): 2025-07-17T10:28:04.8656445Z Work around hiprtc cmake failure for cmake >= 4 2025-07-17T10:28:04.8656698Z Call Stack (most recent call first): 2025-07-17T10:28:04.8657100Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Caffe2/Caffe2Config.cmake:74 (include) 2025-07-17T10:28:04.8657649Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Torch/TorchConfig.cmake:68 (find_package) 2025-07-17T10:28:04.8658048Z CMakeLists.txt:27 (find_package) 2025-07-17T10:28:04.8658185Z 2025-07-17T10:28:04.8658276Z  2025-07-17T10:28:04.8670931Z CMake Deprecation Warning at /opt/rocm/lib/cmake/hiprtc/hiprtc-config.cmake:21 (cmake_minimum_required): 2025-07-17T10:28:04.8671668Z Compatibility with CMake < 3.10 will be removed from a future version of 2025-07-17T10:28:04.8671957Z CMake. 2025-07-17T10:28:04.8672046Z 2025-07-17T10:28:04.8672211Z Update the VERSION argument value. Or, use the ... syntax 2025-07-17T10:28:04.8672553Z to tell CMake that the project requires at least but has been updated 2025-07-17T10:28:04.8672879Z to work with policies introduced by or earlier. 2025-07-17T10:28:04.8673132Z Call Stack (most recent call first): 2025-07-17T10:28:04.8673558Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Caffe2/public/LoadHIP.cmake:67 (find_package) 2025-07-17T10:28:04.8674229Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Caffe2/public/LoadHIP.cmake:177 (find_package_and_print_version) 2025-07-17T10:28:04.8674836Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Caffe2/Caffe2Config.cmake:74 (include) 2025-07-17T10:28:04.8675381Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Torch/TorchConfig.cmake:68 (find_package) 2025-07-17T10:28:04.8676009Z CMakeLists.txt:27 (find_package) 2025-07-17T10:28:04.8676150Z 2025-07-17T10:28:04.8676239Z  2025-07-17T10:28:04.8676438Z hiprtc VERSION: 6.4.43483 2025-07-17T10:28:04.8697457Z hipblaslt VERSION: 0.12.1 2025-07-17T10:28:04.9224884Z rccl VERSION: 2.22.3 2025-07-17T10:28:04.9229707Z hsa-runtime64 VERSION: 1.15.60401 2025-07-17T10:28:04.9252120Z hipsparselt VERSION: 0.2.3 2025-07-17T10:28:06.3499823Z hipblaslt is using scale pointer vec ext 2025-07-17T10:28:06.4767000Z CMake Warning at /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Torch/TorchConfig.cmake:22 (message): 2025-07-17T10:28:06.4767531Z static library kineto_LIBRARY-NOTFOUND not found. 2025-07-17T10:28:06.4767791Z Call Stack (most recent call first): 2025-07-17T10:28:06.4768289Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/share/cmake/Torch/TorchConfig.cmake:125 (append_torchlib_if_found) 2025-07-17T10:28:06.4768734Z CMakeLists.txt:27 (find_package) 2025-07-17T10:28:06.4768896Z 2025-07-17T10:28:06.4768976Z  2025-07-17T10:28:06.4773579Z -- Found Torch: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch.so 2025-07-17T10:28:06.4811647Z -- Configuring done (4.3s) 2025-07-17T10:28:06.4976186Z -- Generating done (0.0s) 2025-07-17T10:28:06.4981449Z -- Build files have been written to: /var/lib/jenkins/pytorch/test/cpp_extensions/open_registration_extension/torch_openreg/build 2025-07-17T10:28:06.6308316Z [ 5%] Building CXX object third_party/openreg/CMakeFiles/openreg.dir/csrc/device.cpp.o 2025-07-17T10:28:06.6309129Z [ 11%] Building CXX object third_party/openreg/CMakeFiles/openreg.dir/csrc/memory.cpp.o 2025-07-17T10:28:06.7376326Z [ 17%] Linking CXX shared library libopenreg.so 2025-07-17T10:28:06.8208666Z [ 17%] Built target openreg 2025-07-17T10:28:06.8308426Z [ 29%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/OpenRegExtra.cpp.o 2025-07-17T10:28:06.8309289Z [ 41%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/native/Extra.cpp.o 2025-07-17T10:28:06.8309912Z [ 41%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/OpenRegMinimal.cpp.o 2025-07-17T10:28:06.8311033Z [ 41%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegDeviceAllocator.cpp.o 2025-07-17T10:28:06.8311883Z [ 47%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegGenerator.cpp.o 2025-07-17T10:28:06.8314403Z [ 58%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/native/Minimal.cpp.o 2025-07-17T10:28:06.8314911Z [ 58%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegGuard.cpp.o 2025-07-17T10:28:06.8319275Z [ 76%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegHostAllocator.cpp.o 2025-07-17T10:28:06.8320013Z [ 76%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegFunctions.cpp.o 2025-07-17T10:28:06.8320548Z [ 76%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegSerialization.cpp.o 2025-07-17T10:28:06.8321721Z [ 82%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegHooks.cpp.o 2025-07-17T10:28:07.5490206Z [ 88%] Linking CXX shared library libtorch_openreg.so 2025-07-17T10:28:07.9741535Z [ 88%] Built target torch_openreg 2025-07-17T10:28:07.9822799Z [ 94%] Building CXX object torch_openreg/csrc/CMakeFiles/torch_bindings.dir/Module.cpp.o 2025-07-17T10:28:08.6638467Z [100%] Linking CXX shared library libtorch_bindings.so 2025-07-17T10:28:08.8220847Z [100%] Built target torch_bindings 2025-07-17T10:28:08.8301524Z Install the project... 2025-07-17T10:28:08.8334095Z -- Install configuration: "" 2025-07-17T10:28:08.8941624Z running install 2025-07-17T10:28:08.8942225Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:90: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-07-17T10:28:08.8943103Z !! 2025-07-17T10:28:08.8943200Z 2025-07-17T10:28:08.8943296Z ******************************************************************************** 2025-07-17T10:28:08.8943555Z Please avoid running ``setup.py`` directly. 2025-07-17T10:28:08.8943847Z Instead, use pypa/build, pypa/installer or other 2025-07-17T10:28:08.8944100Z standards-based tools. 2025-07-17T10:28:08.8944240Z 2025-07-17T10:28:08.8944386Z By 2025-Oct-31, you need to update your project and remove deprecated calls 2025-07-17T10:28:08.8944689Z or your builds will no longer be supported. 2025-07-17T10:28:08.8944830Z 2025-07-17T10:28:08.8945045Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-07-17T10:28:08.8945514Z ******************************************************************************** 2025-07-17T10:28:08.8945670Z 2025-07-17T10:28:08.8945757Z !! 2025-07-17T10:28:08.8945924Z self.initialize_options() 2025-07-17T10:28:08.9094644Z running build 2025-07-17T10:28:08.9094856Z running build_py 2025-07-17T10:28:08.9185583Z creating build/lib.linux-x86_64-cpython-312/torch_openreg 2025-07-17T10:28:08.9187231Z copying torch_openreg/__init__.py -> build/lib.linux-x86_64-cpython-312/torch_openreg 2025-07-17T10:28:08.9190578Z creating build/lib.linux-x86_64-cpython-312/torch_openreg/openreg 2025-07-17T10:28:08.9192381Z copying torch_openreg/openreg/random.py -> build/lib.linux-x86_64-cpython-312/torch_openreg/openreg 2025-07-17T10:28:08.9194361Z copying torch_openreg/openreg/__init__.py -> build/lib.linux-x86_64-cpython-312/torch_openreg/openreg 2025-07-17T10:28:08.9200792Z creating build/lib.linux-x86_64-cpython-312/torch_openreg/lib 2025-07-17T10:28:08.9202233Z copying torch_openreg/lib/libtorch_openreg.so -> build/lib.linux-x86_64-cpython-312/torch_openreg/lib 2025-07-17T10:28:08.9221141Z copying torch_openreg/lib/libtorch_bindings.so -> build/lib.linux-x86_64-cpython-312/torch_openreg/lib 2025-07-17T10:28:08.9227925Z copying torch_openreg/lib/libopenreg.so -> build/lib.linux-x86_64-cpython-312/torch_openreg/lib 2025-07-17T10:28:08.9231526Z running build_ext 2025-07-17T10:28:08.9335624Z building 'torch_openreg._C' extension 2025-07-17T10:28:08.9336749Z creating build/temp.linux-x86_64-cpython-312/torch_openreg/csrc 2025-07-17T10:28:08.9340035Z gcc -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/include/python3.12 -c torch_openreg/csrc/stub.c -o build/temp.linux-x86_64-cpython-312/torch_openreg/csrc/stub.o -g -Wall -Werror 2025-07-17T10:28:08.9716130Z gcc -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/torch_openreg/csrc/stub.o -L/var/lib/jenkins/pytorch/test/cpp_extensions/open_registration_extension/torch_openreg/torch_openreg/lib -ltorch_bindings -o build/lib.linux-x86_64-cpython-312/torch_openreg/_C.cpython-312-x86_64-linux-gnu.so -Wl,-rpath,$ORIGIN/lib 2025-07-17T10:28:09.0213066Z running install_lib 2025-07-17T10:28:09.0292840Z copying build/lib.linux-x86_64-cpython-312/torch_openreg/lib/libopenreg.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg/lib 2025-07-17T10:28:09.0296109Z copying build/lib.linux-x86_64-cpython-312/torch_openreg/lib/libtorch_bindings.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg/lib 2025-07-17T10:28:09.0303844Z copying build/lib.linux-x86_64-cpython-312/torch_openreg/lib/libtorch_openreg.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg/lib 2025-07-17T10:28:09.0324138Z copying build/lib.linux-x86_64-cpython-312/torch_openreg/_C.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg 2025-07-17T10:28:09.0329655Z running install_egg_info 2025-07-17T10:28:09.0487052Z running egg_info 2025-07-17T10:28:09.0553076Z writing torch_openreg.egg-info/PKG-INFO 2025-07-17T10:28:09.0557174Z writing dependency_links to torch_openreg.egg-info/dependency_links.txt 2025-07-17T10:28:09.0558674Z writing requirements to torch_openreg.egg-info/requires.txt 2025-07-17T10:28:09.0559574Z writing top-level names to torch_openreg.egg-info/top_level.txt 2025-07-17T10:28:09.0632175Z reading manifest file 'torch_openreg.egg-info/SOURCES.txt' 2025-07-17T10:28:09.0638804Z writing manifest file 'torch_openreg.egg-info/SOURCES.txt' 2025-07-17T10:28:09.0640196Z removing './install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg-0.0.1-py3.12.egg-info' (and everything under it) 2025-07-17T10:28:09.0645435Z Copying torch_openreg.egg-info to ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_openreg-0.0.1-py3.12.egg-info 2025-07-17T10:28:09.0655542Z running install_scripts 2025-07-17T10:28:09.5506549Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:28:09.5508649Z Executing ['/opt/conda/envs/py_3.12/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 10:28:09.550632] 2025-07-17T10:28:13.1727167Z 2025-07-17T10:28:13.1728197Z test_transformers_privateuse1 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_transformers_privateuse1_1.1_8433a1d27601b25f_.log 2025-07-17T10:28:13.1729570Z 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 2025-07-17T10:28:13.1730584Z 2025-07-17T10:28:13.1730714Z Running test_utils 1/1 ... [2025-07-17 10:28:13.172703] 2025-07-17T10:28:13.1731501Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:28:13.1732133Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_utils.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 10:28:13.172988] 2025-07-17T10:29:05.2909701Z 2025-07-17T10:29:05.2910770Z test_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_utils_1.1_a08a34e89e08d7e1_.log 2025-07-17T10:29:05.4116009Z Running 6000 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::TestDeviceUtilsCUDA::test_basic_cuda, test/test_utils.py::TestDeviceUtilsCUDA::test_decorator_cuda, test/test_utils.py::TestDeviceUtilsCUDA::test_decorator_generator_cuda, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_H_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_H_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_H_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_H_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_H_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_H_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_H_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_H_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_H_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_H_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_H_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_H_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_H_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_T_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_T_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_T_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_T_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_T_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_T_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_T_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_T_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_T_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_T_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_T_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_T_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_T_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___getitem___cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___getitem___cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___getitem___cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___getitem___cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___getitem___cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___getitem___cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___getitem___cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___getitem___cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___getitem___cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___getitem___cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___getitem___cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___getitem___cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___getitem___cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___radd___cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___radd___cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___radd___cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___radd___cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___radd___cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___radd___cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___radd___cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___radd___cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___radd___cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___radd___cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___radd___cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___radd___cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rand___cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rand___cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rand___cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rand___cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rand___cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rand___cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rdiv___cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rdiv___cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rdiv___cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rdiv___cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rdiv___cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rdiv___cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rdiv___cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rdiv___cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rdiv___cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rdiv___cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rdiv___cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rdiv___cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rmatmul___cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rmatmul___cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rmatmul___cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rmatmul___cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rmatmul___cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rmatmul___cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rmod___cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rmod___cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rmod___cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rmod___cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rmod___cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rmod___cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rmod___cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rmod___cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rmod___cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rmul___cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rmul___cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rmul___cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rmul___cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rmul___cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rmul___cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rmul___cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rmul___cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rmul___cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rmul___cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rmul___cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rmul___cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___ror___cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___ror___cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___ror___cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___ror___cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___ror___cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___ror___cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rpow___cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rpow___cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rpow___cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rpow___cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rpow___cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rpow___cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rpow___cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rpow___cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rpow___cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rpow___cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rpow___cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rsub___cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rsub___cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rsub___cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rsub___cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rsub___cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rsub___cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rsub___cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rsub___cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rsub___cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rsub___cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rsub___cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rxor___cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rxor___cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rxor___cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rxor___cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rxor___cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops___rxor___cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__batch_norm_with_update_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__batch_norm_with_update_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__batch_norm_with_update_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__batch_norm_with_update_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__chunk_cat_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__chunk_cat_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__chunk_cat_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__chunk_cat_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__chunk_cat_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__chunk_cat_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__chunk_cat_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__chunk_cat_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__chunk_cat_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__chunk_cat_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__chunk_cat_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops__chunk_cat_cuda_int8, 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test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bfloat16_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bfloat16_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bfloat16_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bfloat16_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bfloat16_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bfloat16_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bfloat16_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bfloat16_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bfloat16_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bfloat16_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bfloat16_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bincount_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bincount_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bincount_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bincount_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bincount_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_and_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_and_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_and_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_and_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_and_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_and_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_left_shift_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_left_shift_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_left_shift_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_left_shift_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_left_shift_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_not_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_not_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_not_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_not_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_not_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_not_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_or_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_or_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_or_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_or_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_or_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_or_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_right_shift_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_right_shift_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_right_shift_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_right_shift_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_right_shift_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_xor_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_xor_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_xor_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_xor_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_xor_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bitwise_xor_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_block_diag_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_block_diag_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_block_diag_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_block_diag_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_block_diag_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_block_diag_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_block_diag_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_block_diag_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_block_diag_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_block_diag_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_block_diag_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_block_diag_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_block_diag_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bmm_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bmm_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bmm_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bmm_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bmm_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bmm_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bool_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bool_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bool_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bool_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bool_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bool_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bool_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bool_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bool_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bool_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bool_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bool_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bool_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_broadcast_shapes_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_broadcast_tensors_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_broadcast_tensors_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_broadcast_tensors_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_broadcast_tensors_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_broadcast_tensors_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_broadcast_tensors_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_broadcast_tensors_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_broadcast_tensors_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_broadcast_tensors_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_broadcast_tensors_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_broadcast_tensors_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_broadcast_tensors_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_broadcast_to_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_broadcast_to_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_broadcast_to_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_broadcast_to_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_broadcast_to_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_broadcast_to_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_broadcast_to_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_broadcast_to_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_broadcast_to_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_broadcast_to_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_broadcast_to_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_broadcast_to_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bucketize_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bucketize_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bucketize_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bucketize_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bucketize_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bucketize_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bucketize_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bucketize_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_bucketize_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_byte_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_byte_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_byte_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_byte_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_byte_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_byte_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_byte_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_byte_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_byte_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_byte_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_byte_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_byte_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cartesian_prod_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cartesian_prod_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cartesian_prod_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cartesian_prod_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cartesian_prod_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cartesian_prod_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cartesian_prod_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cartesian_prod_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cartesian_prod_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cartesian_prod_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cartesian_prod_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cartesian_prod_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cat_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cat_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cat_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cat_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cat_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cat_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cat_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cat_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cat_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cat_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cat_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cat_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cat_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cauchy_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cauchy_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cauchy_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cauchy_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cdist_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cdist_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cdouble_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cdouble_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cdouble_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cdouble_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cdouble_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cdouble_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cdouble_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cdouble_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cdouble_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cdouble_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cdouble_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cdouble_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cdouble_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ceil_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ceil_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ceil_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ceil_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ceil_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ceil_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ceil_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ceil_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ceil_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cfloat_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cfloat_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cfloat_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cfloat_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cfloat_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cfloat_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cfloat_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cfloat_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cfloat_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cfloat_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cfloat_cuda_int64, 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test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cholesky_inverse_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cholesky_solve_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cholesky_solve_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cholesky_solve_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_cholesky_solve_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_chunk_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_chunk_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_chunk_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_chunk_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_chunk_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_chunk_cuda_float16, 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test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_contiguous_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_contiguous_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_contiguous_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_contiguous_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_copysign_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_copysign_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_copysign_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_copysign_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_copysign_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_copysign_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_copysign_cuda_int32, 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test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_div_floor_rounding_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_div_no_rounding_mode_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_div_no_rounding_mode_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_div_no_rounding_mode_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_div_no_rounding_mode_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_div_no_rounding_mode_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_div_no_rounding_mode_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_div_no_rounding_mode_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_div_no_rounding_mode_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_div_no_rounding_mode_cuda_int16, 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test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fft_fft_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fft_fft_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fft_fft_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fft_fft_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fft_fft_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fft_fft_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fft_fft_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fft_fft_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fft_fftn_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fft_fftn_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fft_fftn_cuda_complex32, 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test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fft_rfft2_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fft_rfft2_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fft_rfft2_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fft_rfft_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fft_rfft_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fft_rfft_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fft_rfft_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fft_rfft_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fft_rfft_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fft_rfft_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fft_rfft_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fft_rfft_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fft_rfftn_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fft_rfftn_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fft_rfftn_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fft_rfftn_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fft_rfftn_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fft_rfftn_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fft_rfftn_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fft_rfftn_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fft_rfftn_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fill_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fill_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fill_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fill_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fill_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fill_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fill_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fill_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fill_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fill_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fill_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fill_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fill_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flatten_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flatten_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flatten_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flatten_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flatten_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flatten_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flatten_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flatten_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flatten_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flatten_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flatten_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flatten_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flatten_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flip_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flip_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flip_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flip_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flip_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flip_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flip_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flip_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flip_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flip_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flip_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flip_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fliplr_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fliplr_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fliplr_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fliplr_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fliplr_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fliplr_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fliplr_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fliplr_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fliplr_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fliplr_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fliplr_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fliplr_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flipud_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flipud_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flipud_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flipud_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flipud_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flipud_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flipud_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flipud_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flipud_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flipud_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flipud_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_flipud_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_float_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_float_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_float_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_float_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_float_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_float_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_float_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_float_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_float_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_float_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_float_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_float_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_float_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_float_power_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_float_power_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_float_power_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_float_power_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_float_power_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_float_power_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_float_power_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_float_power_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_float_power_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_float_power_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_float_power_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_float_power_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_floor_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_floor_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_floor_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_floor_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_floor_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_floor_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_floor_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_floor_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_floor_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_floor_divide_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_floor_divide_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_floor_divide_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_floor_divide_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_floor_divide_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_floor_divide_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_floor_divide_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_floor_divide_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_floor_divide_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fmax_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fmax_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fmax_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fmax_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fmax_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fmax_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fmax_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fmax_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fmax_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fmax_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fmin_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fmin_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fmin_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fmin_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fmin_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fmin_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fmin_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fmin_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fmin_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fmin_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fmod_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fmod_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fmod_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fmod_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fmod_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fmod_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fmod_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fmod_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_fmod_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_frac_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_frac_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_frac_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_frac_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_frexp_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_frexp_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_frexp_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_frexp_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_full_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_full_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_full_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_full_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_full_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_full_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_full_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_full_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_full_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_full_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_full_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_full_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_full_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_full_like_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_full_like_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_full_like_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_full_like_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_full_like_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_full_like_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_full_like_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_full_like_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_full_like_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_full_like_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_full_like_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_full_like_cuda_uint16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_full_like_cuda_uint32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_full_like_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gather_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gather_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gather_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gather_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gather_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gather_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gather_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gather_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gather_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gather_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gather_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gather_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gcd_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gcd_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gcd_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gcd_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gcd_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ge_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ge_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ge_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ge_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ge_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ge_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ge_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ge_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ge_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ge_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_geometric_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_geometric_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_geometric_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_geometric_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_geometric_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_geometric_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_geometric_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_geometric_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_geometric_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_geqrf_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_geqrf_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_geqrf_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_geqrf_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gradient_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gradient_cuda_complex128, 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test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_grid_sampler_2d_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gt_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gt_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gt_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gt_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gt_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gt_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gt_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gt_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gt_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_gt_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_half_cuda_bfloat16, 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test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_cond_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_cond_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_cond_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_cond_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_cross_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_cross_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_cross_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_cross_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_cross_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_cross_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_cross_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_cross_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_cross_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_cross_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_cross_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_det_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_det_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_det_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_det_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_diagonal_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_diagonal_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_diagonal_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_diagonal_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_diagonal_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_diagonal_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_diagonal_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_diagonal_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_diagonal_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_diagonal_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_diagonal_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_diagonal_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_diagonal_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_eig_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_eig_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_eig_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_eig_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_eigh_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_eigh_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_eigh_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_eigh_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_eigvals_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_eigvals_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_eigvals_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_eigvals_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_eigvalsh_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_eigvalsh_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_eigvalsh_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_eigvalsh_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_householder_product_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_householder_product_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_householder_product_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_householder_product_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_inv_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_inv_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_inv_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_inv_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_inv_ex_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_inv_ex_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_inv_ex_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_inv_ex_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_ldl_factor_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_ldl_factor_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_ldl_factor_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_ldl_factor_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_ldl_factor_ex_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_ldl_factor_ex_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_ldl_factor_ex_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_ldl_factor_ex_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_ldl_solve_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_ldl_solve_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_ldl_solve_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_ldl_solve_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_lstsq_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_lstsq_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_lstsq_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_lstsq_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_lstsq_grad_oriented_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_lstsq_grad_oriented_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_lstsq_grad_oriented_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_lstsq_grad_oriented_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_lu_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_lu_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_lu_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_lu_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_lu_factor_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_lu_factor_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_lu_factor_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_lu_factor_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_lu_factor_ex_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_lu_factor_ex_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_lu_factor_ex_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_lu_factor_ex_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_lu_solve_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_lu_solve_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_lu_solve_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_lu_solve_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_matrix_norm_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_matrix_norm_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_matrix_norm_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_matrix_norm_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_matrix_norm_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_matrix_norm_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_matrix_power_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_matrix_power_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_matrix_power_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_matrix_power_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_matrix_rank_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_matrix_rank_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_matrix_rank_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_matrix_rank_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_matrix_rank_hermitian_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_matrix_rank_hermitian_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_matrix_rank_hermitian_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_matrix_rank_hermitian_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_multi_dot_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_multi_dot_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_multi_dot_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_multi_dot_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_multi_dot_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_multi_dot_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_norm_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_norm_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_norm_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_norm_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_norm_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_norm_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_norm_subgradients_at_zero_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_norm_subgradients_at_zero_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_norm_subgradients_at_zero_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_norm_subgradients_at_zero_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_norm_subgradients_at_zero_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_norm_subgradients_at_zero_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_pinv_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_pinv_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_pinv_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_pinv_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_pinv_hermitian_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_pinv_hermitian_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_pinv_hermitian_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_pinv_hermitian_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_pinv_singular_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_pinv_singular_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_pinv_singular_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_pinv_singular_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_qr_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_qr_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_qr_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_qr_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_slogdet_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_slogdet_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_slogdet_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_slogdet_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_solve_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_solve_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_solve_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_solve_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_solve_ex_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_solve_ex_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_solve_ex_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_solve_ex_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_solve_triangular_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_solve_triangular_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_solve_triangular_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_solve_triangular_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_svd_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_svd_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_svd_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_svd_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_svdvals_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_svdvals_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_svdvals_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_svdvals_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_tensorinv_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_tensorinv_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_tensorinv_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_tensorinv_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_tensorsolve_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_tensorsolve_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_tensorsolve_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_tensorsolve_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_vander_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_vander_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_vander_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_vander_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_vander_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_vander_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_vander_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_vander_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_vander_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_vecdot_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_vecdot_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_vecdot_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_vecdot_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_vecdot_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_vecdot_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_vector_norm_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_vector_norm_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_vector_norm_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_vector_norm_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_vector_norm_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linalg_vector_norm_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linspace_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linspace_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linspace_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linspace_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linspace_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linspace_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linspace_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linspace_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linspace_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linspace_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linspace_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linspace_tensor_overload_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linspace_tensor_overload_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linspace_tensor_overload_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linspace_tensor_overload_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linspace_tensor_overload_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linspace_tensor_overload_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linspace_tensor_overload_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linspace_tensor_overload_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linspace_tensor_overload_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linspace_tensor_overload_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_linspace_tensor_overload_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_log10_cuda_bfloat16, 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test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logaddexp2_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logaddexp2_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logaddexp2_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logaddexp2_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logaddexp_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logaddexp_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logaddexp_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logaddexp_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logcumsumexp_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logcumsumexp_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logcumsumexp_cuda_complex64, 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test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logical_xor_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logical_xor_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logical_xor_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logical_xor_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logical_xor_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logical_xor_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logical_xor_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logical_xor_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logical_xor_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logical_xor_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logical_xor_cuda_uint8, 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test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logspace_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logspace_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logspace_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logspace_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logspace_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logspace_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logspace_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logspace_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logspace_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logspace_tensor_overload_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logspace_tensor_overload_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logspace_tensor_overload_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logspace_tensor_overload_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logspace_tensor_overload_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logspace_tensor_overload_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logspace_tensor_overload_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logspace_tensor_overload_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logspace_tensor_overload_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logspace_tensor_overload_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logspace_tensor_overload_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_logsumexp_cuda_bfloat16, 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test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_mH_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_mH_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_mH_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_mH_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_mH_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_mH_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_mH_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_mH_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_mH_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_mH_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_mH_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_mH_cuda_int8, 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test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nan_to_num_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nan_to_num_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nan_to_num_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nan_to_num_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nan_to_num_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nan_to_num_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nan_to_num_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nan_to_num_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nan_to_num_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nan_to_num_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nanmean_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nanmean_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nanmean_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nanmean_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nanmean_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nanmean_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nanmean_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nanmedian_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nanmedian_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nanmedian_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nanmedian_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nanmedian_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nanmedian_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nanmedian_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nanmedian_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nanmedian_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nanquantile_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nanquantile_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nansum_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nansum_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nansum_cuda_complex128, 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test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_narrow_copy_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_narrow_copy_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_narrow_copy_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_narrow_copy_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_narrow_copy_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_narrow_copy_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_narrow_copy_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_narrow_copy_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_narrow_copy_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_narrow_copy_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_narrow_copy_cuda_uint8, 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test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_native_layer_norm_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_native_layer_norm_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_native_layer_norm_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ne_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ne_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ne_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ne_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ne_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ne_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ne_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ne_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ne_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ne_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ne_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ne_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_neg_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_neg_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_neg_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_neg_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_neg_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_neg_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_neg_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_neg_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_neg_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_neg_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_neg_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_neg_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_new_empty_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_new_empty_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_new_empty_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_new_empty_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_new_empty_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_new_empty_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_new_empty_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_new_empty_cuda_float64, 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test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_new_zeros_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_new_zeros_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nextafter_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nextafter_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nextafter_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nextafter_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cuda_float64, 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test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_adaptive_max_pool3d_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_adaptive_max_pool3d_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_alpha_dropout_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_alpha_dropout_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_alpha_dropout_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_alpha_dropout_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_avg_pool1d_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_avg_pool1d_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_avg_pool1d_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_avg_pool1d_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_avg_pool2d_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_avg_pool2d_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_avg_pool2d_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_avg_pool2d_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_avg_pool3d_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_avg_pool3d_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_avg_pool3d_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_avg_pool3d_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_batch_norm_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_batch_norm_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_batch_norm_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_batch_norm_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_batch_norm_without_cudnn_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_batch_norm_without_cudnn_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_batch_norm_without_cudnn_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_batch_norm_without_cudnn_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_bilinear_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_bilinear_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_bilinear_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_bilinear_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_binary_cross_entropy_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_binary_cross_entropy_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_binary_cross_entropy_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_binary_cross_entropy_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_binary_cross_entropy_with_logits_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_binary_cross_entropy_with_logits_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_binary_cross_entropy_with_logits_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_binary_cross_entropy_with_logits_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_celu_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_celu_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_celu_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_celu_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_channel_shuffle_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_channel_shuffle_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_channel_shuffle_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_channel_shuffle_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_channel_shuffle_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_channel_shuffle_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_channel_shuffle_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_channel_shuffle_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_channel_shuffle_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_channel_shuffle_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_channel_shuffle_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_channel_shuffle_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv1d_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv1d_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv1d_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv1d_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv1d_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv1d_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv1d_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv2d_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv2d_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv2d_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv2d_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv2d_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv2d_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv2d_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv3d_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv3d_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv3d_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv3d_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv3d_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv3d_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv3d_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv_transpose1d_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv_transpose1d_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv_transpose1d_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv_transpose1d_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv_transpose1d_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv_transpose1d_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv_transpose1d_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv_transpose2d_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv_transpose2d_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv_transpose2d_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv_transpose2d_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv_transpose2d_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv_transpose2d_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv_transpose2d_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv_transpose3d_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv_transpose3d_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv_transpose3d_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv_transpose3d_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv_transpose3d_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv_transpose3d_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_conv_transpose3d_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_cosine_embedding_loss_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_cosine_embedding_loss_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_cosine_embedding_loss_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_cosine_embedding_loss_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_cosine_embedding_loss_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_cosine_embedding_loss_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_cosine_embedding_loss_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_cosine_embedding_loss_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_cosine_embedding_loss_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_cosine_embedding_loss_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_cosine_similarity_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_cosine_similarity_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_cosine_similarity_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_cosine_similarity_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_cross_entropy_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_cross_entropy_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_cross_entropy_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_cross_entropy_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_ctc_loss_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_ctc_loss_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_dropout2d_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_dropout2d_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_dropout2d_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_dropout2d_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_dropout3d_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_dropout3d_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_dropout3d_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_dropout3d_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_dropout_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_dropout_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_dropout_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_dropout_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_elu_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_elu_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_elu_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_elu_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_embedding_bag_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_embedding_bag_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_embedding_bag_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_embedding_bag_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_embedding_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_embedding_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_embedding_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_embedding_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_feature_alpha_dropout_with_train_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_feature_alpha_dropout_with_train_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_feature_alpha_dropout_with_train_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_feature_alpha_dropout_with_train_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_fractional_max_pool2d_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_fractional_max_pool2d_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_fractional_max_pool2d_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_fractional_max_pool2d_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_fractional_max_pool3d_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_fractional_max_pool3d_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_fractional_max_pool3d_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_fractional_max_pool3d_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_gaussian_nll_loss_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_gaussian_nll_loss_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_gaussian_nll_loss_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_gaussian_nll_loss_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_gelu_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_gelu_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_gelu_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_gelu_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_glu_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_glu_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_glu_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_glu_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_grid_sample_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_grid_sample_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_grid_sample_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_grid_sample_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_group_norm_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_group_norm_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_group_norm_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_group_norm_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_hardshrink_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_hardshrink_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_hardshrink_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_hardshrink_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_hardsigmoid_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_hardsigmoid_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_hardsigmoid_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_hardsigmoid_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_hardswish_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_hardswish_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_hardswish_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_hardswish_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_hardtanh_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_hardtanh_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_hardtanh_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_hardtanh_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_hardtanh_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_hardtanh_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_hardtanh_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_hardtanh_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_hinge_embedding_loss_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_hinge_embedding_loss_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_hinge_embedding_loss_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_hinge_embedding_loss_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_huber_loss_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_huber_loss_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_huber_loss_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_huber_loss_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_instance_norm_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_instance_norm_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_instance_norm_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_instance_norm_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_interpolate_area_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_interpolate_area_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_interpolate_area_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_interpolate_area_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_interpolate_bicubic_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_interpolate_bicubic_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_interpolate_bicubic_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_interpolate_bicubic_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_interpolate_bilinear_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_interpolate_bilinear_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_interpolate_bilinear_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_interpolate_bilinear_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_interpolate_linear_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_interpolate_linear_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_interpolate_linear_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_interpolate_linear_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_interpolate_nearest_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_interpolate_nearest_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_interpolate_nearest_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_interpolate_nearest_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_interpolate_nearest_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_interpolate_trilinear_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_interpolate_trilinear_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_interpolate_trilinear_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_interpolate_trilinear_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_kl_div_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_kl_div_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_kl_div_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_kl_div_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_l1_loss_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_l1_loss_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_l1_loss_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_l1_loss_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_l1_loss_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_l1_loss_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_layer_norm_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_layer_norm_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_layer_norm_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_layer_norm_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_leaky_relu_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_leaky_relu_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_leaky_relu_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_leaky_relu_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_linear_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_linear_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_linear_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_linear_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_linear_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_linear_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_local_response_norm_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_local_response_norm_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_local_response_norm_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_local_response_norm_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_logsigmoid_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_logsigmoid_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_logsigmoid_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_logsigmoid_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_margin_ranking_loss_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_margin_ranking_loss_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_margin_ranking_loss_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_margin_ranking_loss_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_margin_ranking_loss_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_margin_ranking_loss_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_margin_ranking_loss_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_margin_ranking_loss_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_margin_ranking_loss_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_pool1d_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_pool1d_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_pool1d_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_pool1d_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_pool2d_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_pool2d_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_pool2d_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_pool2d_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_pool3d_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_pool3d_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_pool3d_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_pool3d_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_unpool1d_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_unpool1d_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_unpool1d_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_unpool1d_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_unpool1d_grad_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_unpool1d_grad_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_unpool1d_grad_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_unpool1d_grad_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_unpool2d_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_unpool2d_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_unpool2d_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_unpool2d_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_unpool2d_grad_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_unpool2d_grad_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_unpool2d_grad_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_unpool2d_grad_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_unpool3d_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_unpool3d_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_unpool3d_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_unpool3d_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_unpool3d_grad_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_unpool3d_grad_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_unpool3d_grad_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_max_unpool3d_grad_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_mish_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_mish_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_mish_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_mish_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_mse_loss_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_mse_loss_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_mse_loss_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_mse_loss_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_multi_head_attention_forward_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_multi_head_attention_forward_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_multi_head_attention_forward_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_multi_head_attention_forward_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_multi_margin_loss_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_multi_margin_loss_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_multi_margin_loss_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_multi_margin_loss_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_multilabel_margin_loss_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_multilabel_margin_loss_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_multilabel_margin_loss_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_multilabel_margin_loss_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_multilabel_soft_margin_loss_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_multilabel_soft_margin_loss_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_multilabel_soft_margin_loss_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_multilabel_soft_margin_loss_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_nll_loss_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_nll_loss_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_nll_loss_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_nll_loss_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_normalize_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_normalize_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_normalize_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_normalize_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_normalize_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_normalize_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_one_hot_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_circular_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_circular_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_circular_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_circular_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_circular_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_circular_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_circular_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_circular_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_circular_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_circular_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_circular_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_circular_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_constant_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_constant_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_constant_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_constant_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_constant_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_constant_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_constant_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_constant_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_constant_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_constant_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_constant_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_constant_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_reflect_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_reflect_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_reflect_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_reflect_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_reflect_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_reflect_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_reflect_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_reflect_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_reflect_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_reflect_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_reflect_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_replicate_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_replicate_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_replicate_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_replicate_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_replicate_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_replicate_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_replicate_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_replicate_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_replicate_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_replicate_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_replicate_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_replicate_negative_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_replicate_negative_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_replicate_negative_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_replicate_negative_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_replicate_negative_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_replicate_negative_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_replicate_negative_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_replicate_negative_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_replicate_negative_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_replicate_negative_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pad_replicate_negative_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pairwise_distance_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pairwise_distance_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pairwise_distance_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pairwise_distance_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pairwise_distance_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pairwise_distance_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pairwise_distance_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pairwise_distance_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pairwise_distance_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pairwise_distance_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pairwise_distance_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pdist_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pdist_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pixel_shuffle_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pixel_shuffle_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pixel_shuffle_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pixel_shuffle_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pixel_shuffle_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pixel_shuffle_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pixel_shuffle_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pixel_shuffle_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pixel_shuffle_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pixel_shuffle_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pixel_shuffle_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pixel_shuffle_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pixel_unshuffle_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pixel_unshuffle_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pixel_unshuffle_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pixel_unshuffle_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pixel_unshuffle_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pixel_unshuffle_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pixel_unshuffle_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pixel_unshuffle_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pixel_unshuffle_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pixel_unshuffle_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pixel_unshuffle_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_pixel_unshuffle_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_poisson_nll_loss_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_poisson_nll_loss_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_poisson_nll_loss_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_poisson_nll_loss_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_poisson_nll_loss_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_poisson_nll_loss_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_poisson_nll_loss_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_poisson_nll_loss_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_poisson_nll_loss_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_prelu_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_prelu_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_prelu_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_prelu_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_relu6_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_relu6_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_relu6_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_relu6_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_relu6_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_relu6_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_relu6_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_relu6_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_relu6_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_relu_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_relu_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_relu_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_relu_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_relu_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_relu_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_relu_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_relu_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_relu_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_rms_norm_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_rms_norm_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_rms_norm_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_rms_norm_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_rms_norm_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_rms_norm_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_rrelu_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_rrelu_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_rrelu_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_rrelu_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_scaled_dot_product_attention_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_scaled_dot_product_attention_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_scaled_dot_product_attention_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_scaled_dot_product_attention_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_selu_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_selu_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_selu_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_selu_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_silu_complex_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_silu_complex_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_silu_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_silu_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_silu_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_silu_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_smooth_l1_loss_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_smooth_l1_loss_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_smooth_l1_loss_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_smooth_l1_loss_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_soft_margin_loss_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_soft_margin_loss_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_soft_margin_loss_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_soft_margin_loss_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softmin_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softmin_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softmin_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softmin_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softmin_with_dtype_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softmin_with_dtype_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softmin_with_dtype_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softmin_with_dtype_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softmin_with_dtype_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softmin_with_dtype_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softmin_with_dtype_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softmin_with_dtype_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softmin_with_dtype_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softmin_with_dtype_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softmin_with_dtype_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softplus_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softplus_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softplus_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softplus_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softshrink_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softshrink_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softshrink_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softshrink_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softsign_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softsign_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softsign_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softsign_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softsign_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softsign_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softsign_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softsign_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softsign_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softsign_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softsign_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_softsign_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_tanhshrink_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_tanhshrink_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_tanhshrink_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_tanhshrink_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_tanhshrink_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_tanhshrink_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_tanhshrink_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_tanhshrink_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_tanhshrink_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_tanhshrink_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_tanhshrink_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_threshold_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_threshold_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_threshold_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_threshold_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_threshold_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_threshold_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_threshold_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_threshold_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_threshold_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_triplet_margin_loss_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_triplet_margin_loss_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_triplet_margin_loss_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_triplet_margin_loss_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_triplet_margin_loss_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_triplet_margin_loss_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_triplet_margin_loss_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_triplet_margin_loss_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_triplet_margin_loss_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_triplet_margin_loss_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_triplet_margin_loss_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_unfold_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_unfold_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_unfold_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_unfold_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_unfold_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_unfold_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_unfold_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_upsample_bilinear_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_upsample_bilinear_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_upsample_bilinear_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_upsample_bilinear_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_upsample_nearest_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_upsample_nearest_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_upsample_nearest_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_upsample_nearest_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nn_functional_upsample_nearest_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nonzero_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nonzero_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nonzero_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nonzero_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nonzero_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nonzero_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nonzero_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nonzero_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nonzero_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nonzero_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nonzero_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nonzero_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nonzero_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nonzero_static_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nonzero_static_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nonzero_static_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nonzero_static_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nonzero_static_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nonzero_static_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nonzero_static_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nonzero_static_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nonzero_static_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nonzero_static_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nonzero_static_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nonzero_static_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_nonzero_static_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_norm_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_norm_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_norm_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_norm_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_norm_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_norm_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_norm_fro_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_norm_fro_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_norm_fro_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_norm_fro_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_norm_fro_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_norm_fro_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_norm_inf_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_norm_inf_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_norm_inf_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_norm_inf_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_norm_inf_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_norm_inf_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_norm_nuc_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_norm_nuc_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_norm_nuc_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_norm_nuc_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_normal_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_normal_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_normal_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_normal_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_normal_in_place_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_normal_in_place_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_normal_in_place_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_normal_in_place_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_normal_in_place_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_normal_in_place_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_normal_number_mean_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_normal_number_mean_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_normal_number_mean_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_normal_number_mean_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ones_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ones_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ones_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ones_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ones_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ones_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ones_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ones_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ones_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ones_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ones_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ones_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ones_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ones_like_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ones_like_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ones_like_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ones_like_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ones_like_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ones_like_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ones_like_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ones_like_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ones_like_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ones_like_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ones_like_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ones_like_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ones_like_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ormqr_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ormqr_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ormqr_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_ormqr_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_outer_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_outer_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_outer_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_outer_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_outer_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_outer_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_outer_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_outer_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_outer_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_outer_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_outer_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_outer_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_pca_lowrank_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_pca_lowrank_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_pca_lowrank_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_pca_lowrank_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_permute_copy_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_permute_copy_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_permute_copy_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_permute_copy_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_permute_copy_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_permute_copy_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_permute_copy_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_permute_copy_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_permute_copy_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_permute_copy_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_permute_copy_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_permute_copy_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_permute_copy_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_permute_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_permute_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_permute_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_permute_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_permute_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_permute_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_permute_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_permute_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_permute_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_permute_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_permute_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_permute_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_permute_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_pinverse_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_pinverse_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_pinverse_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_pinverse_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polar_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polar_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_0_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_0_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_0_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_0_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_0_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_0_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_0_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_0_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_0_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_0_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_1_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_1_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_1_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_1_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_1_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_1_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_1_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_1_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_1_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_1_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_2_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_2_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_2_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_2_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_2_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_2_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_2_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_2_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_2_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_2_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_3_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_3_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_3_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_3_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_3_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_3_cuda_int16, 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test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_4_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_4_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_4_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_polygamma_polygamma_n_4_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_positive_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_positive_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_positive_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_positive_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_positive_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_positive_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_positive_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_positive_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_positive_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_positive_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_positive_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_positive_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_pow_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_pow_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_pow_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_pow_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_pow_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_pow_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_pow_cuda_float64, 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test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_put_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_put_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_put_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_put_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_put_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_put_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_qr_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_qr_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_qr_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_qr_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_quantile_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_quantile_cuda_float64, 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test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_randint_like_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_randint_like_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_randn_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_randn_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_randn_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_randn_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_randn_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_randn_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_randn_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_randn_like_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_randn_like_cuda_complex128, 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test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_remainder_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_remainder_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_remainder_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_remainder_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_remainder_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_remainder_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_renorm_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_renorm_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_renorm_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_renorm_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_renorm_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_renorm_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_repeat_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_repeat_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_repeat_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_repeat_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_repeat_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_repeat_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_repeat_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_repeat_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_repeat_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_repeat_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_repeat_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_repeat_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_repeat_interleave_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_repeat_interleave_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_repeat_interleave_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_repeat_interleave_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_repeat_interleave_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_repeat_interleave_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_repeat_interleave_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_repeat_interleave_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_repeat_interleave_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_repeat_interleave_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_repeat_interleave_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_repeat_interleave_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_repeat_interleave_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_reshape_as_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_reshape_as_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_reshape_as_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_reshape_as_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_reshape_as_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_reshape_as_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_reshape_as_cuda_float32, 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test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sgn_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sgn_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sgn_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sgn_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sgn_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sgn_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sgn_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sgn_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sgn_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sgn_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sgn_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_short_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_short_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_short_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_short_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_short_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_short_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_short_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_short_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_short_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_short_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_short_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_short_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sigmoid_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sigmoid_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sigmoid_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sigmoid_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sigmoid_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sigmoid_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sigmoid_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sigmoid_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sigmoid_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sigmoid_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sigmoid_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sigmoid_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sigmoid_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sign_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sign_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sign_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sign_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sign_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sign_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sign_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sign_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sign_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sign_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_signal_windows_bartlett_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_signal_windows_bartlett_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_signal_windows_blackman_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_signal_windows_blackman_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_signal_windows_cosine_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_signal_windows_cosine_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_signal_windows_exponential_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_signal_windows_exponential_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_signal_windows_gaussian_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_signal_windows_gaussian_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_signal_windows_general_cosine_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_signal_windows_general_cosine_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_signal_windows_general_hamming_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_signal_windows_general_hamming_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_signal_windows_hamming_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_signal_windows_hamming_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_signal_windows_hann_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_signal_windows_hann_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_signal_windows_kaiser_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_signal_windows_kaiser_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_signal_windows_nuttall_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_signal_windows_nuttall_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_signbit_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_signbit_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_signbit_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_signbit_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_signbit_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_signbit_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_signbit_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_signbit_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_signbit_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_signbit_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sin_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sin_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sin_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sin_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sin_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sin_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sin_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sin_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sin_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sin_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sin_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sin_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sin_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sinc_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sinc_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sinc_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sinc_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sinc_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sinc_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sinc_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sinc_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sinc_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sinc_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sinc_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sinc_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sinh_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sinh_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sinh_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sinh_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sinh_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sinh_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sinh_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sinh_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sinh_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sinh_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sinh_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sinh_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sinh_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_slice_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_slice_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_slice_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_slice_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_slice_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_slice_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_slice_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_slice_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_slice_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_slice_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_slice_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_slice_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_slice_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_slice_scatter_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_slice_scatter_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_slice_scatter_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_slice_scatter_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_slice_scatter_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_slice_scatter_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_slice_scatter_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_slice_scatter_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_slice_scatter_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_slice_scatter_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_softmax_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_softmax_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_softmax_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_softmax_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_softmax_with_dtype_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_softmax_with_dtype_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_softmax_with_dtype_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_softmax_with_dtype_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_softmax_with_dtype_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_softmax_with_dtype_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_softmax_with_dtype_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_softmax_with_dtype_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_softmax_with_dtype_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_softmax_with_dtype_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_softmax_with_dtype_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_softmax_with_dtype_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sort_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sort_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sort_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sort_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sort_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sort_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sort_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sort_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sort_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sort_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sparse_mm_reduce_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sparse_mm_reduce_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sparse_mm_reduce_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sparse_mm_reduce_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sparse_sampled_addmm_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sparse_sampled_addmm_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sparse_sampled_addmm_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sparse_sampled_addmm_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_airy_ai_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_airy_ai_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_airy_ai_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_airy_ai_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_airy_ai_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_airy_ai_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_airy_ai_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_airy_ai_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_bessel_j0_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_bessel_j0_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_bessel_j0_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_bessel_j0_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_bessel_j0_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_bessel_j0_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_bessel_j0_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_bessel_j0_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_bessel_j1_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_bessel_j1_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_bessel_j1_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_bessel_j1_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_bessel_j1_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_bessel_j1_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_bessel_j1_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_bessel_j1_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_bessel_y0_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_bessel_y0_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_bessel_y0_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_bessel_y0_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_bessel_y0_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_bessel_y0_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_bessel_y0_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_bessel_y0_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_bessel_y1_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_bessel_y1_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_bessel_y1_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_special_bessel_y1_cuda_int16, 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test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sum_to_size_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sum_to_size_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sum_to_size_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sum_to_size_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sum_to_size_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_sum_to_size_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_svd_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_svd_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_svd_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_svd_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_svd_lowrank_cuda_complex128, 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test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_unsafe_split_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_unsafe_split_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_unsafe_split_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_unsafe_split_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_unsqueeze_copy_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_unsqueeze_copy_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_unsqueeze_copy_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_unsqueeze_copy_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_unsqueeze_copy_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_unsqueeze_copy_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_unsqueeze_copy_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_unsqueeze_copy_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_unsqueeze_copy_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_unsqueeze_copy_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_unsqueeze_copy_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_unsqueeze_copy_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_unsqueeze_copy_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_unsqueeze_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_unsqueeze_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_unsqueeze_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_unsqueeze_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_unsqueeze_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_unsqueeze_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_unsqueeze_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_unsqueeze_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_unsqueeze_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_unsqueeze_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_unsqueeze_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_unsqueeze_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_unsqueeze_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_var_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_var_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_var_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_var_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_var_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_var_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_var_mean_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_var_mean_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_var_mean_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_var_mean_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_var_mean_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_var_mean_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_var_mean_unbiased_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_var_mean_unbiased_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_var_mean_unbiased_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_var_mean_unbiased_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_var_mean_unbiased_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_var_mean_unbiased_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_var_unbiased_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_var_unbiased_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_var_unbiased_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_var_unbiased_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_var_unbiased_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_var_unbiased_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_vdot_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_vdot_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_vdot_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_vdot_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_vdot_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_vdot_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_as_complex_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_as_complex_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_as_complex_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_as_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_as_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_as_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_as_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_as_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_as_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_as_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_as_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_as_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_as_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_as_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_as_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_as_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_as_real_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_as_real_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_copy_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_copy_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_copy_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_copy_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_copy_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_copy_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_copy_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_copy_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_copy_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_copy_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_copy_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_copy_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_view_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_vsplit_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_vsplit_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_vsplit_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_vsplit_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_vsplit_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_vsplit_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_vsplit_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_vsplit_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_vsplit_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_vsplit_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_vsplit_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_vsplit_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_vsplit_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_vstack_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_vstack_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_vstack_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_vstack_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_vstack_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_vstack_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_vstack_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_vstack_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_vstack_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_vstack_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_vstack_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_vstack_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_vstack_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_where_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_where_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_where_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_where_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_where_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_where_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_where_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_where_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_where_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_where_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_where_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_where_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_where_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_xlogy_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_xlogy_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_xlogy_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_xlogy_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_xlogy_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_xlogy_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_xlogy_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_xlogy_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_xlogy_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_xlogy_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zero__cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zero__cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zero__cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zero__cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zero__cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zero__cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zero__cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zero__cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zero__cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zero__cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zero__cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zero__cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zeros_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zeros_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zeros_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zeros_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zeros_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zeros_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zeros_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zeros_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zeros_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zeros_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zeros_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zeros_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zeros_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zeros_like_cuda_bfloat16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zeros_like_cuda_bool, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zeros_like_cuda_complex128, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zeros_like_cuda_complex32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zeros_like_cuda_complex64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zeros_like_cuda_float16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zeros_like_cuda_float32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zeros_like_cuda_float64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zeros_like_cuda_int16, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zeros_like_cuda_int32, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zeros_like_cuda_int64, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zeros_like_cuda_int8, test/test_utils.py::TestDeviceUtilsCUDA::test_device_mode_ops_zeros_like_cuda_uint8, test/test_utils.py::TestDeviceUtilsCUDA::test_get_default_device_cuda, test/test_utils.py::TestDeviceUtilsCUDA::test_get_default_device_more_cuda, test/test_utils.py::TestDeviceUtilsCUDA::test_nn_module_cuda, test/test_utils.py::TestDeviceUtilsCUDA::test_set_default_device_cuda, 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 2025-07-17T10:29:05.7922615Z 2025-07-17T10:29:05.7922847Z Running inductor/test_cpu_cpp_wrapper 1/1 ... [2025-07-17 10:29:05.297321] 2025-07-17T10:29:05.7923173Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:29:05.7923863Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_cpu_cpp_wrapper.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 10:29:05.297630] 2025-07-17T10:29:13.0774680Z 2025-07-17T10:29:13.0775704Z inductor/test_cpu_cpp_wrapper 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cpu_cpp_wrapper_1.1_b3d534614007d711_.log 2025-07-17T10:29:13.0776190Z 2025-07-17T10:29:13.0776846Z Running inductor/test_autoheuristic 1/1 ... [2025-07-17 10:29:13.077475] 2025-07-17T10:29:13.0777151Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:29:13.0778970Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_autoheuristic.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 10:29:13.077757] 2025-07-17T10:29:20.4249567Z 2025-07-17T10:29:20.4250495Z inductor/test_autoheuristic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_autoheuristic_1.1_74efd91b3d6e8752_.log 2025-07-17T10:29:20.4251053Z Running 0 items in this shard: 2025-07-17T10:29:20.4251193Z 2025-07-17T10:29:20.4252004Z Running inductor/test_minifier_isolate 1/1 ... [2025-07-17 10:29:20.425014] 2025-07-17T10:29:20.4252323Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:29:20.4254211Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_minifier_isolate.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 10:29:20.425287] 2025-07-17T10:29:27.9620685Z 2025-07-17T10:29:27.9621694Z inductor/test_minifier_isolate 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_minifier_isolate_1.1_558d1ab0c118282f_.log 2025-07-17T10:29:27.9622252Z 2025-07-17T10:29:27.9622443Z Running optim/test_optim 1/1 ... [2025-07-17 10:29:27.962089] 2025-07-17T10:29:27.9622750Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:29:27.9625662Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'optim/test_optim.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 10:29:27.962373] 2025-07-17T10:29:30.9032182Z 2025-07-17T10:29:30.9033179Z optim/test_optim 1/1 was successful, full logs can be found in artifacts with path test/test-reports/optim.test_optim_1.1_d5f5954fec35dad9_.log 2025-07-17T10:29:30.9033616Z 2025-07-17T10:29:30.9033783Z Running test_import_stats 1/1 ... [2025-07-17 10:29:30.903221] 2025-07-17T10:29:30.9034061Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:29:30.9036361Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_import_stats.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 10:29:30.903510] 2025-07-17T10:29:34.0747458Z 2025-07-17T10:29:34.0748433Z test_import_stats 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_import_stats_1.1_5c1dc41a33390465_.log 2025-07-17T10:29:34.0749034Z Running 0 items in this shard: 2025-07-17T10:29:34.0749176Z 2025-07-17T10:29:34.0749303Z Running test_indexing 1/1 ... [2025-07-17 10:29:34.074732] 2025-07-17T10:29:34.0749587Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:29:34.0751403Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', '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 10:29:34.075012] 2025-07-17T10:29:38.2983205Z 2025-07-17T10:29:38.2984154Z test_indexing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_indexing_1.1_499972c3790a25f4_.log 2025-07-17T10:29:38.2984899Z Running 1 items in this shard: test/test_indexing.py::TestIndexingCUDA::test_index_put_accumulate_large_tensor_cuda 2025-07-17T10:29:38.2985209Z 2025-07-17T10:29:38.2985454Z Running test_jit_autocast 1/1 ... [2025-07-17 10:29:38.298295] 2025-07-17T10:29:38.2985730Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:29:38.2988123Z Executing ['/opt/conda/envs/py_3.12/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 10:29:38.298589] 2025-07-17T10:29:43.3734150Z 2025-07-17T10:29:43.3735026Z test_jit_autocast 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_autocast_1.1_f0d6febde811d0b9_.log 2025-07-17T10:29:43.3735542Z Running 0 items in this shard: 2025-07-17T10:29:43.3735735Z 2025-07-17T10:29:43.3735961Z Running test_jiterator 1/1 ... [2025-07-17 10:29:43.373498] 2025-07-17T10:29:43.3738628Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:29:43.3739746Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_jiterator.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 10:29:43.373778] 2025-07-17T10:29:47.0960784Z 2025-07-17T10:29:47.0961709Z test_jiterator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jiterator_1.1_5ad9bc6c800150e2_.log 2025-07-17T10:29:47.0962194Z Running 0 items in this shard: 2025-07-17T10:29:47.0962391Z 2025-07-17T10:29:47.0963231Z Running test_legacy_vmap 1/1 ... [2025-07-17 10:29:47.096105] 2025-07-17T10:29:47.0963520Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:29:47.0965659Z Executing ['/opt/conda/envs/py_3.12/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 10:29:47.096405] 2025-07-17T10:29:50.8187476Z 2025-07-17T10:29:50.8188731Z test_legacy_vmap 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_legacy_vmap_1.1_ae6a80aa9ae29c5d_.log 2025-07-17T10:29:50.8189368Z Running 0 items in this shard: 2025-07-17T10:29:50.8189544Z 2025-07-17T10:29:50.8191816Z Running test_license 1/1 ... [2025-07-17 10:29:50.818818] 2025-07-17T10:29:50.8192215Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:29:50.8193221Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_license.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 10:29:50.819132] 2025-07-17T10:29:53.8898406Z 2025-07-17T10:29:53.8899555Z test_license 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_license_1.1_c3d5781792123c9a_.log 2025-07-17T10:29:53.8900331Z Running 0 items in this shard: 2025-07-17T10:29:53.8900567Z 2025-07-17T10:29:53.8900755Z Running test_logging 1/1 ... [2025-07-17 10:29:53.889835] 2025-07-17T10:29:53.8901236Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:29:53.8902748Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', '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 10:29:53.890119] 2025-07-17T10:29:56.9609644Z 2025-07-17T10:29:56.9610763Z test_logging 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_logging_1.1_4dcbb89d42d62b86_.log 2025-07-17T10:29:56.9611245Z Running 0 items in this shard: 2025-07-17T10:29:56.9611385Z 2025-07-17T10:29:56.9611564Z Running test_masked 1/1 ... [2025-07-17 10:29:56.960971] 2025-07-17T10:29:56.9611869Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:29:56.9614204Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_masked.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 10:29:56.961249] 2025-07-17T10:30:01.3349267Z 2025-07-17T10:30:01.3350177Z test_masked 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_masked_1.1_448b9eddd72631ce_.log 2025-07-17T10:30:01.3350642Z Running 0 items in this shard: 2025-07-17T10:30:01.3350800Z 2025-07-17T10:30:01.3350928Z Running test_maskedtensor 1/1 ... [2025-07-17 10:30:01.334871] 2025-07-17T10:30:01.3351202Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:30:01.3352947Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_maskedtensor.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 10:30:01.335152] 2025-07-17T10:30:05.8588217Z 2025-07-17T10:30:05.8589770Z test_maskedtensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_maskedtensor_1.1_48dcc2a87061d69e_.log 2025-07-17T10:30:05.8590292Z Running 0 items in this shard: 2025-07-17T10:30:05.8590443Z 2025-07-17T10:30:05.8590567Z Running test_matmul_cuda 1/1 ... [2025-07-17 10:30:05.858809] 2025-07-17T10:30:05.8590835Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:30:05.8592533Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_matmul_cuda.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 10:30:05.859095] 2025-07-17T10:30:09.6317132Z 2025-07-17T10:30:09.6318105Z test_matmul_cuda 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_matmul_cuda_1.1_e4610e7dcf669c35_.log 2025-07-17T10:30:09.6318648Z Running 0 items in this shard: 2025-07-17T10:30:09.6318787Z 2025-07-17T10:30:09.6318912Z Running test_monitor 1/1 ... [2025-07-17 10:30:09.631697] 2025-07-17T10:30:09.6319183Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:30:09.6321461Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_monitor.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 10:30:09.631977] 2025-07-17T10:30:12.8029217Z 2025-07-17T10:30:12.8030251Z test_monitor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_monitor_1.1_77daac9642fdf4e6_.log 2025-07-17T10:30:12.8030728Z Running 0 items in this shard: 2025-07-17T10:30:12.8030862Z 2025-07-17T10:30:12.8031221Z Running test_namedtensor 1/1 ... [2025-07-17 10:30:12.802982] 2025-07-17T10:30:12.8032181Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:30:12.8034117Z Executing ['/opt/conda/envs/py_3.12/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 10:30:12.803274] 2025-07-17T10:30:16.7260456Z 2025-07-17T10:30:16.7261491Z test_namedtensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_namedtensor_1.1_d5ba1caaab7cb2a1_.log 2025-07-17T10:30:16.7261983Z Running 0 items in this shard: 2025-07-17T10:30:16.7262114Z 2025-07-17T10:30:16.7262944Z Running test_native_functions 1/1 ... [2025-07-17 10:30:16.726027] 2025-07-17T10:30:16.7263223Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:30:16.7264272Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_native_functions.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 10:30:16.726305] 2025-07-17T10:30:19.7971020Z 2025-07-17T10:30:19.7972362Z test_native_functions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_native_functions_1.1_cb70627ca1900352_.log 2025-07-17T10:30:19.7973217Z Running 0 items in this shard: 2025-07-17T10:30:19.7974087Z 2025-07-17T10:30:19.7974319Z Running test_numba_integration 1/1 ... [2025-07-17 10:30:19.797162] 2025-07-17T10:30:19.7974774Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:30:19.7976351Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_numba_integration.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 10:30:19.797455] 2025-07-17T10:30:23.2691792Z 2025-07-17T10:30:23.2692872Z test_numba_integration 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_numba_integration_1.1_f8b8f3085f91ca33_.log 2025-07-17T10:30:23.2693410Z Running 0 items in this shard: 2025-07-17T10:30:23.2693536Z 2025-07-17T10:30:23.2693659Z Running test_numpy_interop 1/1 ... [2025-07-17 10:30:23.269146] 2025-07-17T10:30:23.2694565Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:30:23.2695666Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_numpy_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 10:30:23.269434] 2025-07-17T10:30:27.0421094Z 2025-07-17T10:30:27.0422091Z test_numpy_interop 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_numpy_interop_1.1_797ff781a1cf6f4c_.log 2025-07-17T10:30:27.0422601Z Running 0 items in this shard: 2025-07-17T10:30:27.0422784Z 2025-07-17T10:30:27.0422898Z Running test_openmp 1/1 ... [2025-07-17 10:30:27.042099] 2025-07-17T10:30:27.0423174Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:30:27.0425784Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_openmp.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 10:30:27.042384] 2025-07-17T10:30:30.1135362Z 2025-07-17T10:30:30.1136305Z test_openmp 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_openmp_1.1_bf15e0727ab42357_.log 2025-07-17T10:30:30.1136777Z Running 0 items in this shard: 2025-07-17T10:30:30.1136928Z 2025-07-17T10:30:30.1137121Z Running test_ops_fwd_gradients 1/1 ... [2025-07-17 10:30:30.113544] 2025-07-17T10:30:30.1137430Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:30:30.1140080Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_ops_fwd_gradients.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 10:30:30.113830] 2025-07-17T10:30:35.7897218Z 2025-07-17T10:30:35.7898329Z test_ops_fwd_gradients 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_fwd_gradients_1.1_889e2effedb27365_.log 2025-07-17T10:30:35.7898865Z Running 0 items in this shard: 2025-07-17T10:30:35.7899005Z 2025-07-17T10:30:35.7899141Z Running xpu/test_gemm 1/1 ... [2025-07-17 10:30:35.789713] 2025-07-17T10:30:35.7899404Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:30:35.7901359Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'xpu/test_gemm.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 10:30:35.790002] 2025-07-17T10:30:39.5421470Z 2025-07-17T10:30:39.5422432Z xpu/test_gemm 1/1 was successful, full logs can be found in artifacts with path test/test-reports/xpu.test_gemm_1.1_a244250ceb424d65_.log 2025-07-17T10:30:39.5422903Z Running 0 items in this shard: 2025-07-17T10:30:39.5423030Z 2025-07-17T10:30:42.2518796Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T10:30:42.2519737Z import pkg_resources 2025-07-17T10:30:42.2996893Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T10:30:42.2997872Z import pkg_resources 2025-07-17T10:30:42.6353809Z Running inductor/test_cpu_cpp_wrapper 1/1 ... [2025-07-17 10:30:42.634903] 2025-07-17T10:30:42.6354187Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:30:42.6355642Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_cpu_cpp_wrapper.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 10:30:42.635344] 2025-07-17T10:30:42.6547101Z Running inductor/test_autoheuristic 1/1 ... [2025-07-17 10:30:42.654427] 2025-07-17T10:30:42.6547445Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:30:42.6549840Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_autoheuristic.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 10:30:42.654814] 2025-07-17T10:30:50.0307807Z 2025-07-17T10:30:50.0308829Z inductor/test_autoheuristic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_autoheuristic_1.1_97b73e531b78f993_.log 2025-07-17T10:30:50.0309380Z Running 0 items in this shard: 2025-07-17T10:30:50.0309517Z 2025-07-17T10:30:50.0309729Z Running inductor/test_minifier_isolate 1/1 ... [2025-07-17 10:30:50.030684] 2025-07-17T10:30:50.0310043Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:30:50.0311335Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'inductor/test_minifier_isolate.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 10:30:50.030985] 2025-07-17T10:30:52.7223016Z 2025-07-17T10:30:52.7224050Z inductor/test_cpu_cpp_wrapper 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cpu_cpp_wrapper_1.1_080f411040af57e2_.log 2025-07-17T10:30:52.7225134Z 2025-07-17T10:30:52.7225733Z Running optim/test_optim 1/1 ... [2025-07-17 10:30:52.722444] 2025-07-17T10:30:52.7226020Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:30:52.7229414Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'optim/test_optim.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 10:30:52.722781] 2025-07-17T10:30:56.2339876Z 2025-07-17T10:30:56.2340914Z optim/test_optim 1/1 was successful, full logs can be found in artifacts with path test/test-reports/optim.test_optim_1.1_925d5ad89b9130a8_.log 2025-07-17T10:30:56.2341935Z 2025-07-17T10:30:56.2343075Z Running test_import_stats 1/1 ... [2025-07-17 10:30:56.233863] 2025-07-17T10:30:56.2343396Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:30:56.2344084Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_import_stats.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 10:30:56.234195] 2025-07-17T10:30:57.3310665Z 2025-07-17T10:30:57.3312415Z inductor/test_minifier_isolate 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_minifier_isolate_1.1_f9232bf138d664fd_.log 2025-07-17T10:30:57.3312921Z 2025-07-17T10:30:57.3313050Z Running test_indexing 1/1 ... [2025-07-17 10:30:57.330937] 2025-07-17T10:30:57.3313311Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:30:57.3313978Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', '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 10:30:57.331226] 2025-07-17T10:31:02.9616584Z 2025-07-17T10:31:02.9617529Z test_import_stats 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_import_stats_1.1_ecbd1f0ebc9ba3b0_.log 2025-07-17T10:31:02.9619003Z Running 2 items in this shard: test/test_import_stats.py::TestImportTime::test_time_cuda_device_count, test/test_import_stats.py::TestImportTime::test_time_import_torch 2025-07-17T10:31:02.9619449Z 2025-07-17T10:31:02.9619651Z GITHUB_RUN_ID, GITHUB_RUN_ATTEMPT, or ARTIFACTS_FILE_SUFFIX not set, not uploading 2025-07-17T10:31:02.9619998Z Running test_jit_autocast 1/1 ... [2025-07-17 10:31:02.961568] 2025-07-17T10:31:02.9620273Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:31:02.9620502Z Uploading artifacts took 0.00 seconds 2025-07-17T10:31:02.9621164Z Executing ['/opt/conda/envs/py_3.12/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 10:31:02.961901] 2025-07-17T10:31:12.7734743Z 2025-07-17T10:31:12.7736609Z test_indexing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_indexing_1.1_427a87192db66da3_.log 2025-07-17T10:31:12.7755004Z Running 93 items in this shard: test/test_indexing.py::TestIndexingCUDA::test_advancedindex_big_cuda, test/test_indexing.py::TestIndexingCUDA::test_advancedindex_cuda_float16, test/test_indexing.py::TestIndexingCUDA::test_advancedindex_cuda_float64, test/test_indexing.py::TestIndexingCUDA::test_basic_advanced_combined_cuda, test/test_indexing.py::TestIndexingCUDA::test_bool_indices_accumulate_cuda, test/test_indexing.py::TestIndexingCUDA::test_bool_indices_cuda, test/test_indexing.py::TestIndexingCUDA::test_bool_mask_assignment_cuda, test/test_indexing.py::TestIndexingCUDA::test_byte_mask2d_cuda, test/test_indexing.py::TestIndexingCUDA::test_byte_mask_accumulate_cuda, test/test_indexing.py::TestIndexingCUDA::test_byte_mask_cuda, test/test_indexing.py::TestIndexingCUDA::test_byte_tensor_assignment_cuda, test/test_indexing.py::TestIndexingCUDA::test_cpu_indices_cuda, test/test_indexing.py::TestIndexingCUDA::test_cuda_broadcast_index_use_deterministic_algorithms_cuda, test/test_indexing.py::TestIndexingCUDA::test_ellipsis_tensor_cuda, test/test_indexing.py::TestIndexingCUDA::test_empty_index_cuda, test/test_indexing.py::TestIndexingCUDA::test_empty_ndim_index_bool_cuda, test/test_indexing.py::TestIndexingCUDA::test_empty_ndim_index_cuda, test/test_indexing.py::TestIndexingCUDA::test_empty_slice_cuda, test/test_indexing.py::TestIndexingCUDA::test_gather_take_along_dim_cross_device_cuda_float32, test/test_indexing.py::TestIndexingCUDA::test_getitem_scalars_cuda, test/test_indexing.py::TestIndexingCUDA::test_index_cuda, test/test_indexing.py::TestIndexingCUDA::test_index_getitem_copy_bools_slices_cuda, test/test_indexing.py::TestIndexingCUDA::test_index_ind_dtype_cuda, test/test_indexing.py::TestIndexingCUDA::test_index_limits_cuda, test/test_indexing.py::TestIndexingCUDA::test_index_put_accumulate_duplicate_indices_cuda, test/test_indexing.py::TestIndexingCUDA::test_index_put_accumulate_empty_cuda, test/test_indexing.py::TestIndexingCUDA::test_index_put_accumulate_expanded_values_cuda, test/test_indexing.py::TestIndexingCUDA::test_index_put_accumulate_non_contiguous_cuda, test/test_indexing.py::TestIndexingCUDA::test_index_put_deterministic_with_optional_tensors_cuda, test/test_indexing.py::TestIndexingCUDA::test_index_put_large_indices_cuda, test/test_indexing.py::TestIndexingCUDA::test_index_put_src_datatype_cuda_bfloat16, test/test_indexing.py::TestIndexingCUDA::test_index_put_src_datatype_cuda_bool, test/test_indexing.py::TestIndexingCUDA::test_index_put_src_datatype_cuda_complex128, test/test_indexing.py::TestIndexingCUDA::test_index_put_src_datatype_cuda_complex64, test/test_indexing.py::TestIndexingCUDA::test_index_put_src_datatype_cuda_float16, test/test_indexing.py::TestIndexingCUDA::test_index_put_src_datatype_cuda_float8_e4m3fn, test/test_indexing.py::TestIndexingCUDA::test_index_put_src_datatype_cuda_float8_e5m2, test/test_indexing.py::TestIndexingCUDA::test_index_put_src_datatype_cuda_int64, test/test_indexing.py::TestIndexingCUDA::test_index_scalar_with_bool_mask_cuda, test/test_indexing.py::TestIndexingCUDA::test_index_setitem_bools_slices_cuda, test/test_indexing.py::TestIndexingCUDA::test_index_src_datatype_cuda_bfloat16, test/test_indexing.py::TestIndexingCUDA::test_index_src_datatype_cuda_bool, test/test_indexing.py::TestIndexingCUDA::test_index_src_datatype_cuda_float16, test/test_indexing.py::TestIndexingCUDA::test_index_src_datatype_cuda_int64, test/test_indexing.py::TestIndexingCUDA::test_int_assignment_cuda, test/test_indexing.py::TestIndexingCUDA::test_int_indices2d_cuda, test/test_indexing.py::TestIndexingCUDA::test_int_indices_broadcast_cuda, test/test_indexing.py::TestIndexingCUDA::test_int_indices_cuda, test/test_indexing.py::TestIndexingCUDA::test_invalid_device_cuda, test/test_indexing.py::TestIndexingCUDA::test_invalid_index_cuda, test/test_indexing.py::TestIndexingCUDA::test_jit_indexing_cuda, test/test_indexing.py::TestIndexingCUDA::test_list_indices_cuda, test/test_indexing.py::TestIndexingCUDA::test_multi_dimensional_bool_mask_assignment_cuda, test/test_indexing.py::TestIndexingCUDA::test_multi_dimensional_bool_mask_cuda, test/test_indexing.py::TestIndexingCUDA::test_multiple_bool_indices_cuda, test/test_indexing.py::TestIndexingCUDA::test_multiple_byte_mask_cuda, test/test_indexing.py::TestIndexingCUDA::test_multiple_int_cuda, test/test_indexing.py::TestIndexingCUDA::test_none_cuda, test/test_indexing.py::TestIndexingCUDA::test_out_of_bound_index_cuda, test/test_indexing.py::TestIndexingCUDA::test_set_item_to_scalar_tensor_cuda, test/test_indexing.py::TestIndexingCUDA::test_setitem_expansion_error_cuda, test/test_indexing.py::TestIndexingCUDA::test_setitem_scalars_cuda, test/test_indexing.py::TestIndexingCUDA::test_single_int_cuda, test/test_indexing.py::TestIndexingCUDA::test_step_assignment_cuda, test/test_indexing.py::TestIndexingCUDA::test_step_cuda, test/test_indexing.py::TestIndexingCUDA::test_take_along_dim_cuda_float32, test/test_indexing.py::TestIndexingCUDA::test_take_along_dim_cuda_int64, test/test_indexing.py::TestIndexingCUDA::test_take_along_dim_invalid_cuda_float32, test/test_indexing.py::TestIndexingCUDA::test_take_along_dim_invalid_cuda_int64, test/test_indexing.py::TestIndexingCUDA::test_unravel_index_errors_cuda, test/test_indexing.py::TestIndexingCUDA::test_variable_slicing_cuda, test/test_indexing.py::TestIndexingCUDA::test_zero_dim_index_cuda, test/test_indexing.py::NumpyTestsCUDA::test_boolean_assignment_value_mismatch_cuda, test/test_indexing.py::NumpyTestsCUDA::test_boolean_indexing_alldims_cuda, test/test_indexing.py::NumpyTestsCUDA::test_boolean_indexing_onedim_cuda, test/test_indexing.py::NumpyTestsCUDA::test_boolean_indexing_twodim_cuda, test/test_indexing.py::NumpyTestsCUDA::test_boolean_indexing_weirdness_cuda, test/test_indexing.py::NumpyTestsCUDA::test_boolean_indexing_weirdness_tensors_cuda, test/test_indexing.py::NumpyTestsCUDA::test_boolean_list_indexing_cuda, test/test_indexing.py::NumpyTestsCUDA::test_boolean_shape_mismatch_cuda, test/test_indexing.py::NumpyTestsCUDA::test_broadcast_subspace_cuda, test/test_indexing.py::NumpyTestsCUDA::test_broaderrors_indexing_cuda, test/test_indexing.py::NumpyTestsCUDA::test_ellipsis_index_cuda, test/test_indexing.py::NumpyTestsCUDA::test_empty_fancy_index_cuda, test/test_indexing.py::NumpyTestsCUDA::test_empty_tuple_index_cuda, test/test_indexing.py::NumpyTestsCUDA::test_everything_returns_views_cuda, test/test_indexing.py::NumpyTestsCUDA::test_index_is_larger_cuda, test/test_indexing.py::NumpyTestsCUDA::test_index_no_floats_cuda, test/test_indexing.py::NumpyTestsCUDA::test_none_index_cuda, test/test_indexing.py::NumpyTestsCUDA::test_single_bool_index_cuda, test/test_indexing.py::NumpyTestsCUDA::test_single_int_index_cuda, test/test_indexing.py::NumpyTestsCUDA::test_trivial_fancy_out_of_bounds_cuda, test/test_indexing.py::NumpyTestsCUDA::test_truncate_leading_1s_cuda 2025-07-17T10:31:12.7770783Z 2025-07-17T10:31:12.7770906Z Running test_jiterator 1/1 ... [2025-07-17 10:31:12.773442] 2025-07-17T10:31:12.7771183Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:31:12.7771914Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_jiterator.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 10:31:12.773758] 2025-07-17T10:31:25.9204708Z 2025-07-17T10:31:25.9205643Z test_jit_autocast 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_autocast_1.1_de9ebf2243333f7f_.log 2025-07-17T10:31:25.9217065Z 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 2025-07-17T10:31:25.9225969Z 2025-07-17T10:31:25.9226100Z Running test_legacy_vmap 1/1 ... [2025-07-17 10:31:25.920442] 2025-07-17T10:31:25.9226410Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:31:25.9227145Z Executing ['/opt/conda/envs/py_3.12/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 10:31:25.920845] 2025-07-17T10:31:31.4733228Z 2025-07-17T10:31:31.4734168Z test_jiterator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jiterator_1.1_7461335d58fc542d_.log 2025-07-17T10:31:31.4807979Z Running 289 items in this shard: test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_contiguous_shape_strides0_cuda_bfloat16_bfloat16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_contiguous_shape_strides0_cuda_bfloat16_complex128, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_contiguous_shape_strides0_cuda_bfloat16_complex64, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_contiguous_shape_strides0_cuda_bfloat16_float16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_contiguous_shape_strides0_cuda_bfloat16_float32, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_contiguous_shape_strides0_cuda_bfloat16_float64, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_contiguous_shape_strides0_cuda_bfloat16_int16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_contiguous_shape_strides0_cuda_bfloat16_int32, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_contiguous_shape_strides0_cuda_bfloat16_int64, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_contiguous_shape_strides0_cuda_bfloat16_int8, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_contiguous_shape_strides0_cuda_bfloat16_uint8, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_contiguous_shape_strides0_cuda_complex128_bfloat16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_contiguous_shape_strides0_cuda_complex128_complex128, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_contiguous_shape_strides0_cuda_complex128_complex64, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_contiguous_shape_strides0_cuda_complex128_float16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_contiguous_shape_strides0_cuda_complex128_float32, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_contiguous_shape_strides0_cuda_complex128_float64, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_contiguous_shape_strides0_cuda_complex128_int16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_contiguous_shape_strides0_cuda_complex128_int32, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_contiguous_shape_strides0_cuda_complex128_int64, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_contiguous_shape_strides0_cuda_complex128_int8, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_contiguous_shape_strides0_cuda_complex128_uint8, 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test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int16_uint8, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int32_bfloat16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int32_complex128, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int32_complex64, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int32_float16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int32_float32, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int32_float64, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int32_int16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int32_int32, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int32_int64, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int32_int8, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int32_uint8, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int64_bfloat16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int64_complex128, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int64_complex64, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int64_float16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int64_float32, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int64_float64, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int64_int16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int64_int32, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int64_int64, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int64_int8, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int64_uint8, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int8_bfloat16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int8_complex128, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int8_complex64, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int8_float16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int8_float32, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int8_float64, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int8_int16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int8_int32, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int8_int64, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int8_int8, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_int8_uint8, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_uint8_bfloat16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_uint8_complex128, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_uint8_complex64, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_uint8_float16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_uint8_float32, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_uint8_float64, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_uint8_int16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_uint8_int32, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_uint8_int64, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_uint8_int8, test/test_jiterator.py::TestPythonJiteratorCUDA::test_all_dtype_noncontiguous_shape_strides0_cuda_uint8_uint8, test/test_jiterator.py::TestPythonJiteratorCUDA::test_bool_extra_args_is_train_False_cuda, test/test_jiterator.py::TestPythonJiteratorCUDA::test_bool_extra_args_is_train_True_cuda, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha2_beta2_cuda_bfloat16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha2_beta2_cuda_float16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha2_beta2_cuda_float32, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha2_beta2_cuda_float64, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha2_beta_-4_2_cuda_bfloat16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha2_beta_-4_2_cuda_float16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha2_beta_-4_2_cuda_float32, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha2_beta_-4_2_cuda_float64, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha2_beta_3_cuda_bfloat16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha2_beta_3_cuda_float16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha2_beta_3_cuda_float32, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha2_beta_3_cuda_float64, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha_-1_beta2_cuda_bfloat16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha_-1_beta2_cuda_float16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha_-1_beta2_cuda_float32, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha_-1_beta2_cuda_float64, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha_-1_beta_-4_2_cuda_bfloat16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha_-1_beta_-4_2_cuda_float16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha_-1_beta_-4_2_cuda_float32, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha_-1_beta_-4_2_cuda_float64, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha_-1_beta_3_cuda_bfloat16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha_-1_beta_3_cuda_float16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha_-1_beta_3_cuda_float32, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha_-1_beta_3_cuda_float64, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha_2_0_beta2_cuda_bfloat16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha_2_0_beta2_cuda_float16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha_2_0_beta2_cuda_float32, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha_2_0_beta2_cuda_float64, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha_2_0_beta_-4_2_cuda_bfloat16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha_2_0_beta_-4_2_cuda_float16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha_2_0_beta_-4_2_cuda_float32, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha_2_0_beta_-4_2_cuda_float64, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha_2_0_beta_3_cuda_bfloat16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha_2_0_beta_3_cuda_float16, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha_2_0_beta_3_cuda_float32, test/test_jiterator.py::TestPythonJiteratorCUDA::test_extra_args_alpha_2_0_beta_3_cuda_float64, test/test_jiterator.py::TestPythonJiteratorCUDA::test_invalid_function_name_code_string_template T my _kernel(T x) { return x; }_cuda, test/test_jiterator.py::TestPythonJiteratorCUDA::test_invalid_function_name_code_string_template Tmy_kernel(T x) { return x; }_cuda, test/test_jiterator.py::TestPythonJiteratorCUDA::test_multiple_functors_cuda, test/test_jiterator.py::TestPythonJiteratorCUDA::test_various_num_inputs_num_inputs_1_cuda, test/test_jiterator.py::TestPythonJiteratorCUDA::test_various_num_inputs_num_inputs_5_cuda, test/test_jiterator.py::TestPythonJiteratorCUDA::test_various_num_inputs_num_inputs_8_cuda, test/test_jiterator.py::TestPythonJiteratorCUDA::test_various_num_outputs_num_outputs_1_cuda, test/test_jiterator.py::TestPythonJiteratorCUDA::test_various_num_outputs_num_outputs_4_cuda, test/test_jiterator.py::TestPythonJiteratorCUDA::test_various_num_outputs_num_outputs_8_cuda 2025-07-17T10:31:31.4880627Z 2025-07-17T10:31:31.4880759Z Running test_license 1/1 ... [2025-07-17 10:31:31.473574] 2025-07-17T10:31:31.4881190Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:31:31.4881851Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_license.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 10:31:31.473889] 2025-07-17T10:31:33.0989197Z 2025-07-17T10:31:33.0990204Z test_legacy_vmap 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_legacy_vmap_1.1_ed9443b6bb9edefa_.log 2025-07-17T10:31:33.1011993Z 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::TestVmapBatchedGradientLegacyCUDA::test_add_cuda, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCUDA::test_binary_cross_entropy_cuda, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCUDA::test_diagonal_cuda, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCUDA::test_div_cuda, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCUDA::test_expand_cuda, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCUDA::test_index_cuda, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCUDA::test_inplace_manyview_cuda, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCUDA::test_inplace_on_view_cuda, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCUDA::test_lgamma_cuda, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCUDA::test_log1p_cuda, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCUDA::test_log_cuda, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCUDA::test_logsumexp_cuda, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCUDA::test_max_cuda, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCUDA::test_median_cuda, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCUDA::test_min_cuda, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCUDA::test_mul_cuda, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCUDA::test_permute_cuda, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCUDA::test_reshape_cuda, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCUDA::test_select_cuda, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCUDA::test_sigmoid_cuda, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCUDA::test_slice_cuda, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCUDA::test_stack_cuda, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCUDA::test_sub_cuda, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCUDA::test_threshold_cuda, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCUDA::test_trace_cuda, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCUDA::test_unrelated_output_cuda, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCUDA::test_unrelated_output_multiple_grad_cuda, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCUDA::test_vmap_fallback_check, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCUDA::test_vmap_fallback_check_ok 2025-07-17T10:31:33.1033364Z 2025-07-17T10:31:33.1033482Z Running test_logging 1/1 ... [2025-07-17 10:31:33.098881] 2025-07-17T10:31:33.1033739Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:31:33.1034487Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', '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 10:31:33.099218] 2025-07-17T10:31:35.6964060Z 2025-07-17T10:31:35.6965017Z test_license 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_license_1.1_ef878bbe69b84411_.log 2025-07-17T10:31:35.6965769Z Running 2 items in this shard: test/test_license.py::TestLicense::test_distinfo_license, test/test_license.py::TestLicense::test_license_for_wheel 2025-07-17T10:31:35.6966732Z 2025-07-17T10:31:35.6966846Z Running test_masked 1/1 ... [2025-07-17 10:31:35.696252] 2025-07-17T10:31:35.6967110Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:31:35.6967749Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_masked.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 10:31:35.696555] 2025-07-17T10:31:39.0756453Z 2025-07-17T10:31:39.0757440Z test_logging 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_logging_1.1_255348d57cc523e4_.log 2025-07-17T10:31:39.0758596Z Running 1 items in this shard: test/test_logging.py::LoggingTest::testApiUsage 2025-07-17T10:31:39.0758828Z 2025-07-17T10:31:39.0758965Z Running test_maskedtensor 1/1 ... [2025-07-17 10:31:39.075523] 2025-07-17T10:31:39.0759239Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:31:39.0759910Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_maskedtensor.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 10:31:39.075828] 2025-07-17T10:31:56.6724698Z 2025-07-17T10:31:56.6725671Z test_maskedtensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_maskedtensor_1.1_f7fae3e4674eb7bb_.log 2025-07-17T10:31:56.6907184Z Running 956 items in this shard: test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn0, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn1, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn10, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn11, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn12, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn13, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn14, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn15, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn16, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn17, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn18, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn19, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn2, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn20, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn21, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn22, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn23, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn24, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn25, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn26, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn27, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn28, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn29, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn3, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn30, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn31, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn32, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn33, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn34, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn35, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn36, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn37, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn38, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn39, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn4, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn40, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn41, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn42, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn43, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn44, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn45, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn46, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn47, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn48, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn49, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn5, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn50, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn51, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn52, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn53, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn54, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn55, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn56, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn57, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn6, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn7, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn8, test/test_maskedtensor.py::TestUnary::test_inplace_unary_fn9, test/test_maskedtensor.py::TestUnary::test_unary_fn0, test/test_maskedtensor.py::TestUnary::test_unary_fn1, test/test_maskedtensor.py::TestUnary::test_unary_fn10, test/test_maskedtensor.py::TestUnary::test_unary_fn11, test/test_maskedtensor.py::TestUnary::test_unary_fn12, test/test_maskedtensor.py::TestUnary::test_unary_fn13, test/test_maskedtensor.py::TestUnary::test_unary_fn14, test/test_maskedtensor.py::TestUnary::test_unary_fn15, test/test_maskedtensor.py::TestUnary::test_unary_fn16, test/test_maskedtensor.py::TestUnary::test_unary_fn17, test/test_maskedtensor.py::TestUnary::test_unary_fn18, test/test_maskedtensor.py::TestUnary::test_unary_fn19, test/test_maskedtensor.py::TestUnary::test_unary_fn2, test/test_maskedtensor.py::TestUnary::test_unary_fn20, test/test_maskedtensor.py::TestUnary::test_unary_fn21, test/test_maskedtensor.py::TestUnary::test_unary_fn22, test/test_maskedtensor.py::TestUnary::test_unary_fn23, test/test_maskedtensor.py::TestUnary::test_unary_fn24, test/test_maskedtensor.py::TestUnary::test_unary_fn25, test/test_maskedtensor.py::TestUnary::test_unary_fn26, test/test_maskedtensor.py::TestUnary::test_unary_fn27, test/test_maskedtensor.py::TestUnary::test_unary_fn28, test/test_maskedtensor.py::TestUnary::test_unary_fn29, test/test_maskedtensor.py::TestUnary::test_unary_fn3, test/test_maskedtensor.py::TestUnary::test_unary_fn30, test/test_maskedtensor.py::TestUnary::test_unary_fn31, test/test_maskedtensor.py::TestUnary::test_unary_fn32, test/test_maskedtensor.py::TestUnary::test_unary_fn33, test/test_maskedtensor.py::TestUnary::test_unary_fn34, test/test_maskedtensor.py::TestUnary::test_unary_fn35, test/test_maskedtensor.py::TestUnary::test_unary_fn36, test/test_maskedtensor.py::TestUnary::test_unary_fn37, test/test_maskedtensor.py::TestUnary::test_unary_fn38, test/test_maskedtensor.py::TestUnary::test_unary_fn39, test/test_maskedtensor.py::TestUnary::test_unary_fn4, test/test_maskedtensor.py::TestUnary::test_unary_fn40, test/test_maskedtensor.py::TestUnary::test_unary_fn41, test/test_maskedtensor.py::TestUnary::test_unary_fn42, test/test_maskedtensor.py::TestUnary::test_unary_fn43, test/test_maskedtensor.py::TestUnary::test_unary_fn44, test/test_maskedtensor.py::TestUnary::test_unary_fn45, test/test_maskedtensor.py::TestUnary::test_unary_fn46, test/test_maskedtensor.py::TestUnary::test_unary_fn47, test/test_maskedtensor.py::TestUnary::test_unary_fn48, test/test_maskedtensor.py::TestUnary::test_unary_fn49, test/test_maskedtensor.py::TestUnary::test_unary_fn5, test/test_maskedtensor.py::TestUnary::test_unary_fn50, test/test_maskedtensor.py::TestUnary::test_unary_fn51, test/test_maskedtensor.py::TestUnary::test_unary_fn52, test/test_maskedtensor.py::TestUnary::test_unary_fn53, test/test_maskedtensor.py::TestUnary::test_unary_fn54, test/test_maskedtensor.py::TestUnary::test_unary_fn55, test/test_maskedtensor.py::TestUnary::test_unary_fn56, test/test_maskedtensor.py::TestUnary::test_unary_fn57, test/test_maskedtensor.py::TestUnary::test_unary_fn58, test/test_maskedtensor.py::TestUnary::test_unary_fn59, test/test_maskedtensor.py::TestUnary::test_unary_fn6, test/test_maskedtensor.py::TestUnary::test_unary_fn60, test/test_maskedtensor.py::TestUnary::test_unary_fn61, test/test_maskedtensor.py::TestUnary::test_unary_fn7, test/test_maskedtensor.py::TestUnary::test_unary_fn8, test/test_maskedtensor.py::TestUnary::test_unary_fn9, test/test_maskedtensor.py::TestBinary::test_binary_fn0, test/test_maskedtensor.py::TestBinary::test_binary_fn1, test/test_maskedtensor.py::TestBinary::test_binary_fn10, test/test_maskedtensor.py::TestBinary::test_binary_fn11, test/test_maskedtensor.py::TestBinary::test_binary_fn12, test/test_maskedtensor.py::TestBinary::test_binary_fn13, test/test_maskedtensor.py::TestBinary::test_binary_fn14, test/test_maskedtensor.py::TestBinary::test_binary_fn15, test/test_maskedtensor.py::TestBinary::test_binary_fn16, test/test_maskedtensor.py::TestBinary::test_binary_fn17, test/test_maskedtensor.py::TestBinary::test_binary_fn18, test/test_maskedtensor.py::TestBinary::test_binary_fn19, test/test_maskedtensor.py::TestBinary::test_binary_fn2, test/test_maskedtensor.py::TestBinary::test_binary_fn20, test/test_maskedtensor.py::TestBinary::test_binary_fn21, test/test_maskedtensor.py::TestBinary::test_binary_fn22, test/test_maskedtensor.py::TestBinary::test_binary_fn23, test/test_maskedtensor.py::TestBinary::test_binary_fn24, test/test_maskedtensor.py::TestBinary::test_binary_fn25, test/test_maskedtensor.py::TestBinary::test_binary_fn26, test/test_maskedtensor.py::TestBinary::test_binary_fn27, test/test_maskedtensor.py::TestBinary::test_binary_fn28, test/test_maskedtensor.py::TestBinary::test_binary_fn29, test/test_maskedtensor.py::TestBinary::test_binary_fn3, test/test_maskedtensor.py::TestBinary::test_binary_fn30, test/test_maskedtensor.py::TestBinary::test_binary_fn31, test/test_maskedtensor.py::TestBinary::test_binary_fn32, test/test_maskedtensor.py::TestBinary::test_binary_fn33, test/test_maskedtensor.py::TestBinary::test_binary_fn34, test/test_maskedtensor.py::TestBinary::test_binary_fn35, test/test_maskedtensor.py::TestBinary::test_binary_fn4, test/test_maskedtensor.py::TestBinary::test_binary_fn5, test/test_maskedtensor.py::TestBinary::test_binary_fn6, test/test_maskedtensor.py::TestBinary::test_binary_fn7, test/test_maskedtensor.py::TestBinary::test_binary_fn8, test/test_maskedtensor.py::TestBinary::test_binary_fn9, test/test_maskedtensor.py::TestBinary::test_inplace_binary_fn0, test/test_maskedtensor.py::TestBinary::test_inplace_binary_fn1, test/test_maskedtensor.py::TestBinary::test_inplace_binary_fn10, test/test_maskedtensor.py::TestBinary::test_inplace_binary_fn11, test/test_maskedtensor.py::TestBinary::test_inplace_binary_fn12, test/test_maskedtensor.py::TestBinary::test_inplace_binary_fn13, test/test_maskedtensor.py::TestBinary::test_inplace_binary_fn14, test/test_maskedtensor.py::TestBinary::test_inplace_binary_fn15, test/test_maskedtensor.py::TestBinary::test_inplace_binary_fn16, test/test_maskedtensor.py::TestBinary::test_inplace_binary_fn17, test/test_maskedtensor.py::TestBinary::test_inplace_binary_fn18, test/test_maskedtensor.py::TestBinary::test_inplace_binary_fn19, test/test_maskedtensor.py::TestBinary::test_inplace_binary_fn2, test/test_maskedtensor.py::TestBinary::test_inplace_binary_fn20, test/test_maskedtensor.py::TestBinary::test_inplace_binary_fn21, test/test_maskedtensor.py::TestBinary::test_inplace_binary_fn22, test/test_maskedtensor.py::TestBinary::test_inplace_binary_fn23, test/test_maskedtensor.py::TestBinary::test_inplace_binary_fn24, test/test_maskedtensor.py::TestBinary::test_inplace_binary_fn25, test/test_maskedtensor.py::TestBinary::test_inplace_binary_fn26, test/test_maskedtensor.py::TestBinary::test_inplace_binary_fn27, test/test_maskedtensor.py::TestBinary::test_inplace_binary_fn28, test/test_maskedtensor.py::TestBinary::test_inplace_binary_fn29, test/test_maskedtensor.py::TestBinary::test_inplace_binary_fn3, test/test_maskedtensor.py::TestBinary::test_inplace_binary_fn4, test/test_maskedtensor.py::TestBinary::test_inplace_binary_fn5, test/test_maskedtensor.py::TestBinary::test_inplace_binary_fn6, test/test_maskedtensor.py::TestBinary::test_inplace_binary_fn7, test/test_maskedtensor.py::TestBinary::test_inplace_binary_fn8, test/test_maskedtensor.py::TestBinary::test_inplace_binary_fn9, test/test_maskedtensor.py::TestBinary::test_masks_match_fn_name_add, test/test_maskedtensor.py::TestBinary::test_masks_match_fn_name_add_, test/test_maskedtensor.py::TestReductions::test__is_any_true, test/test_maskedtensor.py::TestReductions::test__is_any_true_false, test/test_maskedtensor.py::TestReductions::test_all, test/test_maskedtensor.py::TestReductions::test_amax, test/test_maskedtensor.py::TestReductions::test_amax_grad, test/test_maskedtensor.py::TestReductions::test_amin, test/test_maskedtensor.py::TestReductions::test_amin_grad, test/test_maskedtensor.py::TestReductions::test_any_true_dtype, test/test_maskedtensor.py::TestReductions::test_backward, test/test_maskedtensor.py::TestReductions::test_grad_dtype, test/test_maskedtensor.py::TestReductions::test_max_not_implemented, test/test_maskedtensor.py::TestReductions::test_mean, test/test_maskedtensor.py::TestReductions::test_mean_dim_grad, test/test_maskedtensor.py::TestReductions::test_mean_grad_case_1a, test/test_maskedtensor.py::TestReductions::test_mean_grad_case_1b, test/test_maskedtensor.py::TestReductions::test_mean_grad_case_1c, test/test_maskedtensor.py::TestReductions::test_mean_grad_case_1d, test/test_maskedtensor.py::TestReductions::test_mean_grad_case_1e, test/test_maskedtensor.py::TestReductions::test_mean_grad_case_1f, test/test_maskedtensor.py::TestReductions::test_prod, test/test_maskedtensor.py::TestReductions::test_prod_grad, test/test_maskedtensor.py::TestReductions::test_sum, test/test_maskedtensor.py::TestReductions::test_sum_grad, test/test_maskedtensor.py::TestOperatorsCUDA::test_binary_core_add_layout0_cuda_float16, test/test_maskedtensor.py::TestOperatorsCUDA::test_binary_core_add_layout0_cuda_float32, test/test_maskedtensor.py::TestOperatorsCUDA::test_binary_core_add_layout0_cuda_float64, test/test_maskedtensor.py::TestOperatorsCUDA::test_binary_core_add_layout1_cuda_float16, test/test_maskedtensor.py::TestOperatorsCUDA::test_binary_core_add_layout1_cuda_float32, test/test_maskedtensor.py::TestOperatorsCUDA::test_binary_core_add_layout1_cuda_float64, test/test_maskedtensor.py::TestOperatorsCUDA::test_binary_core_add_layout2_cuda_float16, test/test_maskedtensor.py::TestOperatorsCUDA::test_binary_core_add_layout2_cuda_float32, 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test/test_maskedtensor.py::TestOperatorsCUDA::test_binary_core_div_floor_rounding_layout0_cuda_float16, test/test_maskedtensor.py::TestOperatorsCUDA::test_binary_core_div_floor_rounding_layout0_cuda_float32, test/test_maskedtensor.py::TestOperatorsCUDA::test_binary_core_div_floor_rounding_layout0_cuda_float64, test/test_maskedtensor.py::TestOperatorsCUDA::test_binary_core_div_floor_rounding_layout1_cuda_float16, test/test_maskedtensor.py::TestOperatorsCUDA::test_binary_core_div_floor_rounding_layout1_cuda_float32, test/test_maskedtensor.py::TestOperatorsCUDA::test_binary_core_div_floor_rounding_layout1_cuda_float64, test/test_maskedtensor.py::TestOperatorsCUDA::test_binary_core_div_floor_rounding_layout2_cuda_float16, test/test_maskedtensor.py::TestOperatorsCUDA::test_binary_core_div_floor_rounding_layout2_cuda_float32, test/test_maskedtensor.py::TestOperatorsCUDA::test_binary_core_div_floor_rounding_layout2_cuda_float64, 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test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_signbit_layout1_cuda_float16, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_signbit_layout1_cuda_float32, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_signbit_layout1_cuda_float64, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_signbit_layout2_cuda_float16, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_signbit_layout2_cuda_float32, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_signbit_layout2_cuda_float64, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sin_layout0_cuda_float16, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sin_layout0_cuda_float32, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sin_layout0_cuda_float64, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sin_layout1_cuda_float16, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sin_layout1_cuda_float32, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sin_layout1_cuda_float64, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sin_layout2_cuda_float16, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sin_layout2_cuda_float32, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sin_layout2_cuda_float64, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sinc_layout0_cuda_float16, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sinc_layout0_cuda_float32, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sinc_layout0_cuda_float64, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sinc_layout1_cuda_float16, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sinc_layout1_cuda_float32, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sinc_layout1_cuda_float64, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sinc_layout2_cuda_float16, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sinc_layout2_cuda_float32, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sinc_layout2_cuda_float64, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sinh_layout0_cuda_float16, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sinh_layout0_cuda_float32, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sinh_layout0_cuda_float64, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sinh_layout1_cuda_float16, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sinh_layout1_cuda_float32, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sinh_layout1_cuda_float64, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sinh_layout2_cuda_float16, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sinh_layout2_cuda_float32, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sinh_layout2_cuda_float64, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sqrt_layout0_cuda_float16, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sqrt_layout0_cuda_float32, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sqrt_layout0_cuda_float64, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sqrt_layout1_cuda_float16, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sqrt_layout1_cuda_float32, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sqrt_layout1_cuda_float64, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sqrt_layout2_cuda_float16, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sqrt_layout2_cuda_float32, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_sqrt_layout2_cuda_float64, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_square_layout0_cuda_float16, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_square_layout0_cuda_float32, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_square_layout0_cuda_float64, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_square_layout1_cuda_float16, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_square_layout1_cuda_float32, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_square_layout1_cuda_float64, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_square_layout2_cuda_float16, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_square_layout2_cuda_float32, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_square_layout2_cuda_float64, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_tan_layout0_cuda_float16, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_tan_layout0_cuda_float32, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_tan_layout0_cuda_float64, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_tan_layout1_cuda_float16, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_tan_layout1_cuda_float32, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_tan_layout1_cuda_float64, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_tan_layout2_cuda_float16, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_tan_layout2_cuda_float32, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_tan_layout2_cuda_float64, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_tanh_layout0_cuda_float16, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_tanh_layout0_cuda_float32, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_tanh_layout0_cuda_float64, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_tanh_layout1_cuda_float16, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_tanh_layout1_cuda_float32, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_tanh_layout1_cuda_float64, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_tanh_layout2_cuda_float16, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_tanh_layout2_cuda_float32, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_tanh_layout2_cuda_float64, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_trunc_layout0_cuda_float16, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_trunc_layout0_cuda_float32, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_trunc_layout0_cuda_float64, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_trunc_layout1_cuda_float16, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_trunc_layout1_cuda_float32, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_trunc_layout1_cuda_float64, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_trunc_layout2_cuda_float16, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_trunc_layout2_cuda_float32, test/test_maskedtensor.py::TestOperatorsCUDA::test_unary_core_trunc_layout2_cuda_float64, test/test_maskedtensor.py::TestBasicsCUDA::test_add_cuda, test/test_maskedtensor.py::TestBasicsCUDA::test_contiguous_cuda, test/test_maskedtensor.py::TestBasicsCUDA::test_diff_dim_cuda, test/test_maskedtensor.py::TestBasicsCUDA::test_diff_layouts_cuda, test/test_maskedtensor.py::TestBasicsCUDA::test_diff_sizes_cuda, test/test_maskedtensor.py::TestBasicsCUDA::test_grad_warning_cuda, test/test_maskedtensor.py::TestBasicsCUDA::test_invalid_sparse_coo_values_cuda, test/test_maskedtensor.py::TestBasicsCUDA::test_invalid_sparse_csr_values_cuda, test/test_maskedtensor.py::TestBasicsCUDA::test_invalid_sparse_layout_cuda, test/test_maskedtensor.py::TestBasicsCUDA::test_invalid_tensor_inputs_cuda, test/test_maskedtensor.py::TestBasicsCUDA::test_nn_unfold_cuda, test/test_maskedtensor.py::TestBasicsCUDA::test_softmax_cuda, test/test_maskedtensor.py::TestBasicsCUDA::test_stack_cuda, test/test_maskedtensor.py::TestBasicsCUDA::test_to_dense_and_sparse_coo_cuda, test/test_maskedtensor.py::TestBasicsCUDA::test_to_dense_and_sparse_csr_cuda, test/test_maskedtensor.py::TestBasicsCUDA::test_to_dense_cuda, test/test_maskedtensor.py::TestBasicsCUDA::test_to_sparse_cuda, test/test_maskedtensor.py::TestBasicsCUDA::test_unfold_cuda, test/test_maskedtensor.py::TestBasicsCUDA::test_where_cuda 2025-07-17T10:31:56.7084474Z 2025-07-17T10:31:56.7084644Z Running test_matmul_cuda 1/1 ... [2025-07-17 10:31:56.673374] 2025-07-17T10:31:56.7085137Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:31:56.7085802Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_matmul_cuda.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 10:31:56.673687] 2025-07-17T10:31:58.8538677Z 2025-07-17T10:31:58.8539601Z test_masked 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_masked_1.1_9165978b3c13a083_.log 2025-07-17T10:31:58.8577492Z Running 194 items in this shard: test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_amax_cuda_bfloat16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_amax_cuda_float16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_amax_cuda_float32, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_amax_cuda_float64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_amax_cuda_int16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_amax_cuda_int32, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_amax_cuda_int64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_amax_cuda_int8, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_amax_cuda_uint8, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_amin_cuda_bfloat16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_amin_cuda_float16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_amin_cuda_float32, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_amin_cuda_float64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_amin_cuda_int16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_amin_cuda_int32, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_amin_cuda_int64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_amin_cuda_int8, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_amin_cuda_uint8, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_mean_cuda_bfloat16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_mean_cuda_complex128, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_mean_cuda_complex64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_mean_cuda_float16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_mean_cuda_float32, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_mean_cuda_float64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_prod_cuda_bfloat16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_prod_cuda_bool, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_prod_cuda_complex128, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_prod_cuda_complex64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_prod_cuda_float16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_prod_cuda_float32, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_prod_cuda_float64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_prod_cuda_int16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_prod_cuda_int32, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_prod_cuda_int64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_prod_cuda_int8, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_prod_cuda_uint8, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_sum_cuda_bfloat16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_sum_cuda_bool, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_sum_cuda_complex128, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_sum_cuda_complex64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_sum_cuda_float16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_sum_cuda_float32, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_sum_cuda_float64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_sum_cuda_int16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_sum_cuda_int32, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_sum_cuda_int64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_sum_cuda_int8, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_coo_masked_sum_cuda_uint8, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_amax_cuda_bfloat16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_amax_cuda_float16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_amax_cuda_float32, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_amax_cuda_float64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_amax_cuda_int16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_amax_cuda_int32, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_amax_cuda_int64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_amax_cuda_int8, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_amax_cuda_uint8, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_amin_cuda_bfloat16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_amin_cuda_float16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_amin_cuda_float32, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_amin_cuda_float64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_amin_cuda_int16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_amin_cuda_int32, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_amin_cuda_int64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_amin_cuda_int8, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_amin_cuda_uint8, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_mean_cuda_bfloat16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_mean_cuda_complex128, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_mean_cuda_complex64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_mean_cuda_float16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_mean_cuda_float32, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_mean_cuda_float64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_prod_cuda_bfloat16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_prod_cuda_bool, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_prod_cuda_complex128, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_prod_cuda_complex64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_prod_cuda_float16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_prod_cuda_float32, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_prod_cuda_float64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_prod_cuda_int16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_prod_cuda_int32, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_prod_cuda_int64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_prod_cuda_int8, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_prod_cuda_uint8, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_sum_cuda_bfloat16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_sum_cuda_bool, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_sum_cuda_complex128, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_sum_cuda_complex64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_sum_cuda_float16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_sum_cuda_float32, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_sum_cuda_float64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_sum_cuda_int16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_sum_cuda_int32, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_sum_cuda_int64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_sum_cuda_int8, test/test_masked.py::TestMaskedCUDA::test_mask_layout_sparse_csr_masked_sum_cuda_uint8, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_amax_cuda_bfloat16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_amax_cuda_float16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_amax_cuda_float32, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_amax_cuda_float64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_amax_cuda_int16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_amax_cuda_int32, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_amax_cuda_int64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_amax_cuda_int8, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_amax_cuda_uint8, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_amin_cuda_bfloat16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_amin_cuda_float16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_amin_cuda_float32, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_amin_cuda_float64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_amin_cuda_int16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_amin_cuda_int32, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_amin_cuda_int64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_amin_cuda_int8, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_amin_cuda_uint8, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_mean_cuda_bfloat16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_mean_cuda_complex128, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_mean_cuda_complex64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_mean_cuda_float16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_mean_cuda_float32, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_mean_cuda_float64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_prod_cuda_bfloat16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_prod_cuda_bool, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_prod_cuda_complex128, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_prod_cuda_complex64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_prod_cuda_float16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_prod_cuda_float32, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_prod_cuda_float64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_prod_cuda_int16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_prod_cuda_int32, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_prod_cuda_int64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_prod_cuda_int8, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_prod_cuda_uint8, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_sum_cuda_bfloat16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_sum_cuda_bool, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_sum_cuda_complex128, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_sum_cuda_complex64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_sum_cuda_float16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_sum_cuda_float32, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_sum_cuda_float64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_sum_cuda_int16, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_sum_cuda_int32, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_sum_cuda_int64, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_sum_cuda_int8, test/test_masked.py::TestMaskedCUDA::test_mask_layout_strided_masked_sum_cuda_uint8, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_log_softmax_cuda_bfloat16, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_log_softmax_cuda_float16, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_log_softmax_cuda_float32, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_log_softmax_cuda_float64, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_norm_cuda_bfloat16, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_norm_cuda_float16, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_norm_cuda_float32, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_norm_cuda_float64, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_normalize_cuda_bfloat16, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_normalize_cuda_complex128, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_normalize_cuda_complex64, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_normalize_cuda_float16, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_normalize_cuda_float32, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_normalize_cuda_float64, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_softmax_cuda_bfloat16, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_softmax_cuda_float16, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_softmax_cuda_float32, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_softmax_cuda_float64, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_softmin_cuda_bfloat16, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_softmin_cuda_float16, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_softmin_cuda_float32, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_softmin_cuda_float64, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_std_cuda_bfloat16, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_std_cuda_complex128, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_std_cuda_complex64, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_std_cuda_float16, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_std_cuda_float32, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_std_cuda_float64, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_std_cuda_int16, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_std_cuda_int32, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_std_cuda_int64, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_std_cuda_int8, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_std_cuda_uint8, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_var_cuda_bfloat16, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_var_cuda_complex128, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_var_cuda_complex64, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_var_cuda_float16, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_var_cuda_float32, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_var_cuda_float64, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_var_cuda_int16, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_var_cuda_int32, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_var_cuda_int64, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_var_cuda_int8, test/test_masked.py::TestMaskedCUDA::test_reference_masked_masked_var_cuda_uint8, test/test_masked.py::TestMaskedCUDA::test_where_coo_fill_value_0_cuda, test/test_masked.py::TestMaskedCUDA::test_where_coo_fill_value_123_cuda, test/test_masked.py::TestMaskedCUDA::test_where_csr_fill_value_0_cuda, test/test_masked.py::TestMaskedCUDA::test_where_csr_fill_value_123_cuda, test/test_masked.py::TestMaskedCUDA::test_where_hybrid_coo_fill_value_0_cuda, test/test_masked.py::TestMaskedCUDA::test_where_hybrid_coo_fill_value_123_cuda 2025-07-17T10:31:58.8614442Z 2025-07-17T10:31:58.8614580Z Running test_monitor 1/1 ... [2025-07-17 10:31:58.853989] 2025-07-17T10:31:58.8614843Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:31:58.8615658Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_monitor.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 10:31:58.854289] 2025-07-17T10:32:02.7258163Z 2025-07-17T10:32:02.7259622Z test_monitor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_monitor_1.1_3dbdbfb6f56e0efd_.log 2025-07-17T10:32:02.7260911Z Running 6 items in this shard: test/test_monitor.py::TestMonitor::test_event_handler, test/test_monitor.py::TestMonitor::test_fixed_count_stat, test/test_monitor.py::TestMonitor::test_interval_stat, test/test_monitor.py::TestMonitor::test_log_event, test/test_monitor.py::TestMonitor::test_wait_counter, test/test_monitor.py::TestMonitorTensorboard::test_event_handler 2025-07-17T10:32:02.7261801Z 2025-07-17T10:32:02.7261925Z Running test_namedtensor 1/1 ... [2025-07-17 10:32:02.725683] 2025-07-17T10:32:02.7262203Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:32:02.7262884Z Executing ['/opt/conda/envs/py_3.12/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 10:32:02.726029] 2025-07-17T10:32:08.3007059Z 2025-07-17T10:32:08.3008071Z test_namedtensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_namedtensor_1.1_dd40a2aceb1091b7_.log 2025-07-17T10:32:08.3022023Z 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 2025-07-17T10:32:08.3035397Z 2025-07-17T10:32:08.3035614Z Running test_native_functions 1/1 ... [2025-07-17 10:32:08.300614] 2025-07-17T10:32:08.3035899Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:32:08.3036579Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_native_functions.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 10:32:08.300938] 2025-07-17T10:32:12.2732642Z 2025-07-17T10:32:12.2733624Z test_native_functions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_native_functions_1.1_b994c3be625068b9_.log 2025-07-17T10:32:12.2736812Z Running 11 items in this shard: test/test_native_functions.py::TestNativeFunctions::test_intlist_error_with_overload, test/test_native_functions.py::TestNativeFunctions::test_optional_filled_intlist, test/test_native_functions.py::TestNativeFunctions::test_optional_floatlist, test/test_native_functions.py::TestNativeFunctions::test_optional_floatlist_invalid, test/test_native_functions.py::TestNativeFunctions::test_optional_intlist, test/test_native_functions.py::TestNativeFunctions::test_optional_intlist_invalid, test/test_native_functions.py::TestNativeFunctions::test_string_defaults, test/test_native_functions.py::TestNativeFunctions::test_symintlist_error, test/test_native_functions.py::TestNativeFunctions::test_symintlist_error_with_overload, test/test_native_functions.py::TestNativeFunctions::test_symintlist_error_with_overload_but_is_unique, test/test_native_functions.py::TestNativeFunctions::test_vararg_symintlist_error 2025-07-17T10:32:12.2739013Z 2025-07-17T10:32:12.2739168Z Running test_numba_integration 1/1 ... [2025-07-17 10:32:12.273109] 2025-07-17T10:32:12.2739456Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:32:12.2740135Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_numba_integration.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 10:32:12.273452] 2025-07-17T10:32:16.8549796Z 2025-07-17T10:32:16.8550798Z test_numba_integration 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_numba_integration_1.1_c32eba0b7377c2fe_.log 2025-07-17T10:32:16.8553005Z Running 8 items in this shard: test/test_numba_integration.py::TestNumbaIntegration::test_active_device, test/test_numba_integration.py::TestNumbaIntegration::test_array_adaptor, test/test_numba_integration.py::TestNumbaIntegration::test_conversion_errors, test/test_numba_integration.py::TestNumbaIntegration::test_cuda_array_interface, test/test_numba_integration.py::TestNumbaIntegration::test_from_cuda_array_interface, test/test_numba_integration.py::TestNumbaIntegration::test_from_cuda_array_interface_active_device, test/test_numba_integration.py::TestNumbaIntegration::test_from_cuda_array_interface_inferred_strides, test/test_numba_integration.py::TestNumbaIntegration::test_from_cuda_array_interface_lifetime 2025-07-17T10:32:16.8555361Z 2025-07-17T10:32:16.8555500Z Running test_numpy_interop 1/1 ... [2025-07-17 10:32:16.854780] 2025-07-17T10:32:16.8555776Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:32:16.8556570Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_numpy_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 10:32:16.855098] 2025-07-17T10:32:21.2796920Z 2025-07-17T10:32:21.2797897Z test_numpy_interop 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_numpy_interop_1.1_6b5140816467194e_.log 2025-07-17T10:32:21.2807178Z Running 44 items in this shard: test/test_numpy_interop.py::TestNumPyInteropCUDA::test___eq___cuda_bool, test/test_numpy_interop.py::TestNumPyInteropCUDA::test___eq___cuda_complex128, test/test_numpy_interop.py::TestNumPyInteropCUDA::test___eq___cuda_complex64, test/test_numpy_interop.py::TestNumPyInteropCUDA::test___eq___cuda_float16, test/test_numpy_interop.py::TestNumPyInteropCUDA::test___eq___cuda_float32, test/test_numpy_interop.py::TestNumPyInteropCUDA::test___eq___cuda_float64, test/test_numpy_interop.py::TestNumPyInteropCUDA::test___eq___cuda_int16, test/test_numpy_interop.py::TestNumPyInteropCUDA::test___eq___cuda_int32, test/test_numpy_interop.py::TestNumPyInteropCUDA::test___eq___cuda_int64, test/test_numpy_interop.py::TestNumPyInteropCUDA::test___eq___cuda_int8, test/test_numpy_interop.py::TestNumPyInteropCUDA::test___eq___cuda_uint8, test/test_numpy_interop.py::TestNumPyInteropCUDA::test_ctor_with_invalid_numpy_array_sequence_cuda, test/test_numpy_interop.py::TestNumPyInteropCUDA::test_ctor_with_numpy_scalar_ctor_cuda, test/test_numpy_interop.py::TestNumPyInteropCUDA::test_empty_tensors_interop_cuda, test/test_numpy_interop.py::TestNumPyInteropCUDA::test_from_list_of_ndarray_warning_cuda, test/test_numpy_interop.py::TestNumPyInteropCUDA::test_from_numpy_cuda, test/test_numpy_interop.py::TestNumPyInteropCUDA::test_from_numpy_no_leak_on_invalid_dtype_cuda, test/test_numpy_interop.py::TestNumPyInteropCUDA::test_from_numpy_zero_element_type_cuda, test/test_numpy_interop.py::TestNumPyInteropCUDA::test_has_storage_numpy_cuda, test/test_numpy_interop.py::TestNumPyInteropCUDA::test_multiplication_numpy_scalar_cuda, test/test_numpy_interop.py::TestNumPyInteropCUDA::test_ndarray_astype_object_graph_break_2_cuda, test/test_numpy_interop.py::TestNumPyInteropCUDA::test_ndarray_astype_object_graph_break_cuda, test/test_numpy_interop.py::TestNumPyInteropCUDA::test_numpy_array_interface_cuda, test/test_numpy_interop.py::TestNumPyInteropCUDA::test_numpy_index_cuda, test/test_numpy_interop.py::TestNumPyInteropCUDA::test_numpy_index_multi_cuda, test/test_numpy_interop.py::TestNumPyInteropCUDA::test_numpy_non_writeable_cuda, test/test_numpy_interop.py::TestNumPyInteropCUDA::test_numpy_scalar_cmp_cuda_bfloat16, test/test_numpy_interop.py::TestNumPyInteropCUDA::test_numpy_scalar_cmp_cuda_bool, test/test_numpy_interop.py::TestNumPyInteropCUDA::test_numpy_scalar_cmp_cuda_complex128, test/test_numpy_interop.py::TestNumPyInteropCUDA::test_numpy_scalar_cmp_cuda_complex64, test/test_numpy_interop.py::TestNumPyInteropCUDA::test_numpy_scalar_cmp_cuda_float16, test/test_numpy_interop.py::TestNumPyInteropCUDA::test_numpy_scalar_cmp_cuda_float32, test/test_numpy_interop.py::TestNumPyInteropCUDA::test_numpy_scalar_cmp_cuda_float64, test/test_numpy_interop.py::TestNumPyInteropCUDA::test_numpy_scalar_cmp_cuda_int16, test/test_numpy_interop.py::TestNumPyInteropCUDA::test_numpy_scalar_cmp_cuda_int32, test/test_numpy_interop.py::TestNumPyInteropCUDA::test_numpy_scalar_cmp_cuda_int64, test/test_numpy_interop.py::TestNumPyInteropCUDA::test_numpy_scalar_cmp_cuda_int8, test/test_numpy_interop.py::TestNumPyInteropCUDA::test_numpy_scalar_cmp_cuda_uint8, test/test_numpy_interop.py::TestNumPyInteropCUDA::test_numpy_unresizable_cuda, test/test_numpy_interop.py::TestNumPyInteropCUDA::test_parse_numpy_int_cuda, test/test_numpy_interop.py::TestNumPyInteropCUDA::test_parse_numpy_int_overflow_cuda, test/test_numpy_interop.py::TestNumPyInteropCUDA::test_to_numpy_bool_cuda, test/test_numpy_interop.py::TestNumPyInteropCUDA::test_to_numpy_cuda, test/test_numpy_interop.py::TestNumPyInteropCUDA::test_to_numpy_force_argument_cuda 2025-07-17T10:32:21.2815553Z 2025-07-17T10:32:21.2815678Z Running test_openmp 1/1 ... [2025-07-17 10:32:21.279479] 2025-07-17T10:32:21.2815940Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:32:21.2816594Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_openmp.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 10:32:21.279785] 2025-07-17T10:32:28.0598896Z 2025-07-17T10:32:28.0600424Z test_openmp 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_openmp_1.1_a6ebd424a6e4b594_.log 2025-07-17T10:32:28.0601162Z Running 2 items in this shard: test/test_openmp.py::TestOpenMP_ParallelFor::test_n_threads, test/test_openmp.py::TestOpenMP_ParallelFor::test_one_thread 2025-07-17T10:32:28.0601571Z 2025-07-17T10:32:28.0601721Z Running test_ops_fwd_gradients 1/1 ... [2025-07-17 10:32:28.059739] 2025-07-17T10:32:28.0602026Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:32:28.0603714Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_ops_fwd_gradients.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 10:32:28.060034] 2025-07-17T10:33:18.0719686Z 2025-07-17T10:33:18.0724780Z test_matmul_cuda 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_matmul_cuda_1.1_7f95e708acfbbd6a_.log 2025-07-17T10:33:18.1200032Z Running 1598 items in this shard: test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_1_K_1_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_1_K_1_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_1_K_1_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_1_K_1_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_1_K_1_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_1_K_1_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_1_K_32_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_1_K_32_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_1_K_32_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_1_K_32_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_1_K_32_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_1_K_32_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_1_K_64_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_1_K_64_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_1_K_64_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_1_K_64_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_1_K_64_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_1_K_64_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_32_K_1_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_32_K_1_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_32_K_1_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_32_K_1_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_32_K_1_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_32_K_1_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_32_K_32_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_32_K_32_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_32_K_32_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_32_K_32_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_32_K_32_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_32_K_32_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_32_K_64_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_32_K_64_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_32_K_64_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_32_K_64_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_32_K_64_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_32_K_64_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_64_K_1_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_64_K_1_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_64_K_1_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_64_K_1_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_64_K_1_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_64_K_1_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_64_K_32_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_64_K_32_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_64_K_32_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_64_K_32_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_64_K_32_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_64_K_32_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_64_K_64_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_64_K_64_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_64_K_64_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_64_K_64_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_64_K_64_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_1_N_64_K_64_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_1_K_1_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_1_K_1_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_1_K_1_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_1_K_1_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_1_K_1_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_1_K_1_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_1_K_32_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_1_K_32_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_1_K_32_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_1_K_32_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_1_K_32_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_1_K_32_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_1_K_64_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_1_K_64_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_1_K_64_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_1_K_64_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_1_K_64_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_1_K_64_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_32_K_1_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_32_K_1_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_32_K_1_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_32_K_1_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_32_K_1_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_32_K_1_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_32_K_32_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_32_K_32_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_32_K_32_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_32_K_32_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_32_K_32_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_32_K_32_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_32_K_64_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_32_K_64_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_32_K_64_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_32_K_64_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_32_K_64_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_32_K_64_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_64_K_1_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_64_K_1_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_64_K_1_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_64_K_1_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_64_K_1_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_64_K_1_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_64_K_32_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_64_K_32_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_64_K_32_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_64_K_32_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_64_K_32_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_64_K_32_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_64_K_64_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_64_K_64_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_64_K_64_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_64_K_64_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_64_K_64_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_32_N_64_K_64_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_1_K_1_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_1_K_1_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_1_K_1_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_1_K_1_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_1_K_1_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_1_K_1_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_1_K_32_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_1_K_32_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_1_K_32_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_1_K_32_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_1_K_32_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_1_K_32_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_1_K_64_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_1_K_64_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_1_K_64_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_1_K_64_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_1_K_64_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_1_K_64_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_32_K_1_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_32_K_1_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_32_K_1_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_32_K_1_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_32_K_1_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_32_K_1_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_32_K_32_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_32_K_32_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_32_K_32_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_32_K_32_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_32_K_32_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_32_K_32_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_32_K_64_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_32_K_64_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_32_K_64_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_32_K_64_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_32_K_64_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_32_K_64_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_64_K_1_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_64_K_1_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_64_K_1_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_64_K_1_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_64_K_1_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_64_K_1_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_64_K_32_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_64_K_32_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_64_K_32_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_64_K_32_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_64_K_32_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_64_K_32_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_64_K_64_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_64_K_64_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_64_K_64_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_64_K_64_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_64_K_64_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_bfloat16_M_64_N_64_K_64_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_1_K_1_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_1_K_1_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_1_K_1_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_1_K_1_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_1_K_1_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_1_K_1_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_1_K_32_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_1_K_32_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_1_K_32_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_1_K_32_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_1_K_32_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_1_K_32_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_1_K_64_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_1_K_64_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_1_K_64_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_1_K_64_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_1_K_64_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_1_K_64_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_32_K_1_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_32_K_1_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_32_K_1_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_32_K_1_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_32_K_1_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_32_K_1_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_32_K_32_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_32_K_32_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_32_K_32_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_32_K_32_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_32_K_32_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_32_K_32_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_32_K_64_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_32_K_64_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_32_K_64_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_32_K_64_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_32_K_64_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_32_K_64_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_64_K_1_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_64_K_1_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_64_K_1_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_64_K_1_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_64_K_1_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_64_K_1_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_64_K_32_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_64_K_32_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_64_K_32_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_64_K_32_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_64_K_32_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_64_K_32_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_64_K_64_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_64_K_64_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_64_K_64_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_64_K_64_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_64_K_64_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_1_N_64_K_64_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_1_K_1_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_1_K_1_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_1_K_1_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_1_K_1_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_1_K_1_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_1_K_1_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_1_K_32_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_1_K_32_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_1_K_32_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_1_K_32_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_1_K_32_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_1_K_32_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_1_K_64_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_1_K_64_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_1_K_64_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_1_K_64_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_1_K_64_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_1_K_64_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_32_K_1_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_32_K_1_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_32_K_1_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_32_K_1_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_32_K_1_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_32_K_1_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_32_K_32_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_32_K_32_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_32_K_32_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_32_K_32_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_32_K_32_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_32_K_32_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_32_K_64_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_32_K_64_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_32_K_64_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_32_K_64_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_32_K_64_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_32_K_64_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_64_K_1_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_64_K_1_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_64_K_1_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_64_K_1_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_64_K_1_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_64_K_1_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_64_K_32_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_64_K_32_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_64_K_32_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_64_K_32_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_64_K_32_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_64_K_32_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_64_K_64_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_64_K_64_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_64_K_64_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_64_K_64_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_64_K_64_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_32_N_64_K_64_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_1_K_1_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_1_K_1_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_1_K_1_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_1_K_1_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_1_K_1_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_1_K_1_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_1_K_32_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_1_K_32_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_1_K_32_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_1_K_32_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_1_K_32_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_1_K_32_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_1_K_64_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_1_K_64_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_1_K_64_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_1_K_64_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_1_K_64_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_1_K_64_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_32_K_1_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_32_K_1_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_32_K_1_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_32_K_1_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_32_K_1_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_32_K_1_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_32_K_32_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_32_K_32_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_32_K_32_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_32_K_32_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_32_K_32_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_32_K_32_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_32_K_64_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_32_K_64_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_32_K_64_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_32_K_64_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_32_K_64_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_32_K_64_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_64_K_1_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_64_K_1_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_64_K_1_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_64_K_1_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_64_K_1_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_64_K_1_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_64_K_32_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_64_K_32_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_64_K_32_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_64_K_32_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_64_K_32_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_64_K_32_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_64_K_64_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_64_K_64_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_64_K_64_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_64_K_64_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_64_K_64_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float16_M_64_N_64_K_64_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_1_K_1_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_1_K_1_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_1_K_1_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_1_K_1_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_1_K_1_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_1_K_1_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_1_K_32_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_1_K_32_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_1_K_32_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_1_K_32_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_1_K_32_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_1_K_32_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_1_K_64_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_1_K_64_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_1_K_64_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_1_K_64_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_1_K_64_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_1_K_64_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_32_K_1_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_32_K_1_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_32_K_1_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_32_K_1_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_32_K_1_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_32_K_1_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_32_K_32_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_32_K_32_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_32_K_32_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_32_K_32_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_32_K_32_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_32_K_32_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_32_K_64_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_32_K_64_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_32_K_64_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_32_K_64_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_32_K_64_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_32_K_64_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_64_K_1_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_64_K_1_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_64_K_1_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_64_K_1_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_64_K_1_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_64_K_1_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_64_K_32_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_64_K_32_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_64_K_32_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_64_K_32_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_64_K_32_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_64_K_32_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_64_K_64_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_64_K_64_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_64_K_64_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_64_K_64_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_64_K_64_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_1_N_64_K_64_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_1_K_1_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_1_K_1_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_1_K_1_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_1_K_1_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_1_K_1_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_1_K_1_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_1_K_32_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_1_K_32_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_1_K_32_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_1_K_32_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_1_K_32_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_1_K_32_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_1_K_64_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_1_K_64_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_1_K_64_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_1_K_64_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_1_K_64_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_1_K_64_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_32_K_1_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_32_K_1_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_32_K_1_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_32_K_1_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_32_K_1_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_32_K_1_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_32_K_32_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_32_K_32_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_32_K_32_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_32_K_32_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_32_K_32_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_32_K_32_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_32_K_64_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_32_K_64_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_32_K_64_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_32_K_64_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_32_K_64_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_32_K_64_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_64_K_1_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_64_K_1_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_64_K_1_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_64_K_1_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_64_K_1_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_64_K_1_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_64_K_32_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_64_K_32_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_64_K_32_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_64_K_32_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_64_K_32_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_64_K_32_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_64_K_64_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_64_K_64_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_64_K_64_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_64_K_64_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_64_K_64_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_32_N_64_K_64_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_1_K_1_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_1_K_1_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_1_K_1_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_1_K_1_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_1_K_1_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_1_K_1_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_1_K_32_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_1_K_32_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_1_K_32_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_1_K_32_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_1_K_32_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_1_K_32_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_1_K_64_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_1_K_64_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_1_K_64_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_1_K_64_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_1_K_64_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_1_K_64_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_32_K_1_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_32_K_1_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_32_K_1_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_32_K_1_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_32_K_1_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_32_K_1_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_32_K_32_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_32_K_32_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_32_K_32_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_32_K_32_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_32_K_32_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_32_K_32_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_32_K_64_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_32_K_64_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_32_K_64_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_32_K_64_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_32_K_64_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_32_K_64_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_64_K_1_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_64_K_1_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_64_K_1_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_64_K_1_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_64_K_1_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_64_K_1_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_64_K_32_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_64_K_32_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_64_K_32_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_64_K_32_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_64_K_32_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_64_K_32_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_64_K_64_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_64_K_64_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_64_K_64_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_64_K_64_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_64_K_64_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_addmm_baddmm_dtype_overload_float32_M_64_N_64_K_64_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_addmm_alignment_cuda_float16, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_addmm_no_reduced_precision_small_size_4_size_32768_backend_cublas_cuda_float16, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_addmm_no_reduced_precision_small_size_4_size_32768_backend_cublaslt_cuda_float16, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_addmm_no_reduced_precision_small_size_8_size_32768_backend_cublas_cuda_float16, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_addmm_no_reduced_precision_small_size_8_size_32768_backend_cublaslt_cuda_float16, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_addmm_reduced_precision_fp16_accumulate_size_10000_backend_cublas_cuda_bfloat16, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_addmm_reduced_precision_fp16_accumulate_size_10000_backend_cublas_cuda_float16, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_addmm_reduced_precision_fp16_accumulate_size_10000_backend_cublaslt_cuda_bfloat16, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_addmm_reduced_precision_fp16_accumulate_size_10000_backend_cublaslt_cuda_float16, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_addmm_reduced_precision_fp16_accumulate_size_1000_backend_cublas_cuda_bfloat16, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_addmm_reduced_precision_fp16_accumulate_size_1000_backend_cublas_cuda_float16, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_addmm_reduced_precision_fp16_accumulate_size_1000_backend_cublaslt_cuda_bfloat16, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_addmm_reduced_precision_fp16_accumulate_size_1000_backend_cublaslt_cuda_float16, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_addmm_reduced_precision_fp16_accumulate_size_100_backend_cublas_cuda_bfloat16, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_addmm_reduced_precision_fp16_accumulate_size_100_backend_cublas_cuda_float16, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_addmm_reduced_precision_fp16_accumulate_size_100_backend_cublaslt_cuda_bfloat16, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_addmm_reduced_precision_fp16_accumulate_size_100_backend_cublaslt_cuda_float16, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_addmm_reduced_precision_size_10000_backend_cublas_cuda_bfloat16, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_addmm_reduced_precision_size_10000_backend_cublas_cuda_float16, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_addmm_reduced_precision_size_10000_backend_cublaslt_cuda_bfloat16, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_addmm_reduced_precision_size_10000_backend_cublaslt_cuda_float16, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_addmm_reduced_precision_size_1000_backend_cublas_cuda_bfloat16, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_addmm_reduced_precision_size_1000_backend_cublas_cuda_float16, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_addmm_reduced_precision_size_1000_backend_cublaslt_cuda_bfloat16, 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test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_addmm_size_10000_backend_cublaslt_cuda_bfloat16, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_addmm_size_10000_backend_cublaslt_cuda_float16, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_addmm_size_10000_backend_cublaslt_cuda_float32, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_addmm_size_1000_backend_cublas_cuda_bfloat16, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_addmm_size_1000_backend_cublas_cuda_float16, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_addmm_size_1000_backend_cublas_cuda_float32, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_addmm_size_1000_backend_cublaslt_cuda_bfloat16, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_addmm_size_1000_backend_cublaslt_cuda_float16, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_addmm_size_1000_backend_cublaslt_cuda_float32, 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test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_cublas_baddbmm_large_input_2_100_100_100_cuda_float32, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_fp16_accum_and_fp32_out_failure_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_fp16_accum_and_fp32_out_failure_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_fp16_accum_and_fp32_out_failure_batch_size_32_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_fp16_accum_and_fp32_out_failure_batch_size_32_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_grouped_gemm_2d_2d_strided_False_a_row_major_False_b_row_major_False_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_grouped_gemm_2d_2d_strided_False_a_row_major_False_b_row_major_True_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_grouped_gemm_2d_2d_strided_False_a_row_major_True_b_row_major_False_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_grouped_gemm_2d_2d_strided_False_a_row_major_True_b_row_major_True_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_grouped_gemm_2d_2d_strided_True_a_row_major_False_b_row_major_False_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_grouped_gemm_2d_2d_strided_True_a_row_major_False_b_row_major_True_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_grouped_gemm_2d_2d_strided_True_a_row_major_True_b_row_major_False_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_grouped_gemm_2d_2d_strided_True_a_row_major_True_b_row_major_True_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_grouped_gemm_2d_3d_strided_False_a_row_major_False_b_row_major_False_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_grouped_gemm_2d_3d_strided_False_a_row_major_False_b_row_major_True_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_grouped_gemm_2d_3d_strided_False_a_row_major_True_b_row_major_False_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_grouped_gemm_2d_3d_strided_False_a_row_major_True_b_row_major_True_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_grouped_gemm_2d_3d_strided_True_a_row_major_False_b_row_major_False_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_grouped_gemm_2d_3d_strided_True_a_row_major_False_b_row_major_True_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_grouped_gemm_2d_3d_strided_True_a_row_major_True_b_row_major_False_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_grouped_gemm_2d_3d_strided_True_a_row_major_True_b_row_major_True_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_grouped_gemm_3d_2d_strided_False_a_row_major_False_b_row_major_False_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_grouped_gemm_3d_2d_strided_False_a_row_major_False_b_row_major_True_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_grouped_gemm_3d_2d_strided_False_a_row_major_True_b_row_major_False_cuda, 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test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_mm_bmm_dtype_overload_float32_M_64_N_32_K_1_batch_size_16_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_mm_bmm_dtype_overload_float32_M_64_N_32_K_1_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_mm_bmm_dtype_overload_float32_M_64_N_32_K_1_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_mm_bmm_dtype_overload_float32_M_64_N_32_K_32_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_mm_bmm_dtype_overload_float32_M_64_N_32_K_32_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_mm_bmm_dtype_overload_float32_M_64_N_32_K_32_batch_size_16_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_mm_bmm_dtype_overload_float32_M_64_N_32_K_32_batch_size_16_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_mm_bmm_dtype_overload_float32_M_64_N_32_K_32_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_mm_bmm_dtype_overload_float32_M_64_N_32_K_32_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_mm_bmm_dtype_overload_float32_M_64_N_32_K_64_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_mm_bmm_dtype_overload_float32_M_64_N_32_K_64_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_mm_bmm_dtype_overload_float32_M_64_N_32_K_64_batch_size_16_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_mm_bmm_dtype_overload_float32_M_64_N_32_K_64_batch_size_16_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_mm_bmm_dtype_overload_float32_M_64_N_32_K_64_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_mm_bmm_dtype_overload_float32_M_64_N_32_K_64_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_mm_bmm_dtype_overload_float32_M_64_N_64_K_1_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_mm_bmm_dtype_overload_float32_M_64_N_64_K_1_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_mm_bmm_dtype_overload_float32_M_64_N_64_K_1_batch_size_16_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_mm_bmm_dtype_overload_float32_M_64_N_64_K_1_batch_size_16_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_mm_bmm_dtype_overload_float32_M_64_N_64_K_1_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_mm_bmm_dtype_overload_float32_M_64_N_64_K_1_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_mm_bmm_dtype_overload_float32_M_64_N_64_K_32_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_mm_bmm_dtype_overload_float32_M_64_N_64_K_32_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_mm_bmm_dtype_overload_float32_M_64_N_64_K_32_batch_size_16_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_mm_bmm_dtype_overload_float32_M_64_N_64_K_32_batch_size_16_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_mm_bmm_dtype_overload_float32_M_64_N_64_K_32_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_mm_bmm_dtype_overload_float32_M_64_N_64_K_32_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_mm_bmm_dtype_overload_float32_M_64_N_64_K_64_batch_size0_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_mm_bmm_dtype_overload_float32_M_64_N_64_K_64_batch_size0_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_mm_bmm_dtype_overload_float32_M_64_N_64_K_64_batch_size_16_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_mm_bmm_dtype_overload_float32_M_64_N_64_K_64_batch_size_16_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_mm_bmm_dtype_overload_float32_M_64_N_64_K_64_batch_size_1_backend_cublas_cuda, test/test_matmul_cuda.py::TestMatmulCudaCUDA::test_mm_bmm_dtype_overload_float32_M_64_N_64_K_64_batch_size_1_backend_cublaslt_cuda, test/test_matmul_cuda.py::TestMixedDtypesLinearCudaCUDA::test_mixed_dtypes_linear_cuda_bfloat16, test/test_matmul_cuda.py::TestMixedDtypesLinearCudaCUDA::test_mixed_dtypes_linear_cuda_float16, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_compile_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_error_messages_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_error_messages_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_False_1023_64_48_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_False_1023_64_48_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_False_1025_128_96_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_False_1025_128_96_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_False_127_96_1024_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_False_127_96_1024_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_False_128_128_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_False_128_128_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_False_128_256_512_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_False_128_256_512_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_False_197_224_272_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_False_197_224_272_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_False_197_240_272_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_False_197_240_272_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_False_256_256_256_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_False_256_256_256_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_False_256_512_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_False_256_512_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_False_2_1024_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_False_2_1024_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_False_31_1024_64_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_False_31_1024_64_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_False_45_96_1024_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_False_45_96_1024_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_False_512_128_256_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_False_512_128_256_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_False_65_96_112_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_False_65_96_112_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_True_1023_64_48_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_True_1023_64_48_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_True_1025_128_96_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_True_1025_128_96_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_True_127_96_1024_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_True_127_96_1024_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_True_128_128_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_True_128_128_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_True_128_256_512_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_True_128_256_512_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_True_197_224_272_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_True_197_224_272_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_True_197_240_272_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_True_197_240_272_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_True_256_256_256_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_True_256_256_256_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_True_256_512_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_True_256_512_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_True_2_1024_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_True_2_1024_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_True_31_1024_64_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_True_31_1024_64_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_True_45_96_1024_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_True_45_96_1024_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_True_512_128_256_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_True_512_128_256_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_True_65_96_112_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_eye_b_eye_fast_accum_True_65_96_112_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_False_1023_64_48_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_False_1023_64_48_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_False_1025_128_96_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_False_1025_128_96_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_False_127_96_1024_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_False_127_96_1024_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_False_128_128_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_False_128_128_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_False_128_256_512_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_False_128_256_512_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_False_197_224_272_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_False_197_224_272_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_False_197_240_272_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_False_197_240_272_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_False_256_256_256_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_False_256_256_256_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_False_256_512_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_False_256_512_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_False_2_1024_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_False_2_1024_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_False_31_1024_64_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_False_31_1024_64_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_False_45_96_1024_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_False_45_96_1024_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_False_512_128_256_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_False_512_128_256_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_False_65_96_112_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_False_65_96_112_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_True_1023_64_48_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_True_1023_64_48_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_True_1025_128_96_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_True_1025_128_96_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_True_127_96_1024_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_True_127_96_1024_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_True_128_128_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_True_128_128_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_True_128_256_512_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_True_128_256_512_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_True_197_224_272_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_True_197_224_272_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_True_197_240_272_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_True_197_240_272_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_True_256_256_256_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_True_256_256_256_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_True_256_512_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_True_256_512_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_True_2_1024_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_True_2_1024_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_True_31_1024_64_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_True_31_1024_64_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_True_45_96_1024_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_True_45_96_1024_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_True_512_128_256_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_True_512_128_256_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_True_65_96_112_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_fast_accum_True_65_96_112_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_False_1023_64_48_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_False_1023_64_48_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_False_1025_128_96_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_False_1025_128_96_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_False_127_96_1024_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_False_127_96_1024_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_False_128_128_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_False_128_128_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_False_128_256_512_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_False_128_256_512_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_False_197_224_272_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_False_197_224_272_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_False_197_240_272_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_False_197_240_272_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_False_256_256_256_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_False_256_256_256_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_False_256_512_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_False_256_512_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_False_2_1024_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_False_2_1024_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_False_31_1024_64_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_False_31_1024_64_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_False_45_96_1024_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_False_45_96_1024_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_False_512_128_256_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_False_512_128_256_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_False_65_96_112_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_False_65_96_112_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_True_1023_64_48_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_True_1023_64_48_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_True_1025_128_96_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_True_1025_128_96_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_True_127_96_1024_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_True_127_96_1024_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_True_128_128_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_True_128_128_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_True_128_256_512_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_True_128_256_512_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_True_197_224_272_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_True_197_224_272_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_True_197_240_272_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_True_197_240_272_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_True_256_256_256_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_True_256_256_256_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_True_256_512_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_True_256_512_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_True_2_1024_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_True_2_1024_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_True_31_1024_64_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_True_31_1024_64_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_True_45_96_1024_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_True_45_96_1024_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_True_512_128_256_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_True_512_128_256_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_True_65_96_112_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_ones_modified_fast_accum_True_65_96_112_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_False_1023_64_48_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_False_1023_64_48_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_False_1025_128_96_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_False_1025_128_96_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_False_127_96_1024_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_False_127_96_1024_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_False_128_128_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_False_128_128_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_False_128_256_512_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_False_128_256_512_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_False_197_224_272_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_False_197_224_272_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_False_197_240_272_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_False_197_240_272_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_False_256_256_256_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_False_256_256_256_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_False_256_512_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_False_256_512_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_False_2_1024_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_False_2_1024_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_False_31_1024_64_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_False_31_1024_64_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_False_45_96_1024_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_False_45_96_1024_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_False_512_128_256_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_False_512_128_256_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_False_65_96_112_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_False_65_96_112_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_True_1023_64_48_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_True_1023_64_48_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_True_1025_128_96_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_True_1025_128_96_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_True_127_96_1024_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_True_127_96_1024_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_True_128_128_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_True_128_128_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_True_128_256_512_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_True_128_256_512_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_True_197_224_272_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_True_197_224_272_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_True_197_240_272_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_True_197_240_272_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_True_256_256_256_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_True_256_256_256_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_True_256_512_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_True_256_512_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_True_2_1024_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_True_2_1024_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_True_31_1024_64_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_True_31_1024_64_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_True_45_96_1024_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_True_45_96_1024_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_True_512_128_256_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_True_512_128_256_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_True_65_96_112_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_b_scale_modified_fast_accum_True_65_96_112_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_False_1023_64_48_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_False_1023_64_48_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_False_1025_128_96_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_False_1025_128_96_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_False_127_96_1024_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_False_127_96_1024_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_False_128_128_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_False_128_128_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_False_128_256_512_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_False_128_256_512_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_False_197_224_272_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_False_197_224_272_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_False_197_240_272_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_False_197_240_272_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_False_256_256_256_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_False_256_256_256_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_False_256_512_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_False_256_512_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_False_2_1024_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_False_2_1024_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_False_31_1024_64_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_False_31_1024_64_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_False_45_96_1024_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_False_45_96_1024_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_False_512_128_256_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_False_512_128_256_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_False_65_96_112_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_False_65_96_112_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_True_1023_64_48_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_True_1023_64_48_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_True_1025_128_96_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_True_1025_128_96_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_True_127_96_1024_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_True_127_96_1024_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_True_128_128_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_True_128_128_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_True_128_256_512_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_True_128_256_512_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_True_197_224_272_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_True_197_224_272_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_True_197_240_272_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_True_197_240_272_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_True_256_256_256_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_True_256_256_256_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_True_256_512_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_True_256_512_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_True_2_1024_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_True_2_1024_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_True_31_1024_64_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_True_31_1024_64_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_True_45_96_1024_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_True_45_96_1024_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_True_512_128_256_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_True_512_128_256_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_True_65_96_112_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_ones_modified_b_ones_fast_accum_True_65_96_112_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_False_1023_64_48_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_False_1023_64_48_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_False_1025_128_96_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_False_1025_128_96_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_False_127_96_1024_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_False_127_96_1024_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_False_128_128_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_False_128_128_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_False_128_256_512_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_False_128_256_512_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_False_197_224_272_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_False_197_224_272_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_False_197_240_272_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_False_197_240_272_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_False_256_256_256_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_False_256_256_256_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_False_256_512_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_False_256_512_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_False_2_1024_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_False_2_1024_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_False_31_1024_64_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_False_31_1024_64_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_False_45_96_1024_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_False_45_96_1024_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_False_512_128_256_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_False_512_128_256_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_False_65_96_112_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_False_65_96_112_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_True_1023_64_48_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_True_1023_64_48_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_True_1025_128_96_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_True_1025_128_96_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_True_127_96_1024_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_True_127_96_1024_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_True_128_128_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_True_128_128_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_True_128_256_512_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_True_128_256_512_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_True_197_224_272_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_True_197_224_272_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_True_197_240_272_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_True_197_240_272_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_True_256_256_256_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_True_256_256_256_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_True_256_512_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_True_256_512_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_True_2_1024_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_True_2_1024_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_True_31_1024_64_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_True_31_1024_64_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_True_45_96_1024_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_True_45_96_1024_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_True_512_128_256_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_True_512_128_256_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_True_65_96_112_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_a_scale_modified_b_ones_fast_accum_True_65_96_112_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_False_1023_64_48_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_False_1023_64_48_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_False_1025_128_96_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_False_1025_128_96_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_False_127_96_1024_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_False_127_96_1024_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_False_128_128_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_False_128_128_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_False_128_256_512_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_False_128_256_512_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_False_197_224_272_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_False_197_224_272_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_False_197_240_272_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_False_197_240_272_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_False_256_256_256_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_False_256_256_256_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_False_256_512_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_False_256_512_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_False_2_1024_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_False_2_1024_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_False_31_1024_64_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_False_31_1024_64_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_False_45_96_1024_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_False_45_96_1024_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_False_512_128_256_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_False_512_128_256_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_False_65_96_112_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_False_65_96_112_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_True_1023_64_48_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_True_1023_64_48_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_True_1025_128_96_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_True_1025_128_96_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_True_127_96_1024_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_True_127_96_1024_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_True_128_128_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_True_128_128_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_True_128_256_512_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_True_128_256_512_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_True_197_224_272_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_True_197_224_272_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_True_197_240_272_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_True_197_240_272_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_True_256_256_256_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_True_256_256_256_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_True_256_512_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_True_256_512_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_True_2_1024_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_True_2_1024_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_True_31_1024_64_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_True_31_1024_64_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_True_45_96_1024_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_True_45_96_1024_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_True_512_128_256_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_True_512_128_256_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_True_65_96_112_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_from_data_fast_accum_True_65_96_112_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_False_1023_64_48_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_False_1023_64_48_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_False_1025_128_96_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_False_1025_128_96_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_False_127_96_1024_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_False_127_96_1024_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_False_128_128_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_False_128_128_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_False_128_256_512_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_False_128_256_512_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_False_197_224_272_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_False_197_224_272_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_False_197_240_272_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_False_197_240_272_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_False_256_256_256_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_False_256_256_256_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_False_256_512_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_False_256_512_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_False_2_1024_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_False_2_1024_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_False_31_1024_64_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_False_31_1024_64_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_False_45_96_1024_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_False_45_96_1024_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_False_512_128_256_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_False_512_128_256_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_False_65_96_112_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_False_65_96_112_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_True_1023_64_48_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_True_1023_64_48_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_True_1025_128_96_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_True_1025_128_96_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_True_127_96_1024_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_True_127_96_1024_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_True_128_128_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_True_128_128_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_True_128_256_512_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_True_128_256_512_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_True_197_224_272_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_True_197_224_272_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_True_197_240_272_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_True_197_240_272_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_True_256_256_256_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_True_256_256_256_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_True_256_512_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_True_256_512_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_True_2_1024_128_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_True_2_1024_128_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_True_31_1024_64_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_True_31_1024_64_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_True_45_96_1024_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_True_45_96_1024_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_True_512_128_256_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_True_512_128_256_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_True_65_96_112_recipe_mxfp8_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_mxfp8_nvfp4_numerics_test_case_name_data_random_scales_one_fast_accum_True_65_96_112_recipe_nvfp4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_blockwise_nvfp4_compile_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_error_message_fp8_pre_sm89_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_float32_output_errors_with_bias_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_float8_basics_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_float8_bias_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_float8_bias_relu_edgecase_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_float8_error_messages_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_float8_rowwise_scaling_sanity_use_fast_accum_False_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_float8_rowwise_scaling_sanity_use_fast_accum_True_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_float8_scale_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_float8_scale_fast_accum_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_honor_sm_carveout_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_non_divisible_leading_dim_bias_False_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_non_divisible_leading_dim_bias_True_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_pack_uint4_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_scaled_grouped_gemm_2d_2d_fast_accum_False_strided_False_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_scaled_grouped_gemm_2d_2d_fast_accum_False_strided_True_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_scaled_grouped_gemm_2d_2d_fast_accum_True_strided_False_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_scaled_grouped_gemm_2d_2d_fast_accum_True_strided_True_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_scaled_grouped_gemm_2d_3d_fast_accum_False_strided_False_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_scaled_grouped_gemm_2d_3d_fast_accum_False_strided_True_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_scaled_grouped_gemm_2d_3d_fast_accum_True_strided_False_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_scaled_grouped_gemm_2d_3d_fast_accum_True_strided_True_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_scaled_grouped_gemm_3d_2d_fast_accum_False_strided_False_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_scaled_grouped_gemm_3d_2d_fast_accum_False_strided_True_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_scaled_grouped_gemm_3d_2d_fast_accum_True_strided_False_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_scaled_grouped_gemm_3d_2d_fast_accum_True_strided_True_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_scaled_grouped_gemm_3d_3d_fast_accum_False_strided_False_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_scaled_grouped_gemm_3d_3d_fast_accum_False_strided_True_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_scaled_grouped_gemm_3d_3d_fast_accum_True_strided_False_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_scaled_grouped_gemm_3d_3d_fast_accum_True_strided_True_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_scaled_mm_change_stride_bfloat16_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_scaled_mm_change_stride_float16_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_scaled_mm_change_stride_float32_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_scaled_mm_vs_emulated_bfloat16_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_scaled_mm_vs_emulated_float16_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_scaled_mm_vs_emulated_float32_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_scaled_mm_vs_emulated_row_wise_bfloat16_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_scaled_mm_vs_emulated_row_wise_float32_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_zero_dim_tensorwise_which_dim_zero_0_use_torch_compile_False_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_zero_dim_tensorwise_which_dim_zero_0_use_torch_compile_True_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_zero_dim_tensorwise_which_dim_zero_1_use_torch_compile_False_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_zero_dim_tensorwise_which_dim_zero_1_use_torch_compile_True_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_zero_dim_tensorwise_which_dim_zero_2_use_torch_compile_False_cuda, test/test_matmul_cuda.py::TestFP8MatmulCUDA::test_zero_dim_tensorwise_which_dim_zero_2_use_torch_compile_True_cuda 2025-07-17T10:33:18.1662132Z 2025-07-17T10:33:18.1662297Z Running xpu/test_gemm 1/1 ... [2025-07-17 10:33:18.074260] 2025-07-17T10:33:18.1662573Z SCRIBE_GRAPHQL_ACCESS_TOKEN is NOT set 2025-07-17T10:33:18.1663238Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'xpu/test_gemm.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 10:33:18.074594] 2025-07-17T10:33:21.7888554Z 2025-07-17T10:33:21.7889562Z xpu/test_gemm 1/1 was successful, full logs can be found in artifacts with path test/test-reports/xpu.test_gemm_1.1_47f51fd02163eff0_.log 2025-07-17T10:33:21.7890043Z Running 0 items in this shard: 2025-07-17T10:33:21.7890189Z 2025-07-17T10:42:09.3459873Z 2025-07-17T10:42:09.3461194Z test_ops_fwd_gradients 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_fwd_gradients_1.1_be432eaf5f6dc7fd_.log 2025-07-17T10:42:09.4243200Z Running 3189 items in this shard: test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_H_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_H_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_T_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_T_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad___getitem___cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad___getitem___cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad___radd___cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad___radd___cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad___rdiv___cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad___rdiv___cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad___rmatmul___cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad___rmatmul___cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad___rmod___cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad___rmul___cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad___rmul___cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad___rpow___cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad___rpow___cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad___rsub___cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad___rsub___cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad__batch_norm_with_update_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad__chunk_cat_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad__chunk_cat_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad__native_batch_norm_legit_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad__segment_reduce_lengths_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad__segment_reduce_offsets_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad__softmax_backward_data_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad__unsafe_masked_index_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad__unsafe_masked_index_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad__unsafe_masked_index_put_accumulate_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad__unsafe_masked_index_put_accumulate_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad__upsample_bilinear2d_aa_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_abs_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_abs_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_acos_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_acos_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_acosh_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_acosh_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_add_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_add_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_addbmm_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_addbmm_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_addcdiv_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_addcdiv_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_addcmul_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_addcmul_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_addmm_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_addmm_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_addmm_decomposed_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_addmm_decomposed_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_addmv_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_addmv_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_addr_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_addr_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_alias_copy_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_alias_copy_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_all_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_all_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_allclose_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_allclose_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_amax_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_amin_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_aminmax_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_angle_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_angle_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_any_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_any_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_arange_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_argmax_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_argmin_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_argsort_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_argwhere_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_argwhere_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_as_strided_copy_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_as_strided_copy_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_as_strided_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_as_strided_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_as_strided_partial_views_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_as_strided_partial_views_cuda_float64, 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test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_atanh_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_atleast_1d_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_atleast_1d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_atleast_2d_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_atleast_2d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_atleast_3d_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_atleast_3d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_baddbmm_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_fn_fwgrad_bwgrad_baddbmm_cuda_float64, 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test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_maximum_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_mean_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_mean_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_median_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_meshgrid_list_of_tensors_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_meshgrid_list_of_tensors_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_meshgrid_variadic_tensors_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_meshgrid_variadic_tensors_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_min_binary_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_min_reduction_no_dim_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_min_reduction_with_dim_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_minimum_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_mm_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_mm_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_mode_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_movedim_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_movedim_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_msort_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_mul_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_mul_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_multinomial_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_mv_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_mv_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_mvlgamma_mvlgamma_p_1_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_mvlgamma_mvlgamma_p_3_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_mvlgamma_mvlgamma_p_5_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nan_to_num_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nanmean_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nanmean_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nanmedian_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nanquantile_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nansum_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nansum_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_narrow_copy_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_narrow_copy_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_narrow_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_narrow_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_native_batch_norm_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_native_dropout_backward_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_native_layer_norm_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_ne_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_ne_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_neg_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_neg_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_new_empty_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_new_empty_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_new_empty_strided_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_new_empty_strided_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_new_full_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_new_full_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_new_ones_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_new_ones_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_new_zeros_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_new_zeros_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nextafter_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_adaptive_avg_pool1d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_adaptive_avg_pool2d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_adaptive_avg_pool3d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_adaptive_max_pool1d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_adaptive_max_pool2d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_adaptive_max_pool3d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_alpha_dropout_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_avg_pool1d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_avg_pool2d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_avg_pool3d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_batch_norm_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_batch_norm_without_cudnn_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_bilinear_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_binary_cross_entropy_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_binary_cross_entropy_with_logits_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_celu_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_channel_shuffle_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_channel_shuffle_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_conv1d_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_conv1d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_conv2d_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_conv2d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_conv3d_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_conv3d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_conv_transpose1d_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_conv_transpose1d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_conv_transpose2d_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_conv_transpose2d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_conv_transpose3d_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_conv_transpose3d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_cosine_embedding_loss_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_cosine_similarity_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_cross_entropy_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_ctc_loss_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_dropout2d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_dropout3d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_dropout_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_elu_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_embedding_bag_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_embedding_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_feature_alpha_dropout_with_train_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_feature_alpha_dropout_without_train_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_feature_alpha_dropout_without_train_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_fractional_max_pool2d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_fractional_max_pool3d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_gaussian_nll_loss_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_gelu_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_glu_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_grid_sample_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_group_norm_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_hardshrink_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_hardsigmoid_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_hardswish_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_hardtanh_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_hinge_embedding_loss_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_huber_loss_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_instance_norm_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_interpolate_area_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_interpolate_bicubic_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_interpolate_bilinear_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_interpolate_linear_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_interpolate_nearest-exact_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_interpolate_nearest_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_interpolate_trilinear_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_kl_div_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_l1_loss_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_l1_loss_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_layer_norm_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_leaky_relu_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_linear_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_linear_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_local_response_norm_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_logsigmoid_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_margin_ranking_loss_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_max_pool1d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_max_pool2d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_max_pool3d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_max_unpool1d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_max_unpool1d_grad_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_max_unpool2d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_max_unpool2d_grad_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_max_unpool3d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_max_unpool3d_grad_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_mish_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_mse_loss_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_multi_head_attention_forward_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_multi_margin_loss_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_multilabel_margin_loss_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_multilabel_soft_margin_loss_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_nll_loss_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_normalize_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_normalize_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_pad_circular_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_pad_circular_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_pad_constant_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_pad_constant_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_pad_reflect_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_pad_reflect_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_pad_replicate_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_pad_replicate_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_pad_replicate_negative_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_pad_replicate_negative_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_pairwise_distance_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_pairwise_distance_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_pdist_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_pixel_shuffle_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_pixel_shuffle_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_pixel_unshuffle_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_pixel_unshuffle_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_poisson_nll_loss_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_prelu_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_relu6_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_relu_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_rms_norm_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_rms_norm_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_rrelu_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_scaled_dot_product_attention_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_selu_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_silu_complex_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_silu_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_smooth_l1_loss_cuda_float64, 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test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_tanhshrink_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_tanhshrink_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_threshold_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_triplet_margin_loss_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_triplet_margin_loss_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_triplet_margin_with_distance_loss_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_triplet_margin_with_distance_loss_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_unfold_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_unfold_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_upsample_bilinear_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nn_functional_upsample_nearest_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nonzero_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nonzero_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nonzero_static_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_nonzero_static_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_norm_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_norm_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_norm_fro_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_norm_fro_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_norm_inf_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_norm_inf_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_norm_nuc_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_norm_nuc_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_normal_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_normal_in_place_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_normal_in_place_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_normal_number_mean_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_ones_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_ones_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_ones_like_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_ones_like_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_ormqr_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_ormqr_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_outer_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_outer_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_pca_lowrank_cuda_complex128, 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test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_round_decimals_neg_3_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_rsqrt_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_rsqrt_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_rsub_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_rsub_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_scalar_tensor_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_scalar_tensor_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_scatter_add_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_forward_mode_AD_scatter_add_cuda_float64, 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test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD__segment_reduce_lengths_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD__segment_reduce_offsets_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD__softmax_backward_data_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD__unsafe_masked_index_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD__unsafe_masked_index_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD__unsafe_masked_index_put_accumulate_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD__unsafe_masked_index_put_accumulate_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD__upsample_bilinear2d_aa_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_abs_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_abs_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_acos_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_acos_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_acosh_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_acosh_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_add_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_add_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_addbmm_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_addbmm_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_addcdiv_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_addcdiv_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_addcmul_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_addcmul_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_addmm_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_addmm_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_addmm_decomposed_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_addmm_decomposed_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_addmv_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_addmv_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_addr_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_addr_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_alias_copy_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_alias_copy_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_all_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_all_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_allclose_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_allclose_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_amax_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_amin_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_aminmax_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_angle_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_angle_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_any_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_any_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_arange_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_argmax_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_argmin_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_argsort_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_argwhere_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_argwhere_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_as_strided_copy_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_as_strided_copy_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_as_strided_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_as_strided_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_as_strided_partial_views_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_as_strided_partial_views_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_as_strided_scatter_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_as_strided_scatter_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_asin_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_asin_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_asinh_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_asinh_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_atan2_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_atan_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_atan_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_atanh_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_atanh_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_atleast_1d_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_atleast_1d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_atleast_2d_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_atleast_2d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_atleast_3d_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_atleast_3d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_baddbmm_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_baddbmm_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_bernoulli_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_bfloat16_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_bfloat16_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_block_diag_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_block_diag_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_bmm_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_bmm_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_bool_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_bool_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_broadcast_tensors_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_broadcast_tensors_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_broadcast_to_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_broadcast_to_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_bucketize_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_byte_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_byte_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_cartesian_prod_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_cartesian_prod_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_cat_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_cat_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_cauchy_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_cdist_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_cdouble_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_cdouble_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_ceil_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_cfloat_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_cfloat_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_chalf_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_chalf_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_char_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_char_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_cholesky_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_cholesky_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_cholesky_inverse_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_cholesky_inverse_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_cholesky_solve_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_cholesky_solve_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_chunk_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_chunk_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_clamp_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_clamp_max_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_clamp_min_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_clone_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_clone_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_column_stack_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_column_stack_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_combinations_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_combinations_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_complex_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_conj_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_conj_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_conj_physical_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_conj_physical_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_constant_pad_nd_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_constant_pad_nd_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_contiguous_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_contiguous_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_copysign_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_corrcoef_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_corrcoef_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_cos_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_cos_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_cosh_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_cosh_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_count_nonzero_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_count_nonzero_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_cov_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_cov_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_cross_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_cross_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_cummax_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_cummin_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_cumprod_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_cumprod_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_cumsum_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_cumsum_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_cumulative_trapezoid_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_cumulative_trapezoid_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_deg2rad_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_diag_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_diag_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_diag_embed_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_diag_embed_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_diagflat_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_diagflat_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_diagonal_copy_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_diagonal_copy_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_diagonal_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_diagonal_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_diagonal_scatter_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_diagonal_scatter_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_diff_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_diff_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_digamma_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_dist_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_dist_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_div_floor_rounding_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_div_no_rounding_mode_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_div_no_rounding_mode_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_div_trunc_rounding_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_dot_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_dot_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_double_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_double_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_dsplit_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_dsplit_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_dstack_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_dstack_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_einsum_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_einsum_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_empty_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_empty_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_empty_like_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_empty_like_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_empty_permuted_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_empty_permuted_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_empty_strided_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_empty_strided_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_eq_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_eq_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_equal_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_equal_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_erf_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_erfc_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_erfinv_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_exp2_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_exp2_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_exp_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_exp_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_expand_as_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_expand_as_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_expand_copy_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_expand_copy_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_expand_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_expand_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_expm1_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_expm1_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_exponential_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_eye_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_eye_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_fft2_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_fft2_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_fft_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_fft_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_fftn_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_fftn_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_fftshift_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_fftshift_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_hfft2_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_hfft2_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_hfft_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_hfft_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_hfftn_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_hfftn_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_ifft2_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_ifft2_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_ifft_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_ifft_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_ifftn_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_ifftn_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_ifftshift_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_ifftshift_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_ihfft2_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_ihfft_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_ihfftn_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_irfft2_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_irfft2_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_irfft_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_irfft_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_irfftn_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_irfftn_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_rfft2_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_rfft_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fft_rfftn_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fill_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fill_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_flatten_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_flatten_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_flip_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_flip_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fliplr_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fliplr_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_flipud_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_flipud_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_float_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_float_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_float_power_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_float_power_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_floor_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_floor_divide_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fmax_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fmin_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_fmod_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_frac_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_frexp_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_full_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_full_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_full_like_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_full_like_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_gather_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_gather_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_ge_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_geometric_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_geqrf_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_geqrf_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_gradient_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_gradient_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_grid_sampler_2d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_gt_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_half_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_half_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_heaviside_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_histc_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_hsplit_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_hsplit_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_hstack_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_hstack_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_hypot_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_i0_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_igamma_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_igammac_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_imag_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_index_add_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_index_add_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_index_copy_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_index_copy_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_index_fill_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_index_fill_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_index_put_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_index_put_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_index_reduce_amax_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_index_reduce_amin_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_index_reduce_mean_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_index_reduce_prod_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_index_select_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_index_select_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_inner_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_inner_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_int_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_int_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_isclose_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_isclose_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_isfinite_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_isfinite_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_isin_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_isinf_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_isinf_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_isnan_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_isnan_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_isneginf_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_isposinf_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_isreal_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_isreal_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_istft_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_item_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_item_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_jiterator_2inputs_2outputs_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_jiterator_2inputs_2outputs_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_jiterator_4inputs_with_extra_args_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_jiterator_4inputs_with_extra_args_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_jiterator_binary_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_jiterator_binary_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_jiterator_binary_return_by_ref_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_jiterator_binary_return_by_ref_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_jiterator_unary_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_jiterator_unary_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_kron_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_kron_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_kthvalue_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_ldexp_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_ldexp_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_le_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_lerp_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_lerp_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_lgamma_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_cholesky_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_cholesky_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_cholesky_ex_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_cholesky_ex_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_cond_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_cond_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_cross_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_cross_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_det_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_det_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_diagonal_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_diagonal_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_eig_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_eig_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_eigh_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_eigh_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_eigvals_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_eigvals_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_eigvalsh_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_eigvalsh_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_householder_product_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_householder_product_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_inv_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_inv_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_inv_ex_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_inv_ex_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_ldl_factor_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_ldl_factor_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_ldl_factor_ex_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_ldl_factor_ex_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_ldl_solve_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_ldl_solve_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_lstsq_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_lstsq_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_lstsq_grad_oriented_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_lstsq_grad_oriented_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_lu_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_lu_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_lu_factor_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_lu_factor_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_lu_factor_ex_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_lu_factor_ex_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_lu_solve_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_lu_solve_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_matrix_norm_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_matrix_norm_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_matrix_power_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_matrix_power_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_matrix_rank_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_matrix_rank_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_matrix_rank_hermitian_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_matrix_rank_hermitian_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_multi_dot_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_multi_dot_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_norm_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_norm_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_norm_subgradients_at_zero_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_norm_subgradients_at_zero_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_pinv_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_pinv_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_pinv_hermitian_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_pinv_hermitian_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_pinv_singular_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_pinv_singular_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_qr_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_qr_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_slogdet_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_slogdet_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_solve_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_solve_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_solve_ex_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_solve_ex_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_solve_triangular_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_solve_triangular_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_svd_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_svd_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_svdvals_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_svdvals_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_tensorinv_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_tensorinv_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_tensorsolve_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_tensorsolve_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_vander_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_vander_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_vecdot_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_vecdot_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_vector_norm_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linalg_vector_norm_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linspace_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linspace_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linspace_tensor_overload_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_linspace_tensor_overload_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_log10_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_log10_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_log1p_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_log1p_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_log2_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_log2_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_log_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_log_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_log_normal_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_log_softmax_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_log_softmax_with_dtype_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_log_softmax_with_dtype_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_logaddexp2_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_logaddexp_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_logcumsumexp_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_logcumsumexp_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_logdet_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_logdet_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_logical_and_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_logical_and_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_logical_not_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_logical_not_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_logical_or_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_logical_or_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_logical_xor_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_logical_xor_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_logit_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_logspace_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_logspace_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_logspace_tensor_overload_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_logspace_tensor_overload_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_logsumexp_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_logsumexp_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_long_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_long_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_lt_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_lu_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_lu_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_lu_solve_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_lu_solve_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_lu_unpack_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_lu_unpack_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_mH_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_mH_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_mT_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_mT_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_amax_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_amin_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_argmax_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_argmin_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_cumprod_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_cumprod_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_cumsum_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_cumsum_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_fill_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_fill_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_log_softmax_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_logaddexp_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_logsumexp_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_logsumexp_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_mean_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_mean_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_median_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_norm_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_normalize_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_normalize_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_prod_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_prod_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_scatter_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_scatter_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_select_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_select_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_softmax_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_softmin_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_std_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_std_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_sum_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_sum_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_var_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_masked_var_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_matmul_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_matmul_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_matrix_exp_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_matrix_exp_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_max_binary_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_max_pool2d_with_indices_backward_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_max_reduction_no_dim_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_max_reduction_with_dim_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_maximum_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_mean_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_mean_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_median_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_meshgrid_list_of_tensors_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_meshgrid_list_of_tensors_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_meshgrid_variadic_tensors_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_meshgrid_variadic_tensors_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_min_binary_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_min_reduction_no_dim_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_min_reduction_with_dim_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_minimum_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_mm_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_mm_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_mode_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_movedim_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_movedim_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_msort_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_mul_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_mul_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_multinomial_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_mv_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_mv_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_mvlgamma_mvlgamma_p_1_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_mvlgamma_mvlgamma_p_3_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_mvlgamma_mvlgamma_p_5_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nan_to_num_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nanmean_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nanmean_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nanmedian_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nanquantile_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nansum_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nansum_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_narrow_copy_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_narrow_copy_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_narrow_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_narrow_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_native_batch_norm_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_native_dropout_backward_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_native_layer_norm_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_ne_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_ne_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_neg_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_neg_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_new_empty_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_new_empty_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_new_empty_strided_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_new_empty_strided_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_new_full_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_new_full_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_new_ones_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_new_ones_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_new_zeros_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_new_zeros_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nextafter_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_adaptive_avg_pool1d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_adaptive_avg_pool2d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_adaptive_avg_pool3d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_adaptive_max_pool1d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_adaptive_max_pool2d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_adaptive_max_pool3d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_alpha_dropout_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_avg_pool1d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_avg_pool2d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_avg_pool3d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_batch_norm_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_batch_norm_without_cudnn_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_bilinear_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_binary_cross_entropy_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_binary_cross_entropy_with_logits_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_celu_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_channel_shuffle_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_channel_shuffle_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_conv1d_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_conv1d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_conv2d_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_conv2d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_conv3d_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_conv3d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_conv_transpose1d_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_conv_transpose1d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_conv_transpose2d_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_conv_transpose2d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_conv_transpose3d_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_conv_transpose3d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_cosine_embedding_loss_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_cosine_similarity_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_cross_entropy_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_ctc_loss_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_dropout2d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_dropout3d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_dropout_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_elu_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_embedding_bag_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_embedding_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_feature_alpha_dropout_with_train_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_feature_alpha_dropout_without_train_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_feature_alpha_dropout_without_train_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_fractional_max_pool2d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_fractional_max_pool3d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_gaussian_nll_loss_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_gelu_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_glu_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_grid_sample_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_group_norm_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_hardshrink_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_hardsigmoid_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_hardswish_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_hardtanh_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_hinge_embedding_loss_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_huber_loss_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_instance_norm_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_interpolate_area_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_interpolate_bicubic_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_interpolate_bilinear_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_interpolate_linear_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_interpolate_nearest-exact_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_interpolate_nearest_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_interpolate_trilinear_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_kl_div_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_l1_loss_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_l1_loss_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_layer_norm_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_leaky_relu_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_linear_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_linear_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_local_response_norm_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_logsigmoid_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_margin_ranking_loss_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_max_pool1d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_max_pool2d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_max_pool3d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_max_unpool1d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_max_unpool1d_grad_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_max_unpool2d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_max_unpool2d_grad_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_max_unpool3d_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_max_unpool3d_grad_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_mish_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_mse_loss_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_multi_head_attention_forward_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_multi_margin_loss_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_multilabel_margin_loss_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_multilabel_soft_margin_loss_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_nll_loss_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_normalize_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_normalize_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_pad_circular_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_pad_circular_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_pad_constant_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_pad_constant_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_pad_reflect_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_pad_reflect_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_pad_replicate_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_pad_replicate_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_pad_replicate_negative_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_pad_replicate_negative_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_pairwise_distance_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_pairwise_distance_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_pdist_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_pixel_shuffle_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_pixel_shuffle_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_pixel_unshuffle_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_pixel_unshuffle_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_poisson_nll_loss_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_prelu_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_relu6_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_relu_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_rms_norm_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_rms_norm_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_rrelu_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_scaled_dot_product_attention_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_selu_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_silu_complex_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_silu_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_smooth_l1_loss_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_soft_margin_loss_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_softmin_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_softmin_with_dtype_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_softmin_with_dtype_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_softplus_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_softshrink_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_softsign_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_softsign_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_tanhshrink_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_tanhshrink_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_threshold_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_triplet_margin_loss_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_triplet_margin_loss_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_triplet_margin_with_distance_loss_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_triplet_margin_with_distance_loss_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_unfold_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_unfold_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_upsample_bilinear_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nn_functional_upsample_nearest_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nonzero_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nonzero_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nonzero_static_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_nonzero_static_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_norm_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_norm_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_norm_fro_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_norm_fro_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_norm_inf_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_norm_inf_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_norm_nuc_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_norm_nuc_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_normal_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_normal_in_place_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_normal_in_place_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_normal_number_mean_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_ones_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_ones_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_ones_like_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_ones_like_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_ormqr_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_ormqr_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_outer_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_outer_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_pca_lowrank_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_pca_lowrank_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_permute_copy_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_permute_copy_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_permute_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_permute_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_pinverse_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_pinverse_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_polar_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_polygamma_polygamma_n_0_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_polygamma_polygamma_n_1_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_polygamma_polygamma_n_2_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_polygamma_polygamma_n_3_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_polygamma_polygamma_n_4_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_positive_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_positive_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_pow_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_pow_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_prod_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_prod_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_put_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_put_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_qr_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_qr_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_quantile_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_rad2deg_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_rand_like_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_rand_like_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_randint_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_randint_like_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_randn_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_randn_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_randn_like_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_randn_like_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_ravel_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_ravel_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_real_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_real_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_reciprocal_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_reciprocal_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_remainder_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_renorm_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_renorm_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_repeat_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_repeat_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_repeat_interleave_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_repeat_interleave_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_reshape_as_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_reshape_as_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_reshape_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_reshape_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_resize__cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_resize__cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_resize_as__cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_resize_as__cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_resolve_conj_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_resolve_conj_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_resolve_neg_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_resolve_neg_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_roll_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_roll_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_rot90_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_rot90_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_round_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_round_decimals_0_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_round_decimals_3_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_round_decimals_neg_3_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_rsqrt_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_rsqrt_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_rsub_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_rsub_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_scalar_tensor_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_scalar_tensor_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_scatter_add_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_scatter_add_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_scatter_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_scatter_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_scatter_reduce_amax_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_scatter_reduce_amin_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_scatter_reduce_mean_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_scatter_reduce_prod_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_scatter_reduce_sum_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_searchsorted_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_select_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_select_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_select_scatter_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_sgn_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_sgn_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_short_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_short_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_sigmoid_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_sigmoid_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_sign_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_signal_windows_bartlett_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_signal_windows_blackman_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_signal_windows_cosine_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_signal_windows_exponential_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_signal_windows_gaussian_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_signal_windows_general_cosine_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_signal_windows_general_hamming_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_signal_windows_hamming_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_signal_windows_hann_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_signal_windows_kaiser_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_signal_windows_nuttall_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_signbit_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_sin_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_sin_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_sinc_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_sinc_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_sinh_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_sinh_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_slice_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_slice_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_slice_scatter_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_softmax_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_softmax_with_dtype_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_softmax_with_dtype_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_sort_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_sparse_mm_reduce_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_sparse_sampled_addmm_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_sparse_sampled_addmm_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_airy_ai_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_bessel_j0_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_bessel_j1_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_bessel_y0_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_bessel_y1_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_chebyshev_polynomial_t_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_chebyshev_polynomial_u_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_chebyshev_polynomial_v_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_chebyshev_polynomial_w_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_entr_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_erfcx_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_hermite_polynomial_h_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_hermite_polynomial_he_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_i0e_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_i1_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_i1e_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_laguerre_polynomial_l_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_legendre_polynomial_p_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_log_ndtr_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_modified_bessel_i0_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_modified_bessel_i1_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_modified_bessel_k0_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_modified_bessel_k1_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_ndtr_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_ndtri_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_polygamma_special_polygamma_n_0_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_scaled_modified_bessel_k0_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_scaled_modified_bessel_k1_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_shifted_chebyshev_polynomial_t_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_shifted_chebyshev_polynomial_u_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_shifted_chebyshev_polynomial_v_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_shifted_chebyshev_polynomial_w_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_spherical_bessel_j0_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_xlog1py_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_special_zeta_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_split_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_split_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_split_list_args_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_split_list_args_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_split_with_sizes_copy_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_split_with_sizes_copy_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_split_with_sizes_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_split_with_sizes_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_sqrt_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_sqrt_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_square_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_square_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_squeeze_copy_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_squeeze_copy_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_squeeze_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_squeeze_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_squeeze_multiple_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_squeeze_multiple_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_stack_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_stack_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_std_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_std_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_std_mean_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_std_mean_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_std_mean_unbiased_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_std_mean_unbiased_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_std_unbiased_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_std_unbiased_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_stft_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_stft_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_sub_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_sub_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_sum_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_sum_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_sum_to_size_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_sum_to_size_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_svd_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_svd_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_svd_lowrank_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_svd_lowrank_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_t_copy_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_t_copy_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_t_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_t_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_take_along_dim_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_take_along_dim_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_take_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_take_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_tan_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_tan_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_tanh_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_tanh_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_tensor_split_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_tensor_split_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_tensordot_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_tensordot_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_tile_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_tile_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_to_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_to_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_to_sparse_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_to_sparse_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_topk_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_torch_ops_aten__safe_softmax_default_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_trace_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_trace_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_transpose_copy_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_transpose_copy_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_transpose_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_transpose_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_trapezoid_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_trapezoid_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_trapz_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_trapz_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_triangular_solve_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_triangular_solve_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_tril_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_tril_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_triu_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_triu_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_true_divide_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_true_divide_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_trunc_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_unbind_copy_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_unbind_copy_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_unbind_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_unbind_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_unflatten_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_unflatten_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_unfold_copy_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_unfold_copy_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_unfold_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_unfold_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_uniform_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_uniform_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_unique_consecutive_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_unique_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_unsafe_chunk_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_unsafe_chunk_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_unsafe_split_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_unsafe_split_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_unsqueeze_copy_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_unsqueeze_copy_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_unsqueeze_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_unsqueeze_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_var_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_var_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_var_mean_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_var_mean_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_var_mean_unbiased_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_var_mean_unbiased_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_var_unbiased_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_var_unbiased_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_vdot_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_vdot_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_view_as_complex_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_view_as_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_view_as_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_view_as_real_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_view_copy_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_view_copy_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_view_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_view_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_vsplit_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_vsplit_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_vstack_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_vstack_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_where_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_where_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_xlogy_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_zero__cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_zero__cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_zeros_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_zeros_cuda_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_zeros_like_cuda_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCUDA::test_inplace_forward_mode_AD_zeros_like_cuda_float64 2025-07-17T10:42:09.4990821Z 2025-07-17T10:42:10.0248779Z Running test batch 'tests to run' cost 7111.97 seconds 2025-07-17T10:42:10.0257115Z Emiting td_test_failure_stats_v2 2025-07-17T10:42:10.0261514Z Writing 1 documents to S3 ossci-raw-job-status/ossci_uploaded_metrics/td_test_failure_stats_v2_1752748930_b10d21e462fa11f08566e20a74e71ef7 2025-07-17T10:42:10.1960455Z /var/lib/jenkins/pytorch/tools/stats/upload_metrics.py:156: UserWarning: Error uploading metric td_test_failure_stats_v2 to DynamoDB: Unable to locate credentials 2025-07-17T10:42:10.1961099Z warn(f"Error uploading metric {metric_name} to DynamoDB: {e}") 2025-07-17T10:42:10.1961368Z inductor/test_max_autotune 1/2 failed! 2025-07-17T10:42:11.2129702Z 2025-07-17T10:42:11.2130571Z real 118m36.259s 2025-07-17T10:42:11.2130790Z user 338m18.020s 2025-07-17T10:42:11.2130958Z sys 155m39.798s 2025-07-17T10:42:11.2131118Z + sccache_epilogue 2025-07-17T10:42:11.2131345Z + echo '::group::Sccache Compilation Log' 2025-07-17T10:42:11.2132419Z ##[group]Sccache Compilation Log 2025-07-17T10:42:11.2132668Z + echo '=================== sccache compilation log ===================' 2025-07-17T10:42:11.2132943Z =================== sccache compilation log =================== 2025-07-17T10:42:11.2133341Z + python /var/lib/jenkins/pytorch/.ci/pytorch/print_sccache_log.py /var/lib/jenkins/sccache_error.log 2025-07-17T10:42:11.2263651Z + echo '=========== If your build fails, please take a look at the log above for possible reasons ===========' 2025-07-17T10:42:11.2264151Z =========== If your build fails, please take a look at the log above for possible reasons =========== 2025-07-17T10:42:11.2264475Z + sccache --show-stats 2025-07-17T10:42:11.2303955Z Compile requests 4354 2025-07-17T10:42:11.2304210Z Compile requests executed 171 2025-07-17T10:42:11.2304435Z Cache hits 50 2025-07-17T10:42:11.2304638Z Cache hits (C/C++) 44 2025-07-17T10:42:11.2304850Z Cache hits (HIP) 6 2025-07-17T10:42:11.2305040Z Cache misses 117 2025-07-17T10:42:11.2305228Z Cache misses (C/C++) 103 2025-07-17T10:42:11.2305538Z Cache misses (HIP) 14 2025-07-17T10:42:11.2305740Z Cache hits rate 29.94 % 2025-07-17T10:42:11.2305956Z Cache hits rate (C/C++) 29.93 % 2025-07-17T10:42:11.2306168Z Cache hits rate (HIP) 30.00 % 2025-07-17T10:42:11.2306604Z Cache timeouts 0 2025-07-17T10:42:11.2306822Z Cache read errors 0 2025-07-17T10:42:11.2307027Z Forced recaches 0 2025-07-17T10:42:11.2307239Z Cache write errors 0 2025-07-17T10:42:11.2307435Z Cache errors 0 2025-07-17T10:42:11.2307618Z Compilations 117 2025-07-17T10:42:11.2307808Z Compilation failures 4 2025-07-17T10:42:11.2308007Z Non-cacheable compilations 0 2025-07-17T10:42:11.2308208Z Non-cacheable calls 8 2025-07-17T10:42:11.2308402Z Non-compilation calls 4175 2025-07-17T10:42:11.2308595Z Unsupported compiler calls 0 2025-07-17T10:42:11.2308803Z Average cache write 0.000 s 2025-07-17T10:42:11.2309021Z Average compiler 9.684 s 2025-07-17T10:42:11.2309369Z Average cache read hit 0.000 s 2025-07-17T10:42:11.2309585Z Failed distributed compilations 0 2025-07-17T10:42:11.2309719Z 2025-07-17T10:42:11.2309796Z Non-cacheable reasons: 2025-07-17T10:42:11.2309970Z -E 7 2025-07-17T10:42:11.2310180Z unknown source language 1 2025-07-17T10:42:11.2310315Z 2025-07-17T10:42:11.2310444Z Cache location Local disk: "/var/lib/jenkins/.cache/sccache" 2025-07-17T10:42:11.2310719Z Use direct/preprocessor mode? yes 2025-07-17T10:42:11.2310934Z Version (client) 0.10.0 2025-07-17T10:42:11.2311135Z Cache size 52 MiB 2025-07-17T10:42:11.2311345Z Max cache size 10 GiB 2025-07-17T10:42:11.2311572Z + sccache --stop-server 2025-07-17T10:42:11.2334652Z Stopping sccache server... 2025-07-17T10:42:11.2337195Z Compile requests 4354 2025-07-17T10:42:11.2337425Z Compile requests executed 171 2025-07-17T10:42:11.2337640Z Cache hits 50 2025-07-17T10:42:11.2337864Z Cache hits (C/C++) 44 2025-07-17T10:42:11.2338070Z Cache hits (HIP) 6 2025-07-17T10:42:11.2338269Z Cache misses 117 2025-07-17T10:42:11.2338470Z Cache misses (C/C++) 103 2025-07-17T10:42:11.2338666Z Cache misses (HIP) 14 2025-07-17T10:42:11.2338864Z Cache hits rate 29.94 % 2025-07-17T10:42:11.2339070Z Cache hits rate (C/C++) 29.93 % 2025-07-17T10:42:11.2339262Z Cache hits rate (HIP) 30.00 % 2025-07-17T10:42:11.2339459Z Cache timeouts 0 2025-07-17T10:42:11.2339860Z Cache read errors 0 2025-07-17T10:42:11.2340103Z Forced recaches 0 2025-07-17T10:42:11.2340293Z Cache write errors 0 2025-07-17T10:42:11.2340480Z Cache errors 0 2025-07-17T10:42:11.2340672Z Compilations 117 2025-07-17T10:42:11.2340867Z Compilation failures 4 2025-07-17T10:42:11.2341065Z Non-cacheable compilations 0 2025-07-17T10:42:11.2341263Z Non-cacheable calls 8 2025-07-17T10:42:11.2341463Z Non-compilation calls 4175 2025-07-17T10:42:11.2341656Z Unsupported compiler calls 0 2025-07-17T10:42:11.2341947Z Average cache write 0.000 s 2025-07-17T10:42:11.2342148Z Average compiler 9.684 s 2025-07-17T10:42:11.2342347Z Average cache read hit 0.000 s 2025-07-17T10:42:11.2342552Z Failed distributed compilations 0 2025-07-17T10:42:11.2342690Z 2025-07-17T10:42:11.2342763Z Non-cacheable reasons: 2025-07-17T10:42:11.2342958Z -E 7 2025-07-17T10:42:11.2343165Z unknown source language 1 2025-07-17T10:42:11.2343288Z 2025-07-17T10:42:11.2343424Z Cache location Local disk: "/var/lib/jenkins/.cache/sccache" 2025-07-17T10:42:11.2343696Z Use direct/preprocessor mode? yes 2025-07-17T10:42:11.2343903Z Version (client) 0.10.0 2025-07-17T10:42:11.2344169Z Cache size 52 MiB 2025-07-17T10:42:11.2344380Z Max cache size 10 GiB 2025-07-17T10:42:11.2344594Z + echo ::endgroup:: 2025-07-17T10:42:11.2345155Z ##[endgroup] 2025-07-17T10:42:11.2403356Z ##[error]Process completed with exit code 1. 2025-07-17T10:42:11.2434198Z ##[group]Run # copy test results back to the mounted workspace, needed sudo, resulting permissions were correct 2025-07-17T10:42:11.2434706Z # copy test results back to the mounted workspace, needed sudo, resulting permissions were correct 2025-07-17T10:42:11.2435344Z docker exec -t "be5eba77e402a727f4714ddb7ab9e94c4cfe9ef82df6800750eddceb7a771559" sh -c "cd ../pytorch && sudo cp -R test/test-reports ../workspace/test" 2025-07-17T10:42:11.2447229Z shell: /usr/bin/bash -e {0} 2025-07-17T10:42:11.2447420Z env: 2025-07-17T10:42:11.2447573Z GIT_DEFAULT_BRANCH: main 2025-07-17T10:42:11.2447803Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T10:42:11.2448101Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T10:42:11.2448372Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T10:42:11.2449090Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T10:42:11.2449748Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T10:42:11.2449940Z AWS_REGION: us-east-1 2025-07-17T10:42:11.2450174Z AWS_ACCESS_KEY_ID: *** 2025-07-17T10:42:11.2450427Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T10:42:11.2454387Z AWS_SESSION_TOKEN: *** 2025-07-17T10:42:11.2454675Z CONTAINER_NAME: be5eba77e402a727f4714ddb7ab9e94c4cfe9ef82df6800750eddceb7a771559 2025-07-17T10:42:11.2454974Z ##[endgroup] 2025-07-17T10:42:11.3548886Z ##[group]Run docker exec -t "be5eba77e402a727f4714ddb7ab9e94c4cfe9ef82df6800750eddceb7a771559" sh -c "sudo chown -R 1001:1001 test" 2025-07-17T10:42:11.3549588Z docker exec -t "be5eba77e402a727f4714ddb7ab9e94c4cfe9ef82df6800750eddceb7a771559" sh -c "sudo chown -R 1001:1001 test" 2025-07-17T10:42:11.3561588Z shell: /usr/bin/bash -e {0} 2025-07-17T10:42:11.3561794Z env: 2025-07-17T10:42:11.3561946Z GIT_DEFAULT_BRANCH: main 2025-07-17T10:42:11.3562170Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T10:42:11.3562471Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T10:42:11.3562743Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T10:42:11.3563577Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T10:42:11.3564254Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T10:42:11.3564464Z AWS_REGION: us-east-1 2025-07-17T10:42:11.3564705Z AWS_ACCESS_KEY_ID: *** 2025-07-17T10:42:11.3564958Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T10:42:11.3568895Z AWS_SESSION_TOKEN: *** 2025-07-17T10:42:11.3569193Z CONTAINER_NAME: be5eba77e402a727f4714ddb7ab9e94c4cfe9ef82df6800750eddceb7a771559 2025-07-17T10:42:11.3569652Z ##[endgroup] 2025-07-17T10:42:11.5801270Z ##[group]Run cat test/**/*_toprint.log || true 2025-07-17T10:42:11.5801539Z cat test/**/*_toprint.log || true 2025-07-17T10:42:11.5813322Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T10:42:11.5813568Z env: 2025-07-17T10:42:11.5813735Z GIT_DEFAULT_BRANCH: main 2025-07-17T10:42:11.5813963Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T10:42:11.5814256Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T10:42:11.5814516Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T10:42:11.5815209Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T10:42:11.5815866Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T10:42:11.5816054Z AWS_REGION: us-east-1 2025-07-17T10:42:11.5816325Z AWS_ACCESS_KEY_ID: *** 2025-07-17T10:42:11.5816581Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T10:42:11.5820491Z AWS_SESSION_TOKEN: *** 2025-07-17T10:42:11.5820790Z CONTAINER_NAME: be5eba77e402a727f4714ddb7ab9e94c4cfe9ef82df6800750eddceb7a771559 2025-07-17T10:42:11.5821096Z ##[endgroup] 2025-07-17T10:42:11.5954168Z cat: 'test/**/*_toprint.log': No such file or directory 2025-07-17T10:42:11.6092938Z Prepare all required actions 2025-07-17T10:42:11.6093702Z Getting action download info 2025-07-17T10:42:13.8159042Z Download action repository 'seemethere/upload-artifact-s3@v5' (SHA:baba72d0712b404f646cebe0730933554ebce96a) 2025-07-17T10:42:20.7810937Z Download action repository 'actions/upload-artifact@v4' (SHA:ea165f8d65b6e75b540449e92b4886f43607fa02) 2025-07-17T10:42:21.5469904Z ##[group]Run ./.github/actions/upload-test-artifacts 2025-07-17T10:42:21.5470143Z with: 2025-07-17T10:42:21.5470292Z use-gha: true 2025-07-17T10:42:21.5470540Z file-suffix: test-default-1-6-linux.rocm.gpu.mi300.2_46160759521 2025-07-17T10:42:21.5470819Z s3-bucket: gha-artifacts 2025-07-17T10:42:21.5470985Z env: 2025-07-17T10:42:21.5471137Z GIT_DEFAULT_BRANCH: main 2025-07-17T10:42:21.5471354Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T10:42:21.5471643Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T10:42:21.5471951Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T10:42:21.5472644Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T10:42:21.5473488Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T10:42:21.5473683Z AWS_REGION: us-east-1 2025-07-17T10:42:21.5473907Z AWS_ACCESS_KEY_ID: *** 2025-07-17T10:42:21.5474160Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T10:42:21.5478074Z AWS_SESSION_TOKEN: *** 2025-07-17T10:42:21.5478376Z CONTAINER_NAME: be5eba77e402a727f4714ddb7ab9e94c4cfe9ef82df6800750eddceb7a771559 2025-07-17T10:42:21.5478688Z ##[endgroup] 2025-07-17T10:42:21.5566568Z ##[group]Run actions/upload-artifact@v4 2025-07-17T10:42:21.5566775Z with: 2025-07-17T10:42:21.5567066Z name: test-jsons-runattempt1-test-default-1-6-linux.rocm.gpu.mi300.2_46160759521.zip 2025-07-17T10:42:21.5567524Z retention-days: 14 2025-07-17T10:42:21.5567699Z if-no-files-found: warn 2025-07-17T10:42:21.5567880Z path: test/**/*.json 2025-07-17T10:42:21.5568052Z compression-level: 6 2025-07-17T10:42:21.5568205Z overwrite: false 2025-07-17T10:42:21.5568375Z include-hidden-files: false 2025-07-17T10:42:21.5568556Z env: 2025-07-17T10:42:21.5568702Z GIT_DEFAULT_BRANCH: main 2025-07-17T10:42:21.5568924Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T10:42:21.5569219Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T10:42:21.5569488Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T10:42:21.5570332Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T10:42:21.5570996Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T10:42:21.5571186Z AWS_REGION: us-east-1 2025-07-17T10:42:21.5571405Z AWS_ACCESS_KEY_ID: *** 2025-07-17T10:42:21.5571659Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T10:42:21.5575587Z AWS_SESSION_TOKEN: *** 2025-07-17T10:42:21.5575878Z CONTAINER_NAME: be5eba77e402a727f4714ddb7ab9e94c4cfe9ef82df6800750eddceb7a771559 2025-07-17T10:42:21.5576189Z ##[endgroup] 2025-07-17T10:42:22.1406838Z With the provided path, there will be 7 files uploaded 2025-07-17T10:42:22.1410967Z Artifact name is valid! 2025-07-17T10:42:22.1412126Z Root directory input is valid! 2025-07-17T10:42:22.5603850Z Beginning upload of artifact content to blob storage 2025-07-17T10:42:22.7575102Z Uploaded bytes 45952 2025-07-17T10:42:22.7953063Z Finished uploading artifact content to blob storage! 2025-07-17T10:42:22.7954935Z SHA256 digest of uploaded artifact zip is 55d965e632212ed44607721138068e3045ff7c0233f27cc2b8cde630e17a0fe8 2025-07-17T10:42:22.7956151Z Finalizing artifact upload 2025-07-17T10:42:22.8885524Z Artifact test-jsons-runattempt1-test-default-1-6-linux.rocm.gpu.mi300.2_46160759521.zip.zip successfully finalized. Artifact ID 3553577232 2025-07-17T10:42:22.8886334Z Artifact test-jsons-runattempt1-test-default-1-6-linux.rocm.gpu.mi300.2_46160759521.zip has been successfully uploaded! Final size is 45952 bytes. Artifact ID is 3553577232 2025-07-17T10:42:22.8891170Z Artifact download URL: https://github.com/pytorch/pytorch/actions/runs/16337959895/artifacts/3553577232 2025-07-17T10:42:22.9062529Z ##[group]Run actions/upload-artifact@v4 2025-07-17T10:42:22.9062766Z with: 2025-07-17T10:42:22.9063067Z name: test-reports-runattempt1-test-default-1-6-linux.rocm.gpu.mi300.2_46160759521.zip 2025-07-17T10:42:22.9063426Z retention-days: 14 2025-07-17T10:42:22.9063611Z if-no-files-found: ignore 2025-07-17T10:42:22.9063813Z path: test/**/*.xml test/**/*.csv 2025-07-17T10:42:22.9064022Z compression-level: 6 2025-07-17T10:42:22.9064218Z overwrite: false 2025-07-17T10:42:22.9064396Z include-hidden-files: false 2025-07-17T10:42:22.9064597Z env: 2025-07-17T10:42:22.9064767Z GIT_DEFAULT_BRANCH: main 2025-07-17T10:42:22.9064993Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T10:42:22.9065386Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T10:42:22.9065666Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T10:42:22.9066372Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T10:42:22.9067047Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T10:42:22.9067254Z AWS_REGION: us-east-1 2025-07-17T10:42:22.9067493Z AWS_ACCESS_KEY_ID: *** 2025-07-17T10:42:22.9067755Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T10:42:22.9071722Z AWS_SESSION_TOKEN: *** 2025-07-17T10:42:22.9072023Z CONTAINER_NAME: be5eba77e402a727f4714ddb7ab9e94c4cfe9ef82df6800750eddceb7a771559 2025-07-17T10:42:22.9072494Z ##[endgroup] 2025-07-17T10:42:23.5301588Z With the provided path, there will be 109 files uploaded 2025-07-17T10:42:23.5305105Z Artifact name is valid! 2025-07-17T10:42:23.5306532Z Root directory input is valid! 2025-07-17T10:42:27.7426318Z Beginning upload of artifact content to blob storage 2025-07-17T10:42:28.4255523Z Uploaded bytes 330508 2025-07-17T10:42:28.4651570Z Finished uploading artifact content to blob storage! 2025-07-17T10:42:28.4652483Z SHA256 digest of uploaded artifact zip is ca801b65e32f71c76cfb6a9d9a6bf2bf5077dec6fd444a9777241dea28a40e93 2025-07-17T10:42:28.4653706Z Finalizing artifact upload 2025-07-17T10:42:28.5736346Z Artifact test-reports-runattempt1-test-default-1-6-linux.rocm.gpu.mi300.2_46160759521.zip.zip successfully finalized. Artifact ID 3553577838 2025-07-17T10:42:28.5737960Z Artifact test-reports-runattempt1-test-default-1-6-linux.rocm.gpu.mi300.2_46160759521.zip has been successfully uploaded! Final size is 330508 bytes. Artifact ID is 3553577838 2025-07-17T10:42:28.5742048Z Artifact download URL: https://github.com/pytorch/pytorch/actions/runs/16337959895/artifacts/3553577838 2025-07-17T10:42:28.5918371Z ##[group]Run actions/upload-artifact@v4 2025-07-17T10:42:28.5918619Z with: 2025-07-17T10:42:28.5918902Z name: logs-runattempt1-test-default-1-6-linux.rocm.gpu.mi300.2_46160759521.zip 2025-07-17T10:42:28.5919229Z retention-days: 14 2025-07-17T10:42:28.5919419Z if-no-files-found: ignore 2025-07-17T10:42:28.5919633Z path: usage_log.txt test/**/*.log 2025-07-17T10:42:28.5919850Z compression-level: 6 2025-07-17T10:42:28.5920044Z overwrite: false 2025-07-17T10:42:28.5920246Z include-hidden-files: false 2025-07-17T10:42:28.5920457Z env: 2025-07-17T10:42:28.5920621Z GIT_DEFAULT_BRANCH: main 2025-07-17T10:42:28.5920860Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T10:42:28.5921169Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T10:42:28.5921462Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T10:42:28.5922484Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T10:42:28.5923168Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T10:42:28.5923370Z AWS_REGION: us-east-1 2025-07-17T10:42:28.5923621Z AWS_ACCESS_KEY_ID: *** 2025-07-17T10:42:28.5923886Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T10:42:28.5927845Z AWS_SESSION_TOKEN: *** 2025-07-17T10:42:28.5928147Z CONTAINER_NAME: be5eba77e402a727f4714ddb7ab9e94c4cfe9ef82df6800750eddceb7a771559 2025-07-17T10:42:28.5928464Z ##[endgroup] 2025-07-17T10:42:29.2066920Z Multiple search paths detected. Calculating the least common ancestor of all paths 2025-07-17T10:42:29.2068092Z The least common ancestor is /home/runner/_work/pytorch/pytorch. This will be the root directory of the artifact 2025-07-17T10:42:29.2068576Z With the provided path, there will be 94 files uploaded 2025-07-17T10:42:29.2072127Z Artifact name is valid! 2025-07-17T10:42:29.2073195Z Root directory input is valid! 2025-07-17T10:42:30.8144284Z Beginning upload of artifact content to blob storage 2025-07-17T10:42:31.1742859Z Uploaded bytes 431983 2025-07-17T10:42:31.2093782Z Finished uploading artifact content to blob storage! 2025-07-17T10:42:31.2094959Z SHA256 digest of uploaded artifact zip is decdcae23ce3a42d87ca6fa4b09fd64c16f2b1171d5dda8a8cd6522e6f2de2a1 2025-07-17T10:42:31.2096371Z Finalizing artifact upload 2025-07-17T10:42:31.2999933Z Artifact logs-runattempt1-test-default-1-6-linux.rocm.gpu.mi300.2_46160759521.zip.zip successfully finalized. Artifact ID 3553578131 2025-07-17T10:42:31.3001290Z Artifact logs-runattempt1-test-default-1-6-linux.rocm.gpu.mi300.2_46160759521.zip has been successfully uploaded! Final size is 431983 bytes. Artifact ID is 3553578131 2025-07-17T10:42:31.3007135Z Artifact download URL: https://github.com/pytorch/pytorch/actions/runs/16337959895/artifacts/3553578131 2025-07-17T10:42:31.3226988Z ##[group]Run # shellcheck disable=SC2156 2025-07-17T10:42:31.3227496Z # shellcheck disable=SC2156 2025-07-17T10:42:31.3227995Z find . -iname "core.[1-9]*" -exec docker exec "${CONTAINER_NAME}" sh -c "gdb python {} -ex 'bt' -ex 'q'" \; 2025-07-17T10:42:31.3240362Z shell: /usr/bin/bash -e {0} 2025-07-17T10:42:31.3240723Z env: 2025-07-17T10:42:31.3241050Z GIT_DEFAULT_BRANCH: main 2025-07-17T10:42:31.3241415Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T10:42:31.3241905Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T10:42:31.3242306Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T10:42:31.3243602Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T10:42:31.3244723Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T10:42:31.3245045Z AWS_REGION: us-east-1 2025-07-17T10:42:31.3245474Z AWS_ACCESS_KEY_ID: *** 2025-07-17T10:42:31.3245832Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T10:42:31.3249987Z AWS_SESSION_TOKEN: *** 2025-07-17T10:42:31.3250390Z CONTAINER_NAME: be5eba77e402a727f4714ddb7ab9e94c4cfe9ef82df6800750eddceb7a771559 2025-07-17T10:42:31.3250894Z ##[endgroup] 2025-07-17T10:42:32.2609604Z ##[group]Run actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 2025-07-17T10:42:32.2610084Z with: 2025-07-17T10:42:32.2610464Z name: coredumps-default-1-6-linux.rocm.gpu.mi300.2 2025-07-17T10:42:32.2610822Z retention-days: 14 2025-07-17T10:42:32.2611157Z if-no-files-found: ignore 2025-07-17T10:42:32.2611633Z path: ./**/core.[1-9]* 2025-07-17T10:42:32.2611943Z compression-level: 6 2025-07-17T10:42:32.2612220Z overwrite: false 2025-07-17T10:42:32.2612623Z include-hidden-files: false 2025-07-17T10:42:32.2612950Z env: 2025-07-17T10:42:32.2613246Z GIT_DEFAULT_BRANCH: main 2025-07-17T10:42:32.2613661Z RUNNER_ARTIFACT_DIR: /home/runner/_work/_temp/artifacts 2025-07-17T10:42:32.2614097Z RUNNER_TEST_RESULTS_DIR: /home/runner/_work/_temp/test-results 2025-07-17T10:42:32.2614492Z RUNNER_DOCS_DIR: /home/runner/_work/_temp/docs 2025-07-17T10:42:32.2615593Z GPU_FLAG: --device=/dev/mem --device=/dev/kfd --group-add 110 --device /dev/dri/renderD176 --device /dev/dri/renderD184 --group-add video --group-add 109 --group-add daemon --group-add bin --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --network=host 2025-07-17T10:42:32.2616420Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T10:42:32.2616786Z AWS_REGION: us-east-1 2025-07-17T10:42:32.2617215Z AWS_ACCESS_KEY_ID: *** 2025-07-17T10:42:32.2633636Z AWS_SECRET_ACCESS_KEY: *** 2025-07-17T10:42:32.2637728Z AWS_SESSION_TOKEN: *** 2025-07-17T10:42:32.2638065Z CONTAINER_NAME: be5eba77e402a727f4714ddb7ab9e94c4cfe9ef82df6800750eddceb7a771559 2025-07-17T10:42:32.2638418Z ##[endgroup] 2025-07-17T10:42:38.1970595Z No files were found with the provided path: ./**/core.[1-9]*. No artifacts will be uploaded. 2025-07-17T10:42:38.2262039Z Post job cleanup. 2025-07-17T10:42:38.2574385Z Logging out of registry 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-07-17T10:42:38.2899124Z Post job cleanup. 2025-07-17T10:42:38.3911395Z Post job cleanup. 2025-07-17T10:42:38.3953451Z Post job cleanup. 2025-07-17T10:42:38.4713720Z [command]/usr/bin/git version 2025-07-17T10:42:38.4750115Z git version 2.50.1 2025-07-17T10:42:38.4779245Z Copying '/home/runner/.gitconfig' to '/home/runner/_work/_temp/fd62b105-da39-4fde-b503-64c5a2b22b8e/.gitconfig' 2025-07-17T10:42:38.4788246Z Temporarily overriding HOME='/home/runner/_work/_temp/fd62b105-da39-4fde-b503-64c5a2b22b8e' before making global git config changes 2025-07-17T10:42:38.4788815Z Adding repository directory to the temporary git global config as a safe directory 2025-07-17T10:42:38.4792233Z [command]/usr/bin/git config --global --add safe.directory /home/runner/_work/pytorch/pytorch 2025-07-17T10:42:38.4831220Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2025-07-17T10:42:38.4860459Z [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' || :" 2025-07-17T10:42:38.5150233Z Entering 'android/libs/fbjni' 2025-07-17T10:42:38.5203610Z Entering 'third_party/FP16' 2025-07-17T10:42:38.5258827Z Entering 'third_party/FXdiv' 2025-07-17T10:42:38.5307589Z Entering 'third_party/NNPACK' 2025-07-17T10:42:38.5360054Z Entering 'third_party/NVTX' 2025-07-17T10:42:38.5410838Z Entering 'third_party/VulkanMemoryAllocator' 2025-07-17T10:42:38.5462333Z Entering 'third_party/XNNPACK' 2025-07-17T10:42:38.5525159Z Entering 'third_party/aiter' 2025-07-17T10:42:38.5576027Z Entering 'third_party/aiter/3rdparty/composable_kernel' 2025-07-17T10:42:38.5640175Z Entering 'third_party/benchmark' 2025-07-17T10:42:38.5690829Z Entering 'third_party/composable_kernel' 2025-07-17T10:42:38.5748765Z Entering 'third_party/cpp-httplib' 2025-07-17T10:42:38.5800022Z Entering 'third_party/cpuinfo' 2025-07-17T10:42:38.5848777Z Entering 'third_party/cudnn_frontend' 2025-07-17T10:42:38.5898570Z Entering 'third_party/cutlass' 2025-07-17T10:42:38.5960767Z Entering 'third_party/fbgemm' 2025-07-17T10:42:38.6013267Z Entering 'third_party/fbgemm/external/asmjit' 2025-07-17T10:42:38.6061022Z Entering 'third_party/fbgemm/external/composable_kernel' 2025-07-17T10:42:38.6112091Z Entering 'third_party/fbgemm/external/cpuinfo' 2025-07-17T10:42:38.6160382Z Entering 'third_party/fbgemm/external/cutlass' 2025-07-17T10:42:38.6221604Z Entering 'third_party/fbgemm/external/googletest' 2025-07-17T10:42:38.6270097Z Entering 'third_party/fbgemm/external/hipify_torch' 2025-07-17T10:42:38.6314872Z Entering 'third_party/fbgemm/external/json' 2025-07-17T10:42:38.6366545Z Entering 'third_party/flash-attention' 2025-07-17T10:42:38.6416966Z Entering 'third_party/flash-attention/csrc/composable_kernel' 2025-07-17T10:42:38.6476385Z Entering 'third_party/flash-attention/csrc/cutlass' 2025-07-17T10:42:38.6532270Z Entering 'third_party/flatbuffers' 2025-07-17T10:42:38.6587335Z Entering 'third_party/fmt' 2025-07-17T10:42:38.6639833Z Entering 'third_party/gemmlowp/gemmlowp' 2025-07-17T10:42:38.6690724Z Entering 'third_party/gloo' 2025-07-17T10:42:38.6741453Z Entering 'third_party/googletest' 2025-07-17T10:42:38.6794717Z Entering 'third_party/ideep' 2025-07-17T10:42:38.6843838Z Entering 'third_party/ideep/mkl-dnn' 2025-07-17T10:42:38.6902014Z Entering 'third_party/ittapi' 2025-07-17T10:42:38.6951680Z Entering 'third_party/kineto' 2025-07-17T10:42:38.7003544Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2025-07-17T10:42:38.7052827Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-07-17T10:42:38.7102897Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-07-17T10:42:38.7153756Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-07-17T10:42:38.7201714Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-07-17T10:42:38.7247792Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-07-17T10:42:38.7302364Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-07-17T10:42:38.7352039Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-07-17T10:42:38.7404674Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-07-17T10:42:38.7456244Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-07-17T10:42:38.7509691Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-07-17T10:42:38.7559895Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-07-17T10:42:38.7614558Z Entering 'third_party/kleidiai' 2025-07-17T10:42:38.7668912Z Entering 'third_party/mimalloc' 2025-07-17T10:42:38.7721935Z Entering 'third_party/nlohmann' 2025-07-17T10:42:38.7774159Z Entering 'third_party/onnx' 2025-07-17T10:42:38.7841192Z Entering 'third_party/onnx/third_party/pybind11' 2025-07-17T10:42:38.7896867Z Entering 'third_party/opentelemetry-cpp' 2025-07-17T10:42:38.7950111Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-07-17T10:42:38.7999820Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-07-17T10:42:38.8046413Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-07-17T10:42:38.8097217Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-07-17T10:42:38.8146438Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-07-17T10:42:38.8194233Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-07-17T10:42:38.8245097Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-07-17T10:42:38.8293091Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-07-17T10:42:38.8343577Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-07-17T10:42:38.8396062Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-07-17T10:42:38.8464112Z Entering 'third_party/pocketfft' 2025-07-17T10:42:38.8519353Z Entering 'third_party/protobuf' 2025-07-17T10:42:38.8576181Z Entering 'third_party/protobuf/third_party/benchmark' 2025-07-17T10:42:38.8625929Z Entering 'third_party/protobuf/third_party/googletest' 2025-07-17T10:42:38.8679627Z Entering 'third_party/psimd' 2025-07-17T10:42:38.8733118Z Entering 'third_party/pthreadpool' 2025-07-17T10:42:38.8783150Z Entering 'third_party/pybind11' 2025-07-17T10:42:38.8834391Z Entering 'third_party/python-peachpy' 2025-07-17T10:42:38.8883694Z Entering 'third_party/sleef' 2025-07-17T10:42:38.8936374Z Entering 'third_party/tensorpipe' 2025-07-17T10:42:38.8988197Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-07-17T10:42:38.9036339Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-07-17T10:42:38.9081259Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-07-17T10:42:38.9127056Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-07-17T10:42:38.9175433Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-07-17T10:42:38.9251699Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2025-07-17T10:42:38.9278510Z http.https://github.com/.extraheader 2025-07-17T10:42:38.9288084Z [command]/usr/bin/git config --local --unset-all http.https://github.com/.extraheader 2025-07-17T10:42:38.9323538Z [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' || :" 2025-07-17T10:42:38.9610398Z Entering 'android/libs/fbjni' 2025-07-17T10:42:38.9638007Z http.https://github.com/.extraheader 2025-07-17T10:42:38.9683612Z Entering 'third_party/FP16' 2025-07-17T10:42:38.9711128Z http.https://github.com/.extraheader 2025-07-17T10:42:38.9754849Z Entering 'third_party/FXdiv' 2025-07-17T10:42:38.9783179Z http.https://github.com/.extraheader 2025-07-17T10:42:38.9824713Z Entering 'third_party/NNPACK' 2025-07-17T10:42:38.9852497Z http.https://github.com/.extraheader 2025-07-17T10:42:38.9891509Z Entering 'third_party/NVTX' 2025-07-17T10:42:38.9918721Z http.https://github.com/.extraheader 2025-07-17T10:42:38.9960469Z Entering 'third_party/VulkanMemoryAllocator' 2025-07-17T10:42:38.9988549Z http.https://github.com/.extraheader 2025-07-17T10:42:39.0028267Z Entering 'third_party/XNNPACK' 2025-07-17T10:42:39.0055040Z http.https://github.com/.extraheader 2025-07-17T10:42:39.0221100Z Entering 'third_party/aiter' 2025-07-17T10:42:39.0221914Z http.https://github.com/.extraheader 2025-07-17T10:42:39.0258769Z Entering 'third_party/aiter/3rdparty/composable_kernel' 2025-07-17T10:42:39.0347791Z http.https://github.com/.extraheader 2025-07-17T10:42:39.0348181Z Entering 'third_party/benchmark' 2025-07-17T10:42:39.0348962Z http.https://github.com/.extraheader 2025-07-17T10:42:39.0447127Z Entering 'third_party/composable_kernel' 2025-07-17T10:42:39.0517598Z http.https://github.com/.extraheader 2025-07-17T10:42:39.0594197Z Entering 'third_party/cpp-httplib' 2025-07-17T10:42:39.0634082Z http.https://github.com/.extraheader 2025-07-17T10:42:39.0683472Z Entering 'third_party/cpuinfo' 2025-07-17T10:42:39.0713489Z http.https://github.com/.extraheader 2025-07-17T10:42:39.0757167Z Entering 'third_party/cudnn_frontend' 2025-07-17T10:42:39.0784216Z http.https://github.com/.extraheader 2025-07-17T10:42:39.0828445Z Entering 'third_party/cutlass' 2025-07-17T10:42:39.0858317Z http.https://github.com/.extraheader 2025-07-17T10:42:39.0911128Z Entering 'third_party/fbgemm' 2025-07-17T10:42:39.0938862Z http.https://github.com/.extraheader 2025-07-17T10:42:39.0980994Z Entering 'third_party/fbgemm/external/asmjit' 2025-07-17T10:42:39.1009382Z http.https://github.com/.extraheader 2025-07-17T10:42:39.1045810Z Entering 'third_party/fbgemm/external/composable_kernel' 2025-07-17T10:42:39.1070423Z http.https://github.com/.extraheader 2025-07-17T10:42:39.1112433Z Entering 'third_party/fbgemm/external/cpuinfo' 2025-07-17T10:42:39.1148507Z http.https://github.com/.extraheader 2025-07-17T10:42:39.1173582Z Entering 'third_party/fbgemm/external/cutlass' 2025-07-17T10:42:39.1197799Z http.https://github.com/.extraheader 2025-07-17T10:42:39.1248872Z Entering 'third_party/fbgemm/external/googletest' 2025-07-17T10:42:39.1275306Z http.https://github.com/.extraheader 2025-07-17T10:42:39.1311646Z Entering 'third_party/fbgemm/external/hipify_torch' 2025-07-17T10:42:39.1336236Z http.https://github.com/.extraheader 2025-07-17T10:42:39.1377201Z Entering 'third_party/fbgemm/external/json' 2025-07-17T10:42:39.1401856Z http.https://github.com/.extraheader 2025-07-17T10:42:39.1446761Z Entering 'third_party/flash-attention' 2025-07-17T10:42:39.1475929Z http.https://github.com/.extraheader 2025-07-17T10:42:39.1515936Z Entering 'third_party/flash-attention/csrc/composable_kernel' 2025-07-17T10:42:39.1541389Z http.https://github.com/.extraheader 2025-07-17T10:42:39.1594992Z Entering 'third_party/flash-attention/csrc/cutlass' 2025-07-17T10:42:39.1621511Z http.https://github.com/.extraheader 2025-07-17T10:42:39.1674017Z Entering 'third_party/flatbuffers' 2025-07-17T10:42:39.1700721Z http.https://github.com/.extraheader 2025-07-17T10:42:39.1745381Z Entering 'third_party/fmt' 2025-07-17T10:42:39.1774856Z http.https://github.com/.extraheader 2025-07-17T10:42:39.1818271Z Entering 'third_party/gemmlowp/gemmlowp' 2025-07-17T10:42:39.1851205Z http.https://github.com/.extraheader 2025-07-17T10:42:39.1894375Z Entering 'third_party/gloo' 2025-07-17T10:42:39.1923509Z http.https://github.com/.extraheader 2025-07-17T10:42:39.1968093Z Entering 'third_party/googletest' 2025-07-17T10:42:39.2003264Z http.https://github.com/.extraheader 2025-07-17T10:42:39.2045822Z Entering 'third_party/ideep' 2025-07-17T10:42:39.2074953Z http.https://github.com/.extraheader 2025-07-17T10:42:39.2116559Z Entering 'third_party/ideep/mkl-dnn' 2025-07-17T10:42:39.2144505Z http.https://github.com/.extraheader 2025-07-17T10:42:39.2198891Z Entering 'third_party/ittapi' 2025-07-17T10:42:39.2229857Z http.https://github.com/.extraheader 2025-07-17T10:42:39.2274415Z Entering 'third_party/kineto' 2025-07-17T10:42:39.2305527Z http.https://github.com/.extraheader 2025-07-17T10:42:39.2348856Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2025-07-17T10:42:39.2377653Z http.https://github.com/.extraheader 2025-07-17T10:42:39.2424187Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-07-17T10:42:39.2454028Z http.https://github.com/.extraheader 2025-07-17T10:42:39.2498484Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-07-17T10:42:39.2526990Z http.https://github.com/.extraheader 2025-07-17T10:42:39.2573265Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-07-17T10:42:39.2602484Z http.https://github.com/.extraheader 2025-07-17T10:42:39.2649796Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-07-17T10:42:39.2680004Z http.https://github.com/.extraheader 2025-07-17T10:42:39.2726451Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-07-17T10:42:39.2757111Z http.https://github.com/.extraheader 2025-07-17T10:42:39.2807888Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-07-17T10:42:39.2837940Z http.https://github.com/.extraheader 2025-07-17T10:42:39.2883274Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-07-17T10:42:39.2913710Z http.https://github.com/.extraheader 2025-07-17T10:42:39.2959684Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-07-17T10:42:39.2988932Z http.https://github.com/.extraheader 2025-07-17T10:42:39.3035167Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-07-17T10:42:39.3065127Z http.https://github.com/.extraheader 2025-07-17T10:42:39.3115328Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-07-17T10:42:39.3144661Z http.https://github.com/.extraheader 2025-07-17T10:42:39.3188775Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-07-17T10:42:39.3218270Z http.https://github.com/.extraheader 2025-07-17T10:42:39.3267883Z Entering 'third_party/kleidiai' 2025-07-17T10:42:39.3298424Z http.https://github.com/.extraheader 2025-07-17T10:42:39.3341112Z Entering 'third_party/mimalloc' 2025-07-17T10:42:39.3367817Z http.https://github.com/.extraheader 2025-07-17T10:42:39.3410257Z Entering 'third_party/nlohmann' 2025-07-17T10:42:39.3443558Z http.https://github.com/.extraheader 2025-07-17T10:42:39.3485485Z Entering 'third_party/onnx' 2025-07-17T10:42:39.3516233Z http.https://github.com/.extraheader 2025-07-17T10:42:39.3574811Z Entering 'third_party/onnx/third_party/pybind11' 2025-07-17T10:42:39.3606858Z http.https://github.com/.extraheader 2025-07-17T10:42:39.3656844Z Entering 'third_party/opentelemetry-cpp' 2025-07-17T10:42:39.3688570Z http.https://github.com/.extraheader 2025-07-17T10:42:39.3734586Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-07-17T10:42:39.3764540Z http.https://github.com/.extraheader 2025-07-17T10:42:39.3809174Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-07-17T10:42:39.3840677Z http.https://github.com/.extraheader 2025-07-17T10:42:39.3885160Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-07-17T10:42:39.3915565Z http.https://github.com/.extraheader 2025-07-17T10:42:39.3959810Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-07-17T10:42:39.3987792Z http.https://github.com/.extraheader 2025-07-17T10:42:39.4032487Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-07-17T10:42:39.4058638Z http.https://github.com/.extraheader 2025-07-17T10:42:39.4100303Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-07-17T10:42:39.4127929Z http.https://github.com/.extraheader 2025-07-17T10:42:39.4169800Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-07-17T10:42:39.4197362Z http.https://github.com/.extraheader 2025-07-17T10:42:39.4238077Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-07-17T10:42:39.4268623Z http.https://github.com/.extraheader 2025-07-17T10:42:39.4318305Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-07-17T10:42:39.4348799Z http.https://github.com/.extraheader 2025-07-17T10:42:39.4396850Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-07-17T10:42:39.4425615Z http.https://github.com/.extraheader 2025-07-17T10:42:39.4492238Z Entering 'third_party/pocketfft' 2025-07-17T10:42:39.4522620Z http.https://github.com/.extraheader 2025-07-17T10:42:39.4566717Z Entering 'third_party/protobuf' 2025-07-17T10:42:39.4597744Z http.https://github.com/.extraheader 2025-07-17T10:42:39.4645116Z Entering 'third_party/protobuf/third_party/benchmark' 2025-07-17T10:42:39.4674929Z http.https://github.com/.extraheader 2025-07-17T10:42:39.4720910Z Entering 'third_party/protobuf/third_party/googletest' 2025-07-17T10:42:39.4751221Z http.https://github.com/.extraheader 2025-07-17T10:42:39.4800470Z Entering 'third_party/psimd' 2025-07-17T10:42:39.4829982Z http.https://github.com/.extraheader 2025-07-17T10:42:39.4875749Z Entering 'third_party/pthreadpool' 2025-07-17T10:42:39.4905674Z http.https://github.com/.extraheader 2025-07-17T10:42:39.4950121Z Entering 'third_party/pybind11' 2025-07-17T10:42:39.4978857Z http.https://github.com/.extraheader 2025-07-17T10:42:39.5021875Z Entering 'third_party/python-peachpy' 2025-07-17T10:42:39.5052604Z http.https://github.com/.extraheader 2025-07-17T10:42:39.5094735Z Entering 'third_party/sleef' 2025-07-17T10:42:39.5127493Z http.https://github.com/.extraheader 2025-07-17T10:42:39.5168233Z Entering 'third_party/tensorpipe' 2025-07-17T10:42:39.5200584Z http.https://github.com/.extraheader 2025-07-17T10:42:39.5244714Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-07-17T10:42:39.5273533Z http.https://github.com/.extraheader 2025-07-17T10:42:39.5321153Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-07-17T10:42:39.5350668Z http.https://github.com/.extraheader 2025-07-17T10:42:39.5394354Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-07-17T10:42:39.5422436Z http.https://github.com/.extraheader 2025-07-17T10:42:39.5467071Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-07-17T10:42:39.5495159Z http.https://github.com/.extraheader 2025-07-17T10:42:39.5537773Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-07-17T10:42:39.5568477Z http.https://github.com/.extraheader 2025-07-17T10:42:39.5755289Z Cleaning up orphan processes